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I accept nothing less than Chepodium (Chewin? 👀👀) & Maxpoints (podium is possible but I'm not jinxing Dutch Catdad 👍🏽) tomorrow
#sergio perez#max verstappen#f1#formula 1#sports#saudi arabian grand prix#saudi arabia#jeddah#Q3#that engine better not shit itself#red bull racing
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Breaking Barriers Along The Path To Purchase
By Matthew Tilley, MaxPoint
The future of retail is becoming increasingly hard to predict. It is clear that online, mobile and offline channels are converging, but aside from a few first movers, who, how and when convergence will happen to any particular retailer remains a mystery.
As retail competitors and consumer expectations escalate, one question remains essential: “How can I make it as easy as possible for people to buy from me?”
The answer lies at the heart of retail merchandising, regardless of channels – but finding it hasn’t been easy. Today’s retailers have more information and tools than ever to understand their customers, but more data hasn’t made merchandising simpler. This is due primarily to the fact that many retailers still keep their online and offline data siloed. This practice no longer makes sense. Shoppers have blurred the lines between channels, so their path-to-purchase data should also converge.
Of course, retail data convergence isn’t automatic or easy. But it does present exciting opportunities for breaking down barriers to understanding the path to purchase:
Barriers to in-store purchases
A key challenge for brick-and-mortar retailers is that they simply can’t track their customer’s behaviors at the level of their e-Commerce counterparts. E-Commerce marketers have deep insight into visitor experiences: from real-time pathing through products to understanding what converts and what gets abandoned.
Imagine the power of having the same data about in-store behaviors. For example, if a retailer could highlight areas in the store with high traffic and low conversion, they might add more associates to boost sales. Understanding the volume and speed to purchase of low consideration items might prompt faster checkout strategies to avoid frustrating customers who just won’t wait.
Brick-and-mortar retailers can have these insights, if they acquire the ability to sense and respond to in-store visitors with the same depth as they have with their web site visitors.
Barriers between channels
Most consumers see a retailer as a single entity, regardless of which buying channel they use. With this one-brand view comes an expectation of a seamless, great shopping experience, whether they buy online, pick up in-store or walk the aisles. For example, when a trusted retailer sends a coupon via email or to a mobile device, the customer expects to be able to use it anywhere, in particular at their neighborhood store. The customer doesn’t care about online vs. in-store promotion – and why should they?
If customers aren’t differentiating between dotcom and brick-and-mortar, neither should retailers. In fact, such distinctions only cause friction. Converging data across channels enables more seamless marketing to improve the customer experience.
Barriers in messaging
It’s no secret that the more a retailer knows and acts on deep knowledge about their customers, the more likely the customer is to be delighted and to keep coming back.
This fact drives smart retailers to gather and analyze data on their shoppers to create marketing profiles and personas. But keeping that data separated between channels only gives a partial view of the customer. In the converged world, there must also be a converged view of the customer across channels.
Matching data about what a customer does online to what they reveal about themselves in the store presents insights into their trip mission and the mindset they bring to each purchase. These insights can inform strategies and messaging, such as where to position in-store pickup or what out-of-store marketing to deliver. A full view of each customer puts the retailer in position to make the right moves towards a seamless and enjoyable buyer’s journey.
Getting to this barrier-free future is only possible with data — and not just more data, but expertly converged online and offline insights that reveal customer behavior and sales performance across channels. Of course, the challenge and effort of ingesting, mapping and assessing these vast data sets is significant. But so is the payoff: the ability to deliver amazing experiences that make it as easy as possible for customers to buy and keep coming back.
As Senior Director of Marketing, Matthew Tilley leads customer and partner marketing at MaxPoint, a marketing technology company that generates hyperlocal intelligence to optimize brand and retail performance. Before joining MaxPoint, he led the marketing of promotion settlement and consulting services at Inmar, one of the leading digital and paper coupon process in the U.S. There, he was recognized as one of the coupon industry's leading voices on consumer promotion response. He also spent time working in the agency world serving clients in a variety of industries, including banking, apparel and healthcare. Tilley graduated from Bob Jones University with a B.A. in Public Relations Journalism and from Wake Forest University with a M.B.A.
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#marshmello #snapchat #ihs #maxpoint #lasvegas #encore #surrender #surrendernightclub #vip #greygoose #jackdaniels #goodtimes #businessmeeting #goodnight #edm #edc #techno (at Surrender Nightclub)
#vip#jackdaniels#ihs#businessmeeting#surrendernightclub#encore#surrender#lasvegas#goodtimes#techno#maxpoint#goodnight#edc#marshmello#snapchat#edm#greygoose
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Those Darn Bots & How to Protect Against Them
Every new site wants to get their piece of Google, Yahoo, & Alexa. Yes I said it, Alexa ;) There is a bit of a risk when allowing these bots to traverse your endless universe of pages, posts, and other niceties that you want to get out there an noticed. Keep in mind, these 3 Search Engines arent the only ones looking for your site and content. There are millions of bots out there just sniffin around, and while most are harmless "Let's get your site listed in my directory" variety, there are some that look to do harm. Trust me when I tell you, you don't want them finding out you've left a massive back-door open to your site's shopping cart. Here is how to protect your site against these malicious bots. NOTE: As of October 1st, 2015 this will block the baddest of the bads. I will attempt to update on a monthly basis. Apache Bad-Bot Blocker Create a .htaccess file in the root of your site, if one does not already exist, and add the following to it: Order Allow,Deny Allow from all # AWS MALICIOUS SEARCH BOT Deny from 54.163.251.86 # CGI-BIN BASHER Deny from 62.210.75.170 # TROLL BLOCKS ANY COUNTRY Deny from 69.81.178.135 # FOREIGN BOTS CAUGHT IN THE BAD BOT BLACKHOLE Deny from 86.123.243.250 Deny from 109.233.122.212 Deny from 91.242.33.8 Deny from 109.64.163.94 Deny from 176.28.46.163 Deny from 78.110.8.85 Deny from 128.199.164.76 Deny from 41.66.230.92 Deny from 197.237.209.204 Deny from 95.211.81.86 Deny from 89.123.14.154 Deny from 185.24.233.3 Deny from 92.85.56.141 Deny from 192.186.142.170 Deny from 176.205.252.58 Deny from 178.88.168.243 Deny from 92.85.174.229 Deny from 5.9.151.67 Deny from 82.76.87.136 Deny from 79.177.15.78 Deny from 82.157.210.118 Deny from 79.182.153.82 Deny from 72.252.203.195 Deny from 85.53.243.220 Deny from 77.173.119.236 Deny from 92.11.29.100 Deny from 188.227.187.61 Deny from 130.204.244.14 Deny from 85.113.55.152 Deny from 80.61.57.89 Deny from 176.57.141.193 Deny from 77.172.210.16 Deny from 130.204.232.210 Deny from 192.99.244.194 Deny from 173.246.4.215 Deny from 200.59.199.67 Deny from 76.79.114.2 Deny from 197.45.133.157 Deny from 93.36.118.3 # ANONYMOUS PROXIES Deny from 94.242.246.23 Deny from 197.231.221.211 Deny from 217.115.10.131 # ATTEMPTED WP LOGINS FROM FOREIGN COUNTRIES Deny from 178.137.82.82 Deny from 46.151. Deny from 192.99.111.130 Deny from 178.33.164.31 # FOREIGN BOTS ATTEMPTING XSS SCRIPT ATTACKS Deny from 212.250.16.17 Deny from 103.12.151.188 # SEARCH ENGINES AND SPIDERS # Aboundex crawler Deny from 173.192.34.95 # Baidu crawlers Deny from 123.125. # Bing crawlers that do not respect crawl rate Deny from 157.55.39. Deny from 207.46.13. Deny from 107.178.200. Deny from 157.55.39.113 Deny from 207.46.13.67 # ddos attack hitting image folder Deny from 176.9.124.142 Deny from 75.68.97.63 Deny from 37.14.152.158 Deny from 207.98.189.119 Deny from 78.189.167.52 Deny from 76.116.161.177 Deny from 79.148.112.147 Deny from 173.163.130.213 Deny from 73.18.204.154 Deny from 24.29.71.24 Deny from 72.172.51.166 Deny from 68.38.179.102 Deny from 174.48.165.222 Deny from 67.211.239.30 Deny from 108.16.114.14 Deny from 184.35.14.164 Deny from 66.249.81.254 Deny from 79.14.209.118 Deny from 216.76.54.130 Deny from 72.185.4.223 Deny from 217.129.146.208 Deny from 148.251.13.242 # DotBot Deny from 208.115.113.92 # Gloengine Deny from 142.136.187.187 #Icerocket crawler Deny from 209.191. # Majestic12 bots Germany Deny from 148.251. Deny from 92.232. Deny from 78.46. Deny from 88.150. Deny from 144.76. Deny from 69.64.41. Deny from 87.117. Deny from 162.210. Deny from 37.59.20. Deny from 209.126. Deny from 198.245. Deny from 5.9.97. Deny from 91.194. #Maxpoint crawler Deny from 208.123.79.68 #Netherlands spam bot Deny from 80.82.65.82 # Seznam bot Deny from 77.75.77. # Spanish spam bot Deny from 85.54. Deny from 46.27.98.231 # Spinnr bot Deny from 173.192.238.44 Deny from 174.36.241.151 # Symfony spider Deny from 54.217.185.241 # XoviBot Germany Deny from 88.198. Deny from 212.224.119. #Yandex crawlers Deny from 100.43. Deny from 95.108. Deny from 141.8. Deny from 5.255. Deny from 178.154. Deny from 37.140. Deny from 93.158. Deny from 5.45.254. # Yisou spider Deny from 42.156. # COUNTRY BOTS # Australia bot Deny from 27.54.93.178 # Brazil bots Deny from 54.232.102.193 Deny from 186.202.126.94 Deny from 186.213.72.146 Deny from 186.219.44.6 # admin pages crawler from Brazil Deny from 177.52.160.37 # admin pages crawler from France Deny from 46.105.42.172 # admin pages crawler from China Deny from 120.34.97.183 # foreign wp-configbak crawlers Deny from 88.232.63.48 # Canada bot Deny from 69.161.138.1 # Chinese bots Deny from 192.200.102.102 Deny from 183.138. Deny from 98.126.17.119 Deny from 202.46.52.120 Deny from 42.156. Deny from 27.159. Deny from 27.150. Deny from 27.153. Deny from 140.237. Deny from 59.58. Deny from 182.118. Deny from 49.77. Deny from 149.255. Deny from 108.171. Deny from 120.43. Deny from 222.77. Deny from 220.161. Deny from 120.37. Deny from 110.86. Deny from 110.85. Deny from 221.176. Deny from 54.83. Deny from 195.211. Deny from 113.108.21.69 Deny from 223.104.31.111 Deny from 58.22.67.216 Deny from 183.131.144.204 Deny from 188.165.61.65 # French bots Deny from 46.105.113.8 Deny from 37.59.65.58 Deny from 37.187.89.77 # German bots Deny from 212.90.148.101 Deny from 178.63. Deny from 178.77. Deny from 178.238. Deny from 87.106.187.232 # India bot Deny from 122.166.169.127 # Ireland bot Deny from 54.229.73.40 # Korea bots Deny from 14.63.212.77 Deny from 118.39.113.219 Deny from 211.244. # Lithuania bots Deny from 31.193.196.98 # New Zealand bots Deny from 203.109.158.201 Deny from 27.252.92.103 # Pakistan bot Deny from 27.255.56.87 # Polish bot Deny from 82.139.3.3 Deny from 155.133.19.130 # Russian bots Deny from 46.35.238.234 Deny from 31.184.238.9 Deny from 91.207. Deny from 128.73.60.194 Deny from 94.41.113.152 Deny from 5.164. Deny from 95.105.29.47 Deny from 178.141.211.111 Deny from 46.42.152.84 Deny from 83.234.80.197 Deny from 87.117. Deny from 136.169.197.87 Deny from 188.143.232.72 Deny from 145.255.3.1 Deny from 31.192. # mail.ru Russian crawler Deny from 217.69.133. # google sniper ip from Serbia Deny from 95.180.77.18 # Turkey bots Deny from 88.233.201.38 Deny from 94.73.167.227 # UK bots Deny from 89.238.137.59 Deny from 195.157.124.186 # Ukraine bot Deny from 91.207.7.202 Deny from 46.174.165.192 # Ukraine spam bot Deny from 91.200. # Singapore bot Deny from 27.96.103.38 # Sweden bot Deny from 194.9.94.213 RewriteCond %{QUERY_STRING} http\:\/\/www\.google\.com\/humans\.txt\? RewriteRule .* - # Start Bad Bot Prevention # SetEnvIfNoCase User-Agent ^$ bad_bot SetEnvIfNoCase User-Agent ^12soso bad_bot SetEnvIfNoCase User-Agent ^192.comAgent bad_bot SetEnvIfNoCase User-Agent ^1Noonbot bad_bot SetEnvIfNoCase User-Agent ^1on1searchBot bad_bot SetEnvIfNoCase User-Agent ^3D_SEARCH bad_bot SetEnvIfNoCase User-Agent ^3DE_SEARCH2 bad_bot SetEnvIfNoCase User-Agent ^3GSE bad_bot SetEnvIfNoCase User-Agent ^50.nu bad_bot SetEnvIfNoCase User-Agent ^A1 Sitemap Generator bad_bot SetEnvIfNoCase User-Agent ^A1 Website Download bad_bot SetEnvIfNoCase User-Agent ^A6-Indexer bad_bot SetEnvIfNoCase User-Agent ^AASP bad_bot SetEnvIfNoCase User-Agent ^ABACHOBot bad_bot SetEnvIfNoCase User-Agent ^Abonti bad_bot SetEnvIfNoCase User-Agent ^abot bad_bot SetEnvIfNoCase User-Agent ^AbotEmailSearch bad_bot SetEnvIfNoCase User-Agent ^Aboundex/0.3 bad_bot SetEnvIfNoCase User-Agent ^AboutUsBot bad_bot SetEnvIfNoCase User-Agent ^AccMonitor Compliance Server bad_bot SetEnvIfNoCase User-Agent ^accoona bad_bot SetEnvIfNoCase User-Agent ^AChulkov.NET page walker bad_bot SetEnvIfNoCase User-Agent ^Acme.Spider bad_bot SetEnvIfNoCase User-Agent ^AcoonBot bad_bot SetEnvIfNoCase User-Agent ^acquia-crawler bad_bot SetEnvIfNoCase User-Agent ^ActiveTouristBot bad_bot SetEnvIfNoCase User-Agent ^Ad Muncher bad_bot SetEnvIfNoCase User-Agent ^AdamM Bot bad_bot SetEnvIfNoCase User-Agent ^adbeat_bot bad_bot SetEnvIfNoCase User-Agent ^adminshop.com bad_bot SetEnvIfNoCase User-Agent ^Advanced Email Extractor bad_bot SetEnvIfNoCase User-Agent ^AESOP_com_SpiderMan bad_bot SetEnvIfNoCase User-Agent ^AESpider bad_bot SetEnvIfNoCase User-Agent ^AF Knowledge Now Verity Spider bad_bot SetEnvIfNoCase User-Agent ^aggregator:Vocus bad_bot SetEnvIfNoCase User-Agent ^ah-ha.com crawler bad_bot SetEnvIfNoCase User-Agent ^AhrefsBot bad_bot SetEnvIfNoCase User-Agent ^AIBOT bad_bot SetEnvIfNoCase User-Agent ^aiHitBot bad_bot SetEnvIfNoCase User-Agent ^aipbot bad_bot SetEnvIfNoCase User-Agent ^AISIID bad_bot SetEnvIfNoCase User-Agent ^AITCSRobot/1.1 bad_bot SetEnvIfNoCase User-Agent ^Akamai-SiteSnapshot bad_bot SetEnvIfNoCase User-Agent ^AlexaWebSearchPlatform bad_bot SetEnvIfNoCase User-Agent ^AlexfDownload bad_bot SetEnvIfNoCase User-Agent ^Alexibot bad_bot SetEnvIfNoCase User-Agent ^AlkalineBOT bad_bot SetEnvIfNoCase User-Agent ^All Acronyms Bot bad_bot SetEnvIfNoCase User-Agent ^Alpha Search Agent bad_bot SetEnvIfNoCase User-Agent ^Amerla Search Bot bad_bot SetEnvIfNoCase User-Agent ^Amfibibot bad_bot SetEnvIfNoCase User-Agent ^AmPmPPC.com bad_bot SetEnvIfNoCase User-Agent ^AmPmPPC.com bad_bot SetEnvIfNoCase User-Agent ^AMZNKAssocBot bad_bot SetEnvIfNoCase User-Agent ^Anemone bad_bot SetEnvIfNoCase User-Agent ^Anonymous/3G bot bad_bot SetEnvIfNoCase User-Agent ^Anonymouse.org bad_bot SetEnvIfNoCase User-Agent ^AnotherBot bad_bot SetEnvIfNoCase User-Agent ^AnswerBot bad_bot SetEnvIfNoCase User-Agent ^AnswerBus bad_bot SetEnvIfNoCase User-Agent ^AnswerChase PROve bad_bot SetEnvIfNoCase User-Agent ^AntBot bad_bot SetEnvIfNoCase User-Agent ^antibot- bad_bot SetEnvIfNoCase User-Agent ^AntiSantyWorm bad_bot SetEnvIfNoCase User-Agent ^Antro.Net bad_bot SetEnvIfNoCase User-Agent ^AONDE-Spider bad_bot SetEnvIfNoCase User-Agent ^Aport bad_bot SetEnvIfNoCase User-Agent ^appid: s~stremor-crawler- bad_bot SetEnvIfNoCase User-Agent ^Aqua_Products bad_bot SetEnvIfNoCase User-Agent ^AraBot bad_bot SetEnvIfNoCase User-Agent ^Arachmo bad_bot SetEnvIfNoCase User-Agent ^Arachnophilia bad_bot SetEnvIfNoCase User-Agent ^archive.org_bot bad_bot SetEnvIfNoCase User-Agent ^aria eQualizer bad_bot SetEnvIfNoCase User-Agent ^arianna.libero.it bad_bot SetEnvIfNoCase User-Agent ^Arikus_Spider bad_bot SetEnvIfNoCase User-Agent ^Art-Online.com bad_bot SetEnvIfNoCase User-Agent ^ArtavisBot bad_bot SetEnvIfNoCase User-Agent ^Artera bad_bot SetEnvIfNoCase User-Agent ^ASAHA Search Engine Turkey bad_bot SetEnvIfNoCase User-Agent ^ASpider/0.09 bad_bot SetEnvIfNoCase User-Agent ^ASPSeek bad_bot SetEnvIfNoCase User-Agent ^asterias bad_bot SetEnvIfNoCase User-Agent ^AstroFind bad_bot SetEnvIfNoCase User-Agent ^athenusbot bad_bot SetEnvIfNoCase User-Agent ^AtlocalBot bad_bot SetEnvIfNoCase User-Agent ^Atomic_Email_Hunter bad_bot SetEnvIfNoCase User-Agent ^attach bad_bot SetEnvIfNoCase User-Agent ^attrakt bad_bot SetEnvIfNoCase User-Agent ^Attributor.comBot bad_bot SetEnvIfNoCase User-Agent ^attributor bad_bot SetEnvIfNoCase User-Agent ^Attributor/Dejan- bad_bot SetEnvIfNoCase User-Agent ^augurfind bad_bot SetEnvIfNoCase User-Agent ^AURESYS bad_bot SetEnvIfNoCase User-Agent ^AutoBaron crawler bad_bot SetEnvIfNoCase User-Agent ^autoemailspider bad_bot SetEnvIfNoCase User-Agent ^autowebdir bad_bot SetEnvIfNoCase User-Agent ^AVSearch- bad_bot SetEnvIfNoCase User-Agent ^axfeedsbot bad_bot SetEnvIfNoCase User-Agent ^Axonize-bot bad_bot SetEnvIfNoCase User-Agent ^Ayna bad_bot SetEnvIfNoCase User-Agent ^b2w bad_bot SetEnvIfNoCase User-Agent ^b2w/0.1 bad_bot SetEnvIfNoCase User-Agent ^BackDoorBot bad_bot SetEnvIfNoCase User-Agent ^BackRub/ bad_bot SetEnvIfNoCase User-Agent ^BackStreet Browser bad_bot SetEnvIfNoCase User-Agent ^BackWeb bad_bot SetEnvIfNoCase User-Agent ^Baiduspider bad_bot SetEnvIfNoCase User-Agent ^BaiDuSpider bad_bot SetEnvIfNoCase User-Agent ^BaiduSpider bad_bot SetEnvIfNoCase User-Agent ^Baiduspider-video bad_bot SetEnvIfNoCase User-Agent ^Baiduspider+ bad_bot SetEnvIfNoCase User-Agent ^Bandit bad_bot SetEnvIfNoCase User-Agent ^BatchFTP bad_bot SetEnvIfNoCase User-Agent ^baypup bad_bot SetEnvIfNoCase User-Agent ^BDFetch bad_bot SetEnvIfNoCase User-Agent ^BecomeBot bad_bot SetEnvIfNoCase User-Agent ^BecomeJPBot bad_bot SetEnvIfNoCase User-Agent ^BeetleBot bad_bot SetEnvIfNoCase User-Agent ^Bender bad_bot SetEnvIfNoCase User-Agent ^besserscheitern-crawl bad_bot SetEnvIfNoCase User-Agent ^betaBot bad_bot SetEnvIfNoCase User-Agent ^Big Brother bad_bot SetEnvIfNoCase User-Agent ^Big Data bad_bot SetEnvIfNoCase User-Agent ^Bigado.com bad_bot SetEnvIfNoCase User-Agent ^BigCliqueBot bad_bot SetEnvIfNoCase User-Agent ^BigCliqueBOT bad_bot SetEnvIfNoCase User-Agent ^Bigfoot bad_bot SetEnvIfNoCase User-Agent ^BIGLOTRON bad_bot SetEnvIfNoCase User-Agent ^Bilbo bad_bot SetEnvIfNoCase User-Agent ^BilgiBetaBot bad_bot SetEnvIfNoCase User-Agent ^BilgiBot bad_bot SetEnvIfNoCase User-Agent ^bingbot bad_bot SetEnvIfNoCase User-Agent ^bintellibot bad_bot SetEnvIfNoCase User-Agent ^bitlybot bad_bot SetEnvIfNoCase User-Agent ^BitvoUserAgent bad_bot SetEnvIfNoCase User-Agent ^Bizbot003 bad_bot SetEnvIfNoCase User-Agent ^BizBot04 kirk.overleaf.com bad_bot SetEnvIfNoCase User-Agent ^BizWorks Retriever bad_bot SetEnvIfNoCase User-Agent ^Black Hole bad_bot SetEnvIfNoCase User-Agent ^Blackbird bad_bot SetEnvIfNoCase User-Agent ^BlackMask.Net Search Engine bad_bot SetEnvIfNoCase User-Agent ^BlackWidow bad_bot SetEnvIfNoCase User-Agent ^bladder fusion bad_bot SetEnvIfNoCase User-Agent ^Blaiz-Bee bad_bot SetEnvIfNoCase User-Agent ^BLEXBot bad_bot SetEnvIfNoCase User-Agent ^Blinkx/DFS-Fetch bad_bot SetEnvIfNoCase User-Agent ^BlitzBOT bad_bot SetEnvIfNoCase User-Agent ^Blog Conversation Project bad_bot SetEnvIfNoCase User-Agent ^BlogMyWay bad_bot SetEnvIfNoCase User-Agent ^BlogPulseLive bad_bot SetEnvIfNoCase User-Agent ^BlogRefsBot bad_bot SetEnvIfNoCase User-Agent ^BlogScope bad_bot SetEnvIfNoCase User-Agent ^Blogslive bad_bot SetEnvIfNoCase User-Agent ^BloobyBot bad_bot SetEnvIfNoCase User-Agent ^BlowFish bad_bot SetEnvIfNoCase User-Agent ^BLT bad_bot SetEnvIfNoCase User-Agent ^bnf.fr_bot bad_bot SetEnvIfNoCase User-Agent ^BoaConstrictor bad_bot SetEnvIfNoCase User-Agent ^BoardReader-Image-Fetcher bad_bot SetEnvIfNoCase User-Agent ^BOI_crawl_00 bad_bot SetEnvIfNoCase User-Agent ^BOIA-Scan-Agent bad_bot SetEnvIfNoCase User-Agent ^BOIA.ORG-Scan-Agent bad_bot SetEnvIfNoCase User-Agent ^boitho.com-dc bad_bot SetEnvIfNoCase User-Agent ^Bookmark Buddy bookmark checker bad_bot SetEnvIfNoCase User-Agent ^Bookmark search tool bad_bot SetEnvIfNoCase User-Agent ^bosug bad_bot SetEnvIfNoCase User-Agent ^Bot Apoena bad_bot SetEnvIfNoCase User-Agent ^Bot bad_bot SetEnvIfNoCase User-Agent ^bot bad_bot SetEnvIfNoCase User-Agent ^BOT bad_bot SetEnvIfNoCase User-Agent ^BotALot bad_bot SetEnvIfNoCase User-Agent ^BotRightHere bad_bot SetEnvIfNoCase User-Agent ^Botswana bad_bot SetEnvIfNoCase User-Agent ^bottybot bad_bot SetEnvIfNoCase User-Agent ^BpBot bad_bot SetEnvIfNoCase User-Agent ^BRAINTIME_SEARCH bad_bot SetEnvIfNoCase User-Agent ^BrokenLinkCheck.com bad_bot SetEnvIfNoCase User-Agent ^BrowserEmulator bad_bot SetEnvIfNoCase User-Agent ^BrowserMob bad_bot SetEnvIfNoCase User-Agent ^BruinBot bad_bot SetEnvIfNoCase User-Agent ^BSearchR&D bad_bot SetEnvIfNoCase User-Agent ^BSpider bad_bot SetEnvIfNoCase User-Agent ^btbot bad_bot SetEnvIfNoCase User-Agent ^Btsearch bad_bot SetEnvIfNoCase User-Agent ^Buddy bad_bot SetEnvIfNoCase User-Agent ^Buibui bad_bot SetEnvIfNoCase User-Agent ^BuildCMS crawler bad_bot SetEnvIfNoCase User-Agent ^BuiltBotTough bad_bot SetEnvIfNoCase User-Agent ^Bullseye bad_bot SetEnvIfNoCase User-Agent ^bumblebee bad_bot SetEnvIfNoCase User-Agent ^BunnySlippers bad_bot SetEnvIfNoCase User-Agent ^BuscadorClarin bad_bot SetEnvIfNoCase User-Agent ^Buscaplus Robi bad_bot SetEnvIfNoCase User-Agent ^Butterfly bad_bot SetEnvIfNoCase User-Agent ^BuyHawaiiBot bad_bot SetEnvIfNoCase User-Agent ^BuzzBot bad_bot SetEnvIfNoCase User-Agent ^byindia bad_bot SetEnvIfNoCase User-Agent ^BYINDIA bad_bot SetEnvIfNoCase User-Agent ^BySpider bad_bot SetEnvIfNoCase User-Agent ^byteserver bad_bot SetEnvIfNoCase User-Agent ^bzBot bad_bot SetEnvIfNoCase User-Agent ^c r a w l 3 r bad_bot SetEnvIfNoCase User-Agent ^CacheBlaster bad_bot SetEnvIfNoCase User-Agent ^Caddbot bad_bot SetEnvIfNoCase User-Agent ^Cafi bad_bot SetEnvIfNoCase User-Agent ^Camcrawler bad_bot SetEnvIfNoCase User-Agent ^CamelStampede bad_bot SetEnvIfNoCase User-Agent ^Canon-WebRecord bad_bot SetEnvIfNoCase User-Agent ^Canon-WebRecordPro bad_bot SetEnvIfNoCase User-Agent ^CareerBot bad_bot SetEnvIfNoCase User-Agent ^cataguru bad_bot SetEnvIfNoCase User-Agent ^CatchBot bad_bot SetEnvIfNoCase User-Agent ^CatchBot bad_bot SetEnvIfNoCase User-Agent ^CazoodleBot bad_bot SetEnvIfNoCase User-Agent ^CCBot bad_bot SetEnvIfNoCase User-Agent ^CCGCrawl bad_bot SetEnvIfNoCase User-Agent ^ccubee bad_bot SetEnvIfNoCase User-Agent ^CD-Preload bad_bot SetEnvIfNoCase User-Agent ^CE-Preload bad_bot SetEnvIfNoCase User-Agent ^Cegbfeieh bad_bot SetEnvIfNoCase User-Agent ^Cerberian Drtrs bad_bot SetEnvIfNoCase User-Agent ^CERT FigleafBot bad_bot SetEnvIfNoCase User-Agent ^cfetch bad_bot SetEnvIfNoCase User-Agent ^CFNetwork bad_bot SetEnvIfNoCase User-Agent ^Chameleon bad_bot SetEnvIfNoCase User-Agent ^Charlotte bad_bot SetEnvIfNoCase User-Agent ^Check&Get bad_bot SetEnvIfNoCase User-Agent ^Checkbot bad_bot SetEnvIfNoCase User-Agent ^CheckLinks bad_bot SetEnvIfNoCase User-Agent ^Checklinks bad_bot SetEnvIfNoCase User-Agent ^CheeseBot bad_bot SetEnvIfNoCase User-Agent ^ChemieDE-NodeBot bad_bot SetEnvIfNoCase User-Agent ^CherryPicker bad_bot SetEnvIfNoCase User-Agent ^CherryPickerElite bad_bot SetEnvIfNoCase User-Agent ^CherryPickerSE bad_bot SetEnvIfNoCase User-Agent ^Chilkat bad_bot SetEnvIfNoCase User-Agent ^ChinaClaw bad_bot SetEnvIfNoCase User-Agent ^CipinetBot bad_bot SetEnvIfNoCase User-Agent ^cis455crawler bad_bot SetEnvIfNoCase User-Agent ^citeseerxbot bad_bot SetEnvIfNoCase User-Agent ^cizilla.com/Cizilla- bad_bot SetEnvIfNoCase User-Agent ^ClariaBot bad_bot SetEnvIfNoCase User-Agent ^Clushbot bad_bot SetEnvIfNoCase User-Agent ^COAST scan engine bad_bot SetEnvIfNoCase User-Agent ^COAST WebMaster Pro bad_bot SetEnvIfNoCase User-Agent ^coccoc bad_bot SetEnvIfNoCase User-Agent ^CollapsarWEB bad_bot SetEnvIfNoCase User-Agent ^Collector bad_bot SetEnvIfNoCase User-Agent ^Combine bad_bot SetEnvIfNoCase User-Agent ^combine bad_bot SetEnvIfNoCase User-Agent ^COMBINE bad_bot SetEnvIfNoCase User-Agent ^Compatible bad_bot SetEnvIfNoCase User-Agent ^ConnectSearch bad_bot SetEnvIfNoCase User-Agent ^conpilot bad_bot SetEnvIfNoCase User-Agent ^ContentSmartz bad_bot SetEnvIfNoCase User-Agent ^ContextAd Bot bad_bot SetEnvIfNoCase User-Agent ^contype bad_bot SetEnvIfNoCase User-Agent ^cookieNET bad_bot SetEnvIfNoCase User-Agent ^CoolBot� bad_bot SetEnvIfNoCase User-Agent ^CoolCheck bad_bot SetEnvIfNoCase User-Agent ^Copernic bad_bot SetEnvIfNoCase User-Agent ^Copier bad_bot SetEnvIfNoCase User-Agent ^CopyRightCheck bad_bot SetEnvIfNoCase User-Agent ^core-project bad_bot SetEnvIfNoCase User-Agent ^cosmos bad_bot SetEnvIfNoCase User-Agent ^Covario-IDS bad_bot SetEnvIfNoCase User-Agent ^Cowbot- bad_bot SetEnvIfNoCase User-Agent ^Cowdog Bot bad_bot SetEnvIfNoCase User-Agent ^crabbyBot bad_bot SetEnvIfNoCase User-Agent ^Crawl_Application bad_bot SetEnvIfNoCase User-Agent ^crawl.UserAgent bad_bot SetEnvIfNoCase User-Agent ^Crawl bad_bot SetEnvIfNoCase User-Agent ^crawl bad_bot SetEnvIfNoCase User-Agent ^CrawlConvera 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SetEnvIfNoCase User-Agent ^Link Validator bad_bot SetEnvIfNoCase User-Agent ^link_checker bad_bot SetEnvIfNoCase User-Agent ^LinkAlarm bad_bot SetEnvIfNoCase User-Agent ^linkbot bad_bot SetEnvIfNoCase User-Agent ^Linkbot bad_bot SetEnvIfNoCase User-Agent ^LinkCheck by Siteimprove.com bad_bot SetEnvIfNoCase User-Agent ^LinkCheck Scanner bad_bot SetEnvIfNoCase User-Agent ^LinkChecker bad_bot SetEnvIfNoCase User-Agent ^linkdex.com bad_bot SetEnvIfNoCase User-Agent ^LinkextractorPro bad_bot SetEnvIfNoCase User-Agent ^LinkLint bad_bot SetEnvIfNoCase User-Agent ^linklooker bad_bot SetEnvIfNoCase User-Agent ^Linkman bad_bot SetEnvIfNoCase User-Agent ^Links SQL bad_bot SetEnvIfNoCase User-Agent ^LinkScan bad_bot SetEnvIfNoCase User-Agent ^LinkScan/8.1a.Unix bad_bot SetEnvIfNoCase User-Agent ^LinksManager.com_bot bad_bot SetEnvIfNoCase User-Agent ^LinkSweeper bad_bot SetEnvIfNoCase User-Agent ^LinkWalker bad_bot SetEnvIfNoCase User-Agent ^LiteFinder bad_bot SetEnvIfNoCase User-Agent ^LitlrBot 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bad_bot SetEnvIfNoCase User-Agent ^NetSpider bad_bot SetEnvIfNoCase User-Agent ^NetSrcherP bad_bot SetEnvIfNoCase User-Agent ^NetZIP bad_bot SetEnvIfNoCase User-Agent ^NewMedhunt bad_bot SetEnvIfNoCase User-Agent ^news bot bad_bot SetEnvIfNoCase User-Agent ^News_Search_App bad_bot SetEnvIfNoCase User-Agent ^NewsGatherer bad_bot SetEnvIfNoCase User-Agent ^Newsgroupreporter bad_bot SetEnvIfNoCase User-Agent ^NewsTroveBot bad_bot SetEnvIfNoCase User-Agent ^NextGenSearchBot bad_bot SetEnvIfNoCase User-Agent ^nextthing.org bad_bot SetEnvIfNoCase User-Agent ^NG bad_bot SetEnvIfNoCase User-Agent ^nicebot bad_bot SetEnvIfNoCase User-Agent ^NICErsPRO bad_bot SetEnvIfNoCase User-Agent ^niki-bot bad_bot SetEnvIfNoCase User-Agent ^NimbleCrawler bad_bot SetEnvIfNoCase User-Agent ^nimbus-1 bad_bot SetEnvIfNoCase User-Agent ^ninetowns bad_bot SetEnvIfNoCase User-Agent ^Ninja bad_bot SetEnvIfNoCase User-Agent ^NjuiceBot bad_bot SetEnvIfNoCase User-Agent ^NLese bad_bot SetEnvIfNoCase User-Agent ^NLESE 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Spider bad_bot SetEnvIfNoCase User-Agent ^searchmining bad_bot SetEnvIfNoCase User-Agent ^SearchnowBot_v1 bad_bot SetEnvIfNoCase User-Agent ^searchpreview bad_bot SetEnvIfNoCase User-Agent ^SearchSpider.com bad_bot SetEnvIfNoCase User-Agent ^SearQuBot/SearQuBot v1.0 bad_bot SetEnvIfNoCase User-Agent ^SEB Spider bad_bot SetEnvIfNoCase User-Agent ^Seekbot bad_bot SetEnvIfNoCase User-Agent ^Seeker.lookseek.com bad_bot SetEnvIfNoCase User-Agent ^SeeqBot bad_bot SetEnvIfNoCase User-Agent ^seeqpod-vertical-crawler bad_bot SetEnvIfNoCase User-Agent ^Selflinkchecker bad_bot SetEnvIfNoCase User-Agent ^Semager bad_bot SetEnvIfNoCase User-Agent ^semanticdiscovery bad_bot SetEnvIfNoCase User-Agent ^Semantifire1 bad_bot SetEnvIfNoCase User-Agent ^semisearch bad_bot SetEnvIfNoCase User-Agent ^SemrushBot bad_bot SetEnvIfNoCase User-Agent ^SEOENGWorldBot bad_bot SetEnvIfNoCase User-Agent ^SeznamBot bad_bot SetEnvIfNoCase User-Agent ^SeznamBot/2.0 bad_bot SetEnvIfNoCase User-Agent ^SeznamBot/3.2-test1 bad_bot SetEnvIfNoCase User-Agent ^ShablastBot bad_bot SetEnvIfNoCase User-Agent ^ShadowWebAnalyzer bad_bot SetEnvIfNoCase User-Agent ^Shareaza bad_bot SetEnvIfNoCase User-Agent ^Shelob bad_bot SetEnvIfNoCase User-Agent ^sherlock bad_bot SetEnvIfNoCase User-Agent ^ShopWiki bad_bot SetEnvIfNoCase User-Agent ^ShowLinks bad_bot SetEnvIfNoCase User-Agent ^ShowyouBot bad_bot SetEnvIfNoCase User-Agent ^siclab bad_bot SetEnvIfNoCase User-Agent ^silk bad_bot SetEnvIfNoCase User-Agent ^Siphon bad_bot SetEnvIfNoCase User-Agent ^SiteArchive bad_bot SetEnvIfNoCase User-Agent ^SiteCheck-sitecrawl by Siteimprove.com bad_bot SetEnvIfNoCase User-Agent ^sitecheck.internetseer.com bad_bot SetEnvIfNoCase User-Agent ^SiteFinder bad_bot SetEnvIfNoCase User-Agent ^SiteGuardBot bad_bot SetEnvIfNoCase User-Agent ^SiteOrbiter bad_bot SetEnvIfNoCase User-Agent ^SiteSnagger bad_bot SetEnvIfNoCase User-Agent ^SiteSucker bad_bot SetEnvIfNoCase User-Agent ^SiteSweeper bad_bot SetEnvIfNoCase User-Agent ^SiteXpert bad_bot SetEnvIfNoCase User-Agent ^SkimBot bad_bot SetEnvIfNoCase User-Agent ^SkimWordsBot bad_bot SetEnvIfNoCase User-Agent ^SkreemRBot bad_bot SetEnvIfNoCase User-Agent ^Skywalker bad_bot SetEnvIfNoCase User-Agent ^Sleipnir bad_bot SetEnvIfNoCase User-Agent ^slow-crawler bad_bot SetEnvIfNoCase User-Agent ^SlySearch bad_bot SetEnvIfNoCase User-Agent ^smart-crawler bad_bot SetEnvIfNoCase User-Agent ^SmartDownload bad_bot SetEnvIfNoCase User-Agent ^Smarte Bot bad_bot SetEnvIfNoCase User-Agent ^smartwit.com bad_bot SetEnvIfNoCase User-Agent ^Snake bad_bot SetEnvIfNoCase User-Agent ^snap.com beta crawler bad_bot SetEnvIfNoCase User-Agent ^Snapbot bad_bot SetEnvIfNoCase User-Agent ^SnapPreviewBot bad_bot SetEnvIfNoCase User-Agent ^Snappy bad_bot SetEnvIfNoCase User-Agent ^snookit/Snookit bad_bot SetEnvIfNoCase User-Agent ^Snooper/b97_01 bad_bot SetEnvIfNoCase User-Agent ^Snoopy bad_bot SetEnvIfNoCase User-Agent ^SocialSearcher/0.1 bad_bot SetEnvIfNoCase User-Agent ^SocSciBot bad_bot SetEnvIfNoCase User-Agent ^SOFT411 Directory bad_bot SetEnvIfNoCase User-Agent ^sogou bad_bot SetEnvIfNoCase User-Agent ^Sogou bad_bot SetEnvIfNoCase User-Agent ^sohu agent bad_bot SetEnvIfNoCase User-Agent ^sohu-search bad_bot SetEnvIfNoCase User-Agent ^Sokitomi crawl bad_bot SetEnvIfNoCase User-Agent ^Solbot/1.0 LWP/5.07 bad_bot SetEnvIfNoCase User-Agent ^sootle bad_bot SetEnvIfNoCase User-Agent ^Sosospider bad_bot SetEnvIfNoCase User-Agent ^Space Bison bad_bot SetEnvIfNoCase User-Agent ^Space Fung bad_bot SetEnvIfNoCase User-Agent ^SpaceBison bad_bot SetEnvIfNoCase User-Agent ^SpankBot bad_bot SetEnvIfNoCase User-Agent ^spanner bad_bot SetEnvIfNoCase User-Agent ^Spanner/1.0 (Linux 2.0.27 i586) bad_bot SetEnvIfNoCase User-Agent ^Spatineo Monitor Controller bad_bot SetEnvIfNoCase User-Agent ^Spatineo Serval Controller bad_bot SetEnvIfNoCase User-Agent ^Spatineo Serval GetMapBot bad_bot SetEnvIfNoCase User-Agent ^special_archiver bad_bot SetEnvIfNoCase User-Agent ^Speedy Spider bad_bot SetEnvIfNoCase User-Agent ^SpeedySpider bad_bot SetEnvIfNoCase User-Agent ^Sphere Scout bad_bot SetEnvIfNoCase User-Agent ^Sphere Scout&v bad_bot SetEnvIfNoCase User-Agent ^Sphider bad_bot SetEnvIfNoCase User-Agent ^Sphider2 bad_bot SetEnvIfNoCase User-Agent ^spider bad_bot SetEnvIfNoCase User-Agent ^Spider bad_bot SetEnvIfNoCase User-Agent ^Spider.TerraNautic.net bad_bot SetEnvIfNoCase User-Agent ^SpiderEngine bad_bot SetEnvIfNoCase User-Agent ^SpiderKU bad_bot SetEnvIfNoCase User-Agent ^SpiderMan bad_bot SetEnvIfNoCase User-Agent ^Spinn3r bad_bot SetEnvIfNoCase User-Agent ^Spinne bad_bot SetEnvIfNoCase User-Agent ^sportcrew-Bot bad_bot SetEnvIfNoCase User-Agent ^spyder3.microsys.com bad_bot SetEnvIfNoCase User-Agent ^SQ Webscanner bad_bot SetEnvIfNoCase User-Agent ^sqlmap bad_bot SetEnvIfNoCase User-Agent ^Squid-Prefetch bad_bot SetEnvIfNoCase User-Agent ^SquidClamAV_Redirector bad_bot SetEnvIfNoCase User-Agent ^Sqworm bad_bot SetEnvIfNoCase User-Agent ^SrevBot bad_bot SetEnvIfNoCase 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SetEnvIfNoCase User-Agent ^SuperHTTP bad_bot SetEnvIfNoCase User-Agent ^SuperLumin Downloader bad_bot SetEnvIfNoCase User-Agent ^SuperPagesBot bad_bot SetEnvIfNoCase User-Agent ^Supybot bad_bot SetEnvIfNoCase User-Agent ^SURF bad_bot SetEnvIfNoCase User-Agent ^Surfbot bad_bot SetEnvIfNoCase User-Agent ^SurfControl bad_bot SetEnvIfNoCase User-Agent ^SurveyBot bad_bot SetEnvIfNoCase User-Agent ^suzuran bad_bot SetEnvIfNoCase User-Agent ^SWEBot bad_bot SetEnvIfNoCase User-Agent ^swish-e bad_bot SetEnvIfNoCase User-Agent ^SygolBot bad_bot SetEnvIfNoCase User-Agent ^SynapticWalker bad_bot SetEnvIfNoCase User-Agent ^Syntryx ANT Scout Chassis Pheromone bad_bot SetEnvIfNoCase User-Agent ^SystemSearch-robot bad_bot SetEnvIfNoCase User-Agent ^Szukacz bad_bot SetEnvIfNoCase User-Agent ^T-H-U-N-D-E-R-S-T-O-N-E bad_bot SetEnvIfNoCase User-Agent ^Tailrank bad_bot SetEnvIfNoCase User-Agent ^tAkeOut bad_bot SetEnvIfNoCase User-Agent ^Talkro Web-Shot bad_bot SetEnvIfNoCase User-Agent ^TAMU_CRAWLER bad_bot SetEnvIfNoCase User-Agent ^TapuzBot bad_bot SetEnvIfNoCase User-Agent ^Tarantula bad_bot SetEnvIfNoCase User-Agent ^targetblaster.com bad_bot SetEnvIfNoCase User-Agent ^TargetYourNews.com bot bad_bot SetEnvIfNoCase User-Agent ^TAUSDataBot bad_bot SetEnvIfNoCase User-Agent ^taxinomiabot bad_bot SetEnvIfNoCase User-Agent ^TeamSoft WinInet Component bad_bot SetEnvIfNoCase User-Agent ^Tecomi Bot bad_bot SetEnvIfNoCase User-Agent ^TeezirBot bad_bot SetEnvIfNoCase User-Agent ^Teleport bad_bot SetEnvIfNoCase User-Agent ^Telesoft bad_bot SetEnvIfNoCase User-Agent ^Teradex Mapper bad_bot SetEnvIfNoCase User-Agent ^TERAGRAM_CRAWLER bad_bot SetEnvIfNoCase User-Agent ^TerrawizBot bad_bot SetEnvIfNoCase User-Agent ^TestBot bad_bot SetEnvIfNoCase User-Agent ^testbot bad_bot SetEnvIfNoCase User-Agent ^testBOT bad_bot SetEnvIfNoCase User-Agent ^testing of bot bad_bot SetEnvIfNoCase User-Agent ^TextBot bad_bot SetEnvIfNoCase User-Agent ^thatrobotsite.com bad_bot SetEnvIfNoCase User-Agent ^The Dyslexalizer bad_bot SetEnvIfNoCase User-Agent ^The Intraformant bad_bot SetEnvIfNoCase User-Agent ^The.Intraformant bad_bot SetEnvIfNoCase User-Agent ^TheNomad bad_bot SetEnvIfNoCase User-Agent ^Theophrastus bad_bot SetEnvIfNoCase User-Agent ^theusefulbot bad_bot SetEnvIfNoCase User-Agent ^TheUsefulbot_ bad_bot SetEnvIfNoCase User-Agent ^ThumbBot bad_bot SetEnvIfNoCase User-Agent ^Thumbnail.CZ robot bad_bot SetEnvIfNoCase User-Agent ^thumbshots-de-bot bad_bot SetEnvIfNoCase User-Agent ^tigerbot bad_bot SetEnvIfNoCase User-Agent ^TightTwatBot bad_bot SetEnvIfNoCase User-Agent ^TinEye bad_bot SetEnvIfNoCase User-Agent ^Titan bad_bot SetEnvIfNoCase User-Agent ^TITAN bad_bot SetEnvIfNoCase User-Agent ^to-dress_ru_bot_ bad_bot SetEnvIfNoCase User-Agent ^to-night-Bot bad_bot SetEnvIfNoCase User-Agent ^toCrawl/UrlDispatcher bad_bot SetEnvIfNoCase User-Agent ^Topicalizer bad_bot SetEnvIfNoCase User-Agent ^topicblogs bad_bot SetEnvIfNoCase User-Agent ^Toplistbot bad_bot SetEnvIfNoCase 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^TurnitinBot bad_bot SetEnvIfNoCase User-Agent ^TurnitinBot/1.5 bad_bot SetEnvIfNoCase User-Agent ^TutorGigBot bad_bot SetEnvIfNoCase User-Agent ^TweetedTimes Bot bad_bot SetEnvIfNoCase User-Agent ^TweetmemeBot bad_bot SetEnvIfNoCase User-Agent ^TwengaBot bad_bot SetEnvIfNoCase User-Agent ^TwengaBot-Discover bad_bot SetEnvIfNoCase User-Agent ^TwengaBot bad_bot SetEnvIfNoCase User-Agent ^Twiceler bad_bot SetEnvIfNoCase User-Agent ^Twikle bad_bot SetEnvIfNoCase User-Agent ^twinuffbot bad_bot SetEnvIfNoCase User-Agent ^Twisted PageGetter bad_bot SetEnvIfNoCase User-Agent ^Twitturls bad_bot SetEnvIfNoCase User-Agent ^Twitturly bad_bot SetEnvIfNoCase User-Agent ^TygoBot bad_bot SetEnvIfNoCase User-Agent ^TygoProwler bad_bot SetEnvIfNoCase User-Agent ^Typhoeus bad_bot SetEnvIfNoCase User-Agent ^U.S. Government Printing Office bad_bot SetEnvIfNoCase User-Agent ^uberbot bad_bot SetEnvIfNoCase User-Agent ^ucb-nutch bad_bot SetEnvIfNoCase User-Agent ^UdmSearch bad_bot SetEnvIfNoCase User-Agent ^UFAM-crawler- bad_bot SetEnvIfNoCase User-Agent ^Ultraseek bad_bot SetEnvIfNoCase User-Agent ^UnChaos bad_bot SetEnvIfNoCase User-Agent ^unchaos_crawler_ bad_bot SetEnvIfNoCase User-Agent ^UnisterBot bad_bot SetEnvIfNoCase User-Agent ^Unitek UniEngine bad_bot SetEnvIfNoCase User-Agent ^UniversalSearch bad_bot SetEnvIfNoCase User-Agent ^UnwindFetchor bad_bot SetEnvIfNoCase User-Agent ^UnwindFetchor bad_bot SetEnvIfNoCase User-Agent ^UofTDB_experiment bad_bot SetEnvIfNoCase User-Agent ^updated bad_bot SetEnvIfNoCase User-Agent ^URL Control bad_bot SetEnvIfNoCase User-Agent ^url_gather bad_bot SetEnvIfNoCase User-Agent ^URL-Checker bad_bot SetEnvIfNoCase User-Agent ^URLAppendBot bad_bot SetEnvIfNoCase User-Agent ^URLBlaze bad_bot SetEnvIfNoCase User-Agent ^urlchecker bad_bot SetEnvIfNoCase User-Agent ^urlck/1.2.3 bad_bot SetEnvIfNoCase User-Agent ^UrlDispatcher bad_bot SetEnvIfNoCase User-Agent ^URLSpiderPro bad_bot SetEnvIfNoCase User-Agent ^URLy Warning bad_bot SetEnvIfNoCase User-Agent ^URLy.Warning bad_bot SetEnvIfNoCase User-Agent ^USAF AFKN K2SPIDER bad_bot SetEnvIfNoCase User-Agent ^usasearch bad_bot SetEnvIfNoCase User-Agent ^USS-Cosmix bad_bot SetEnvIfNoCase User-Agent ^USyd-NLP-Spider bad_bot SetEnvIfNoCase User-Agent ^Vacobot bad_bot SetEnvIfNoCase User-Agent ^Vacuum bad_bot SetEnvIfNoCase User-Agent ^VadixBot bad_bot SetEnvIfNoCase User-Agent ^Vagabondo bad_bot SetEnvIfNoCase User-Agent ^Validator bad_bot SetEnvIfNoCase User-Agent ^Valkyrie/1.0 libwww-perl/0.40 bad_bot SetEnvIfNoCase User-Agent ^vBSEO bad_bot SetEnvIfNoCase User-Agent ^VCI bad_bot SetEnvIfNoCase User-Agent ^VCI WebViewer VCI WebViewer Win32 bad_bot SetEnvIfNoCase User-Agent ^VerbstarBot bad_bot SetEnvIfNoCase User-Agent ^VeriCiteCrawler/Nutch-2.2.1 bad_bot SetEnvIfNoCase User-Agent ^Verifactrola bad_bot SetEnvIfNoCase User-Agent ^Verity-URL-Gateway bad_bot SetEnvIfNoCase User-Agent ^Vermut bad_bot SetEnvIfNoCase User-Agent ^vermut bad_bot SetEnvIfNoCase User-Agent ^versus crawler bad_bot SetEnvIfNoCase User-Agent ^versus.integis.ch bad_bot SetEnvIfNoCase User-Agent ^viasarchivinginformation.html bad_bot SetEnvIfNoCase User-Agent ^VIP bad_bot SetEnvIfNoCase User-Agent ^VIPr bad_bot SetEnvIfNoCase User-Agent ^virus-detector bad_bot SetEnvIfNoCase User-Agent ^VisBot bad_bot SetEnvIfNoCase User-Agent ^Vishal For CLIA bad_bot SetEnvIfNoCase User-Agent ^VisWeb bad_bot SetEnvIfNoCase User-Agent ^vlad bad_bot SetEnvIfNoCase User-Agent ^vlsearch bad_bot SetEnvIfNoCase User-Agent ^VMBot bad_bot SetEnvIfNoCase User-Agent ^VocusBot bad_bot SetEnvIfNoCase User-Agent ^VoidEYE bad_bot SetEnvIfNoCase User-Agent ^VoilaBot bad_bot SetEnvIfNoCase User-Agent ^Vortex bad_bot SetEnvIfNoCase User-Agent ^voyager-hc bad_bot SetEnvIfNoCase User-Agent ^voyager-partner-deep bad_bot SetEnvIfNoCase User-Agent ^voyager bad_bot SetEnvIfNoCase User-Agent ^VSE bad_bot SetEnvIfNoCase User-Agent ^vspider bad_bot SetEnvIfNoCase User-Agent ^W3C_Unicorn bad_bot SetEnvIfNoCase User-Agent ^W3C-WebCon bad_bot SetEnvIfNoCase User-Agent ^w3m bad_bot SetEnvIfNoCase User-Agent ^w3search bad_bot SetEnvIfNoCase User-Agent ^wacbot bad_bot SetEnvIfNoCase User-Agent ^wastrix bad_bot SetEnvIfNoCase User-Agent ^Water Conserve Portal bad_bot SetEnvIfNoCase User-Agent ^Water Conserve Spider bad_bot SetEnvIfNoCase User-Agent ^WatzBot bad_bot SetEnvIfNoCase User-Agent ^wauuu engine/Wauuu bad_bot SetEnvIfNoCase User-Agent ^Wavefire bad_bot SetEnvIfNoCase User-Agent ^Waypath bad_bot SetEnvIfNoCase User-Agent ^Wazzup bad_bot SetEnvIfNoCase User-Agent ^Wazzup1.0.4800 bad_bot SetEnvIfNoCase User-Agent ^wbdbot bad_bot SetEnvIfNoCase User-Agent ^Web CEO Online robot bad_bot SetEnvIfNoCase User-Agent ^web crawler bad_bot SetEnvIfNoCase User-Agent ^Web Downloader bad_bot SetEnvIfNoCase User-Agent ^Web Image Collector bad_bot SetEnvIfNoCase User-Agent ^Web Link Validator bad_bot SetEnvIfNoCase User-Agent ^Web Magnet bad_bot SetEnvIfNoCase User-Agent ^Web Site Downloader bad_bot SetEnvIfNoCase User-Agent ^Web Sucker bad_bot SetEnvIfNoCase User-Agent ^web-agent bad_bot SetEnvIfNoCase User-Agent ^Web-Sniffer bad_bot SetEnvIfNoCase User-Agent ^Web.Image.Collector bad_bot SetEnvIfNoCase User-Agent ^WebaltBot bad_bot SetEnvIfNoCase User-Agent ^WebAuto bad_bot SetEnvIfNoCase User-Agent ^WebBandit bad_bot SetEnvIfNoCase User-Agent ^webbandit bad_bot SetEnvIfNoCase User-Agent ^WebBandit/3.50 bad_bot SetEnvIfNoCase User-Agent ^WebBOT bad_bot SetEnvIfNoCase User-Agent ^webbot bad_bot SetEnvIfNoCase User-Agent ^WebBot bad_bot SetEnvIfNoCase User-Agent ^webbul-bot bad_bot SetEnvIfNoCase User-Agent ^WebCapture bad_bot SetEnvIfNoCase User-Agent ^WebCapture 2.0 bad_bot SetEnvIfNoCase User-Agent ^webcheck 1 bad_bot SetEnvIfNoCase User-Agent ^Webclipping.com bad_bot SetEnvIfNoCase User-Agent ^Webclipping.com bad_bot SetEnvIfNoCase User-Agent ^webcollage bad_bot SetEnvIfNoCase User-Agent ^WebCopier bad_bot SetEnvIfNoCase User-Agent ^WebCopy bad_bot SetEnvIfNoCase User-Agent ^WebCorp bad_bot SetEnvIfNoCase User-Agent ^webcrawl.net bad_bot SetEnvIfNoCase User-Agent ^WebCrawler bad_bot SetEnvIfNoCase User-Agent ^webcrawler bad_bot SetEnvIfNoCase User-Agent ^WebCrawler/3.0 Robot libwww/5.0a bad_bot SetEnvIfNoCase User-Agent ^WebDownloader for X bad_bot SetEnvIfNoCase User-Agent ^Webdup bad_bot SetEnvIfNoCase User-Agent ^WebEMailExtrac bad_bot SetEnvIfNoCase User-Agent ^WebEnhancer bad_bot SetEnvIfNoCase User-Agent ^WebFetch bad_bot SetEnvIfNoCase User-Agent ^webfetch bad_bot SetEnvIfNoCase User-Agent ^WebFetcher/0.8, bad_bot SetEnvIfNoCase User-Agent ^WebGather bad_bot SetEnvIfNoCase User-Agent ^WebGo IS bad_bot SetEnvIfNoCase User-Agent ^webGobbler bad_bot SetEnvIfNoCase User-Agent ^WebImages bad_bot SetEnvIfNoCase User-Agent ^Webinator-search2.fasthealth.com bad_bot SetEnvIfNoCase User-Agent ^Webinator-WBI bad_bot SetEnvIfNoCase User-Agent ^WebIndex bad_bot SetEnvIfNoCase User-Agent ^WebIndexer bad_bot SetEnvIfNoCase User-Agent ^weblayers/0.0 bad_bot SetEnvIfNoCase 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SetEnvIfNoCase User-Agent ^WebSpider bad_bot SetEnvIfNoCase User-Agent ^Webster bad_bot SetEnvIfNoCase User-Agent ^WebStripper bad_bot SetEnvIfNoCase User-Agent ^WebTrafficExpress bad_bot SetEnvIfNoCase User-Agent ^WebTrends Link Analyzer bad_bot SetEnvIfNoCase User-Agent ^WebVac bad_bot SetEnvIfNoCase User-Agent ^webwalk bad_bot SetEnvIfNoCase User-Agent ^WebWalker bad_bot SetEnvIfNoCase User-Agent ^WebWasher bad_bot SetEnvIfNoCase User-Agent ^Webwasher bad_bot SetEnvIfNoCase User-Agent ^WebWatch bad_bot SetEnvIfNoCase User-Agent ^WebWhacker bad_bot SetEnvIfNoCase User-Agent ^WebXM bad_bot SetEnvIfNoCase User-Agent ^WebZIP bad_bot SetEnvIfNoCase User-Agent ^Weddings.info Bot bad_bot SetEnvIfNoCase User-Agent ^wenbin/search bad_bot SetEnvIfNoCase User-Agent ^WEP Search bad_bot SetEnvIfNoCase User-Agent ^WEPA bad_bot SetEnvIfNoCase User-Agent ^WeRelateBot bad_bot SetEnvIfNoCase User-Agent ^Whacker bad_bot SetEnvIfNoCase User-Agent ^Whirlpool Web Engine bad_bot SetEnvIfNoCase User-Agent ^WhoWhere Robot bad_bot SetEnvIfNoCase User-Agent ^Widow bad_bot SetEnvIfNoCase User-Agent ^WikiaBot bad_bot SetEnvIfNoCase User-Agent ^Wikio bad_bot SetEnvIfNoCase User-Agent ^wikiwix-bot- bad_bot SetEnvIfNoCase User-Agent ^WinHTTP Example bad_bot SetEnvIfNoCase User-Agent ^WinHttp.WinHttpRequest bad_bot SetEnvIfNoCase User-Agent ^WIRE bad_bot SetEnvIfNoCase User-Agent ^wired-digital-newsbot/1.5 bad_bot SetEnvIfNoCase User-Agent ^WISEbot bad_bot SetEnvIfNoCase User-Agent ^WISENutbot bad_bot SetEnvIfNoCase User-Agent ^wish-la bad_bot SetEnvIfNoCase User-Agent ^wish-project bad_bot SetEnvIfNoCase User-Agent ^wisponbot bad_bot SetEnvIfNoCase User-Agent ^WMCAI-robot bad_bot SetEnvIfNoCase User-Agent ^wminer bad_bot SetEnvIfNoCase User-Agent ^WMSBot bad_bot SetEnvIfNoCase User-Agent ^woriobot bad_bot SetEnvIfNoCase User-Agent ^worldshop bad_bot SetEnvIfNoCase User-Agent ^WorQmada bad_bot SetEnvIfNoCase User-Agent ^Wotbox bad_bot SetEnvIfNoCase User-Agent ^wume_crawler bad_bot SetEnvIfNoCase User-Agent ^WWW Collector bad_bot SetEnvIfNoCase User-Agent ^WWW-Mechanize bad_bot SetEnvIfNoCase User-Agent ^WWWOFFLE bad_bot SetEnvIfNoCase User-Agent ^wwwrobot bad_bot SetEnvIfNoCase User-Agent ^wwwster bad_bot SetEnvIfNoCase User-Agent ^WWWWanderer v3.0 bad_bot SetEnvIfNoCase User-Agent ^wwwxref bad_bot SetEnvIfNoCase User-Agent ^Wysigot bad_bot SetEnvIfNoCase User-Agent ^X-clawler bad_bot SetEnvIfNoCase User-Agent ^Xaldon bad_bot SetEnvIfNoCase User-Agent ^Xenu bad_bot SetEnvIfNoCase User-Agent ^Xerka MetaBot bad_bot SetEnvIfNoCase User-Agent ^Xerka WebBot bad_bot SetEnvIfNoCase User-Agent ^XGET/0.7 bad_bot SetEnvIfNoCase User-Agent ^xirq bad_bot SetEnvIfNoCase User-Agent ^XmarksFetch bad_bot SetEnvIfNoCase User-Agent ^XoviBot bad_bot SetEnvIfNoCase User-Agent ^XoviBot/2.0 bad_bot SetEnvIfNoCase User-Agent ^xqrobot bad_bot SetEnvIfNoCase User-Agent ^Y!J-BRI bad_bot SetEnvIfNoCase User-Agent ^Y!J-BRJ/YATS crawler bad_bot SetEnvIfNoCase User-Agent ^Y!J-BRW bad_bot 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SetEnvIfNoCase User-Agent ^yolinkBot bad_bot SetEnvIfNoCase User-Agent ^yoogliFetchAgent bad_bot SetEnvIfNoCase User-Agent ^Yoono bad_bot SetEnvIfNoCase User-Agent ^yoono bad_bot SetEnvIfNoCase User-Agent ^Yoriwa bad_bot SetEnvIfNoCase User-Agent ^YottaCars_Bot bad_bot SetEnvIfNoCase User-Agent ^you-dir bad_bot SetEnvIfNoCase User-Agent ^Z-Add Link Checker bad_bot SetEnvIfNoCase User-Agent ^zagrebin bad_bot SetEnvIfNoCase User-Agent ^Zao bad_bot SetEnvIfNoCase User-Agent ^zedzo.digest bad_bot SetEnvIfNoCase User-Agent ^zedzo.validate bad_bot SetEnvIfNoCase User-Agent ^zermelo bad_bot SetEnvIfNoCase User-Agent ^Zeus bad_bot SetEnvIfNoCase User-Agent ^zibber-v bad_bot SetEnvIfNoCase User-Agent ^zimeno bad_bot SetEnvIfNoCase User-Agent ^Zing-BottaBot bad_bot SetEnvIfNoCase User-Agent ^ZipppBot bad_bot SetEnvIfNoCase User-Agent ^ZoomSpider bad_bot SetEnvIfNoCase User-Agent ^Zotag Search bad_bot SetEnvIfNoCase User-Agent ^Zotag Search bad_bot SetEnvIfNoCase User-Agent ^ZuiBot bad_bot 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/the/location/to/your/sfs-365.conf; The following code will download the latest SFS configuration. I have mine setup to run nightly through cron. Make sure to change the PROFILE and STORE variables to match what you want. #!/bin/bash # # -------------------------------------------------------------------------------------------- # Nginx Ban List Auto Downloader # -------------------------------------------------------------------------------------------- # -(sfs-30) # -(sfs-90) # -(sfs-full) # -(sfs-180) # -(sfs-365) # # BURL :: base URL, this is the root of where our files are located. # STORE :: this is where the place we store and delete the old file. # -------------------------------------------------------------------------------------------- # Setting up crontab # -------------------------------------------------------------------------------------------- # in it enter :: 1 1 * * * cd /path-to-script/ && ./nbl.sh # Make sure you chmod +x the this script so it can be executed. PROFILE='sfs-90' BURL='https://cdn.content-network.net/nbl/' STORE='/hosting/nginx-config/' # -------------------------------------------------------------------------------------------- # Core App # -------------------------------------------------------------------------------------------- echo "Starting Nginx Ban List." cd ${STORE} echo "Deleting Old Configs." rm ${PROFILE}.conf echo "Getting New Configs." wget ${BURL}${PROFILE}.conf --no-check-certificate -O sfs-latest.conf echo "Reloading Nginx." /etc/init.d/nginx reload echo "Done." # ----------------- --------------------------------------------------------------------------- # End of Core App # -------------------------------------------------------------------------------------------- IIS Bad-Bot Blocker Open up your site's web.config file and add the following to the rewrite section: Read the full article
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Data analysis tools. Week2 assignment.
The hypothesis under investigation is whether there is relation between the alcohol dependence (response categorical variable S2BQ1B1 with 2 levels namely 1-yes & 2-no) and the frequency of alcohol usage (explanatory categorical variable S2AQ4B with 7 categories namely 1, 3, 5, 9, 14, 22 & 30). In order to fiind out whether to accept or reject the above-stated hypothesis I have executed the post goc Chi-square test of independence (due to the fact there are more than 2 categories of the explanatory variable).
The Bonferroni adjustment is as follows:
p/c, where p=0.05 and c=21 (number of comparisons). So the adjusted p-value makes up 0.00238 or 0.238%.
The SAS program run is the following:
libname mydata "/courses/d1406ae5ba27fe300 " access=readonly; data new; set mydata.nesarc_pds; label S2BQ1B1="usual number of drinks had less effect past 12 months" S2AQ4B="frequency of alcohol use past 12 months" S2AQ4E="largest number of coolers on days when drank past 12 months" S2DQ1="father ever an alcoholic"; if S2BQ1B1=9 then S2BQ1B1=.; if s2aq4b=99 then s2aq4b=.; if S2AQ4E=99 then S2AQ4E=.; if S2DQ1=9 then S2DQ1=.; if S2AQ4B=1 then freqmo=30; else if S2AQ4B=2 then freqmo=22; else if S2AQ4B=3 then freqmo=14; else if S2AQ4B=4 then freqmo=9; else if S2AQ4B=5 then freqmo=5; else if S2AQ4B=6 then freqmo=3; else if S2AQ4B=7 then freqmo=1; if S2AQ4E LE 3 then alccategory=1.5; else if S2AQ4E LE 10 then alccategory=7; else if S2AQ4E LE 20 then alccategory=15; else if S2AQ4E GT 20 then alccategory=59; data comparison1; set new; if freqmo=1 or freqmo=3; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison2; set new; if freqmo=1 or freqmo=5; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison3; set new; if freqmo=1 or freqmo=9; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison4; set new; if freqmo=1 or freqmo=14; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison5; set new; if freqmo=1 or freqmo=22; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison6; set new; if freqmo=1 or freqmo=30; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison7; set new; if freqmo=3 or freqmo=5; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison8; set new; if freqmo=3 or freqmo=9; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison9; set new; if freqmo=3 or freqmo=14; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison10; set new; if freqmo=3 or freqmo=22; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison11; set new; if freqmo=3 or freqmo=30; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison12; set new; if freqmo=5 or freqmo=9; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison13; set new; if freqmo=5 or freqmo=14; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison14; set new; if freqmo=5 or freqmo=22; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison15; set new; if freqmo=5 or freqmo=30; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison16; set new; if freqmo=9 or freqmo=14; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison17; set new; if freqmo=9 or freqmo=22; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison18; set new; if freqmo=9 or freqmo=30; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison19; set new; if freqmo=14 or freqmo=22; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison20; set new; if freqmo=14 or freqmo=30; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run; data comparison21; set new; if freqmo=22 or freqmo=30; proc sort; by idnum; proc freq; tables S2BQ1B1*freqmo/chisq; run;
proc sort; by idnum; /*proc anova plots(maxpoints=none); class S2DQ1; model alccategory=S2DQ1; means S2DQ1*/; proc freq; tables S2BQ1B1*freqmo/chisq; run;
The conclusions:
1) A Chi Square test of independence revealed that alcohol usage frequency (collapsed into 7 categories) and past year alcohol dependence (binary categorical variable) were significantly associated, X2 =48.99, p=<.0001.
2) Post hoc comparisons of rates of alcohol dependence by pairs of frequency per month revealed that higher rates of alcohol dependence were seen among those drinking more alcohol. The range of frequency of alcohol usage with the p-value <=0.00238 or .238%, the Bonferri adjustment (and, thus, leading to the rejection of the null hypothesis saying there is no assosciation betwen alcohol dependence and frequency of alcohol consumption) is 1-5 &9-22, proving the first revelation.
Examples of the outputs:
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Vectometry is now part of Vecto
I wrote vecto to be able to draw stuff to PNGs. It’s based on the PostScript/PDF drawing model, all the way down to the level of function arguments. To move to a point, you use (move-to x y). Curves are done with (curve-to x1 y1 x2 y2 x3 y3). Color calls are done with (set-rgb-fill r g b), etc. Each function argument that has multiple components is passed with the components separated.
This is all right, I guess, but it’s also pretty inconvenient if you have an object that aggregates X and Y components to break them out all the time. Passing around six things instead of three or three things instead of one is annoying.
So, a long time ago, I made a more objecty frontend for Vecto and called it Vectometry. It has a load of convenience functions for working with points and colors as objects rather than separated components. It predefines some useful points (like *origin*) and colors (like *white* and *black*). It also adds a set of functions for working with rectangles, which are called “boxes” in the interface.
So, for example, the old vecto code might look like this:
(with-canvas (:height 100 :width 100) (move-to 0 0) (line-to 50 50) (line-to 100 0) (stroke) (save-png "foo.png"))
The new code looks something like this:
(let ((canvas (box 0 0 100 100))) (with-box-canvas canvas (move-to *origin*) (line-to (centerpoint canvas)) (line-to (bottom-right canvas)) (stroke) (save-png "bar.png")))
Boxes have maxpoint, minpoint, centerpoint, bottom-left, top-left, top-right, bottom-right, height, and width functions. They all return about what you’d expect.
But there’s also a combine function that takes two boxes, or a point and a box, or two points, and returns a box big enough to cover the two objects.
And expand takes a box and an amount, and returns a new box that has its corners moved out by the specified amount, in all directions. And the nice thing about with-box-canvas is that if your box doesn’t align with the origin, the drawing system still does - that is, the bottom left of your canvas box can be at positive or negative coordinates, but drawing at the origin will still draw at 0, 0.
displace takes a box and a point, and adds the point components to the minpoint of the box to produce a new box at a new location.
For points, there’s new add, sub, mul, and div functions that do about what you’d expect. And there’s also a function angle to get the angle between two points, and a function apoint that produces a point at a specified angle and distance from the origin.
Color objects are easier to make and pass around. rgb-color does what you’d expect, but there’s also an hsv-color that provides a much nicer way to get related colors, and even an html-color function so you can easily use “#rgb” or “#rrggbb” strings to set fill or stroke colors.
Everything else that doesn’t deal with component arguments is just passed through verbatim to Vecto, so things like stroke or fill-path don’t change.
This object stuff creates new objects all the time instead of mutating old ones. Maybe it’s slower and puts more pressure on the GC. But for the stuff I do I haven’t noticed and it hasn’t mattered.
I’ve been sitting on this vectometry code, using it for all my drawing needs for years but never publicizing it, because it wasn’t documented yet. But I’d rather put it into vecto and make it easily accessible and document it Someday rather than leave it buried.
If you like drawing stuff and 2D APIs and PNGs and stuff, try the latest vecto from Quicklisp and give it a whirl. If you have problems let me know. If you want me to post some example code and output images let me know. Enjoy!
edit Here’s some code I posted to twitter and 100-pointed and 5-pointed stars:
(defun star-demo (box points dip-factor) (let* ((radius (/ (max (height box) (width box)) 2)) (step (/ pi points)) (angle (/ pi 2)) (center (centerpoint box))) (with-box-canvas (expand box 5) (set-fill-color *white*) (clear-canvas) (set-stroke-color (rgba-color 0 0 0 0.5)) (centered-circle-path center radius) (set-line-width 10) (stroke) (translate center) (move-to (apoint angle radius)) (dotimes (i points) (incf angle step) (line-to (apoint angle (* radius dip-factor))) (incf angle step) (line-to (apoint angle radius))) (set-fill-color *black*) (fill-path) (save-png (format nil "star~D.png" points)))))
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Test a Multiple Regression Model
LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly; DATA new1; set mydata.nesarc_pds; /*Set appropriate missing data*/ IF S2AQ5B=99 THEN S2AQ5B=.; IF S2AQ5D=99 THEN S2AQ5D=.; IF S2DQ1=9 THEN S2DQ1=.; RENAME S2AQ5D=NUMBEERSCON; RENAME S2DQ1=FAMHIS; /*SUBSETTING DATA*/ IF ALCABDEP12DX GE 1 THEN ALCOHOLDEP=1; ELSE ALCOHOLDEP=0; IF AGE LE 60; IF S2AQ5B LE 7 THEN BEERFREQ=0; /*DRINK BEERS AT LEAST ONCE PER MONTH*/ ELSE BEERFREQ=1; /*DRINK BEERS AT LEAST ONCE A YEAR*/ */PROC SORT; /*BY IDNUM;*/ RUN; data new2; set new1; NUMBEERSCON_C=NUMBEERSCON-2.7249431; /*BEERFREQ_C=BEERFREQ-6.1190672;*/ AGE_C=AGE-38.3809769; RUN; /*PROC MEANS; VAR NUMBEERSCON_C AGE_C; PROC GLM; MODEL ALCOHOLDEP=BEERFREQ NUMBEERSCON_C AGE_C/SOLUTION CLPARM; PROC GLM; MODEL ALCOHOLDEP=BEERFREQ/SOLUTION CLPARM;*/ PROC GLM PLOTS (MAXPOINTS=NONE) PLOTS (UNPACK)=ALL; MODEL ALCOHOLDEP=BEERFREQ FAMHIS AGE_C NUMBEERSCON_C/SOLUTION CLPARM; output residual=res student=stdres out=results; RUN; PROC GPLOT; LABEL stdres="standardized residual" IDNUM="ID"; PLOT STDRES*IDNUM/VREF=0; RUN; /*DATA PARTIAL; SET NEW1; BEERFREQ1=BEERFREQ*BEERFREQ; RUN; PROC REG PLOTS=PARTIAL PLOTS (MAXPOINTS=NONE); MODEL ALCOHOLDEP=BEERFREQ BEERFREQ1 NUMBEERSCON_C/PARTIAL; RUN;
Is there an association between alcohol dependence and frequency with beers are consumed?
The data was first managed managed to appropriate missing data and ensuring that the categorical explanatory variables (BEERFREQ, FAMHIS) has one category as 0. the quantitative variable was centered (AGE).
As it stands the relationship with the variables are significant with p-values of <.0001 and parameter estimates are negative, indicating that less beer drinking frequency and age is associated with a lesser number of alcohol dependency.
Another potential co founder, number of beers consumed (NUMBEERCON_C) was added. We can conclude that after controlling for the other variables, number of beers consumed is positively associated with alcohol dependence.
The variables measured only account for 15% (R-Square) of the variability of alcohol dependence so clearly there are some errors in estimating the response value.
the Q-Q Plot do not follow perfect normal distribution or a straight line and therefore there are other explanatory variables that can be included to improve the estimation of the observed curvilinearity.
Looking at the plot of the standardized residuals, while most of the residuals fall within standard deviations of the mean, it be be noted that more than 5% have a value greater than 2 and therefore the level of error is unacceptable as it poorly fits the observed data.
The leverage plot shows outliers with residuals greater then 2, as highlighted in red, which indicates that they may influence negatively the estimation of the regression.
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Cheers to the freaking weekend!!!🖤🍍 📸 + 🍸by @welldonefoto Seriously, a #BlackCocktail with a #Lavender garnish?! #MaxPoints 😍 #JauntAndCo #JauntTrouvaille #DressedInBlack #WelcomeToTheDarkside #CheersToTheWeekend #cocktails #craftcocktail #cheers #thirsty #FriYAY #TGIF #HappyFriday #AdventuresInCharcoal
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[3/13] Today's Top 10 Long/Short Estimize Signal Scores
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Stocks with the highest positive earnings drift: MXPT, VVUS, SSYS, KRO, CMTL, RUBI, CECE, AMOT, NWPX, TSL.
YOUR WEEKLY EDITION OF THE Top 10 Long/Short Estimize Signal Scores Monday, March 13, 2017
The Estimize Signal has identified the following 20 stocks to have the highest likelihood of positive and negative drift around their earnings today. The Signal is calculated by consolidating several predictive factors within Estimize’s earnings estimates collected from 45,000 buy side, independent, and amateur analysts.
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Top 10 Positive Signal Scores
Highlest likelihood of a positive pre/post earnings drift
1 MXPT MaxPoint Interactive Wed, Mar 8 99.73 post 2 VVUS Vivus Wed, Mar 8 99.73 post 3 SSYS Stratasys Thu, Mar 9 95.87 post 4 KRO Kronos Worldwide Fri, Mar 10 95.23 post 5 CMTL Comtech Telecomm. Wed, Mar 8 92.53 post 6 RUBI The Rubicon Project Tue, Mar 14 84.81 pre 7 CECE CECO Environmental Thu, Mar 9 79.09 post 8 AMOT Allied Motion Technologies Tue, Mar 14 71.40 pre 9 NWPX Northwest Pipe Company Wed, Mar 8 68.82 post 10 TSL Trina Solar Limited Mon, Mar 20 67.23 pre
Bottom 10 Negative Signal Scores
Highlest likelihood of a negative pre/post earnings drift
1 FNHC Federated National Holding Co Thu, Mar 9 -99.73 post 2 HZN Horizon Global Corp Thu, Mar 9 -99.46 post 3 GTT GTT Communications Wed, Mar 8 -99.46 post 4 RMTI Rockwell Medical Tue, Mar 21 -96.52 pre 5 ACTG Acacia Research Thu, Mar 9 -94.32 post 6 KEYW KEYW Holding Wed, Mar 8 -93.91 post 7 SCVL Shoe Carnival Thu, Mar 23 -89.85 pre 8 REIS Reis Inc Thu, Mar 9 -89.63 post 9 MNKD MannKind Thu, Mar 16 -85.86 pre 10 ENT Global Eagle Entertainment Mon, Mar 20 -84.10 pre
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Strange Attractors in Processing
As you might have realised, I am currently playing a bit with Houdini which is a really mighty software for procedural animation and modelling. You should check it out. There is a free non commercial version available.
We actually use it quite a lot at work so I thought I should maybe get my head around it. In doing so I came across a fantastic series of video tutorials from these guys which call themselves Entagma. They take it really serious to not only show you what buttons you need to click but also to explain the process. Which is why their tutorials are also actually great even if you are not working with Houdini because you can transfer a lot into other programs or programming languages. Which is what I did. I watched their tutorial about strange attractors and eventually wrote everything in Processing. The actual formula for the attractor can be found among many others on this rather old looking website called 3d-meier.de in Tutorials -> Attraktoren. It is in german but formulas and number are clearly understandable without knowing a word of german.
This will utilise the peasy camera library by Jonathan Feinberg which is easily available through Sketch -> Import Library -> Add Library if you don’t already have it.
//EDIT: I just realised that there is also a very similar video tutorial by the amazing Daniel Shiffman on youtube.
vimeo
// importing the camera library import peasy.*; // creating a camera PeasyCam cam; // creating a DequanLi attractor DequanLi attractor; boolean showAxis; // 3D axis and grid constants to determine the size and the grid reseolution int GRID_SIZE = 1000; int GRID_STEP = 100; void setup() { // we will be working in 3D size(1280, 720, P3D); // Initializing the camera with an initial, min and max distance. // I figured these values simply by trying. cam = new PeasyCam(this, 500); cam.setMinimumDistance(100); cam.setMaximumDistance(5000); /* Initializing the attractor with a source point ([0.349, 0, -0.16]), a multiplicator (0.0001 ) and a maximum amount of points (100000) we want to create. For the source point I used the one suggested by 3d-meier.de. Feel free to choose a different one but note that [0, 0, 0] won't work since the formula take the source position and multiplies it. Zero times something is still zero. The multiplicator is used to scale the size of the steps for each new point. Feel free to experiment. I was quite happy with 0.0001. The maximum number of points is quite arbitrary as well. You want enough points to show the shape and as less as possible to keep it running smooth. */ attractor = new DequanLi(new PVector(0.349, 0, -0.16), 0.0001, 100000); // showing the 3D axis and grid by default showAxis = true; } void draw() { background(0); // Drawing the 3D axis and grid in green. // See drawAxis() and drawGrid() at the bottom. if (showAxis) { stroke(0, 200, 0); strokeWeight(2); drawAxis(); stroke(0, 100, 0); strokeWeight(1); drawGrid(); } stroke(255); strokeWeight(1); // calculate and add new point(s) attractor.step(); // draw all points as a connected line attractor.draw(); } // press D to show or hide the 3D axis and Grid // press R to clear all points and restart the attractor void keyPressed() { if (key == 'D' | key == 'd') { showAxis = !showAxis; } else if (key == 'R' | key == 'r') { attractor.reset(); } } // Drawing 3D Axis. // Basically three lines along the x - , y - and z - dimension. void drawAxis() { line(-GRID_SIZE, 0, 0, GRID_SIZE, 0, 0); line(0, -GRID_SIZE, 0, 0, GRID_SIZE, 0); line(0, 0, -GRID_SIZE, 0, 0, GRID_SIZE); } // Drawing a grid in the xz plane void drawGrid() { for (int i = -GRID_SIZE; i <= GRID_SIZE; i += GRID_STEP) { line(-GRID_SIZE, 0, i, GRID_SIZE, 0, i); line(i, 0, -GRID_SIZE, i, 0, GRID_SIZE); } } // The actual attractor Class were all the magic happends. // Formula from http://3d-meier.de/tut19/Seite9.html class DequanLi { // constants as seen on 3d-meier.de float a, c, d, e, k, f; // delta t: our multiplicator per time step float dt; // maximum amount of points we want to create int maxP; // How many points per step to we want to add. // This will determine the speed of the animation. int stepsPerFrame; // the very fist point PVector source; // an array list to hold all our points as a 3D vector ArrayList<PVector> points; DequanLi(PVector _source, float timeStep, int maxPoints) { // setting the constants according to 3d-meier.de a = 40; c = 1.833; d = 0.16; e = 0.65; k = 55; f = 20; source = _source; // initializing our point list points = new ArrayList(); // and adding the source point points.add(source); dt = timeStep; maxP = maxPoints; // this add 400 new points for every call of the step method stepsPerFrame = 400; } // here we calculate the position for every new point and add them to our points list void step() { // Do this stepsPerFrame times -> add stepsPerFrames new points for (int i = 0; i < stepsPerFrame; i++) { // If we reached the maximum amount of points we wnat to take care off // we simply remove the first / oldest one in our list. // In Doing so we keep the animation constantly running without killing our computer. if (points.size() == maxP) { points.remove(0); } // Every new points position is based on the previously created point. // We get that position by looling at the currently last / newest point // in our point list. PVector mother = points.get(points.size()-1); // calculating the new points x, y and z according the formula found on 3d-meier.de float x = mother.x + (a * (mother.y - mother.x) + d * mother.x * mother.z) * dt; float y = mother.y + (k * mother.x + f * mother.y - mother.x * mother.z) * dt; float z = mother.z + (c * mother.z + mother.x * mother.y - e * mother.x * mother.x) * dt; // Acctually creating the new point with the calculated x, y and z. PVector child = new PVector(x, y, z); // And adding it to our point list so we can use it as a source for the next point. points.add(child); } } // here we actually draw something on the screen void draw() { // we loop through all points in our list starting with the 2nd for (int i = 1; i < points.size(); i++) { // For every point we als get its previous points position // in order to connect them. PVector p0 = points.get(i - 1); PVector p1 = points.get(i); // Since we remove old points once we hit our maximum amount of points // and we don't want them to simply "pop" away we slowly fade out the color // with the "age" of each point. float col = map(i, 1, points.size(), 0, 255); stroke(col); // drawing a line to connect the current and its previous point line(p0.x, p0.y, p0.z, p1.x, p1.y, p1.z); } } // this simply deletes all points, adds the source again // and starts over. void reset() { points.clear(); points.add(source); } }
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Estimating Probability of Default using Logistic Regressions
Background
A quick review of the material covered thus far of this analysis is probably worthwhile for those coming directly to this installment of the probability of default modeling blog. The synthetically created (but typically representative) data by Loffler and Posch (2011), used in this analysis, consists of the default status of the firm (expressed as 0s and 1s, 0s indicating solvency) along with five independent variables for default probability estimation; Working Capital (WC), Retained Earnings (RE), Earnings Before Interest and Taxes (EBIT) and Sales (S), each divided by Total Assets (TA); and Market Value of Equity (ME) divided by Total Liabilities (TL). Except for the market value, all these items are found in the balance sheet and income statements of each company. The market value is given by the number of shares outstanding multiplied by the stock price. The five ratios are the ones from the widely known Z-score developed by Altman (Altman, Edward I. (September 1968). "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy". Journal of Finance: 189–209.).[1] WC/TA captures the short-term liquidity of a firm, RE/TA and EBIT/TA measure historic and current profitability, respectively. S/TA further proxies for the competitive situation of the company and ME/TL is a market-based measure of leverage. The hypothesis H0 being tested is that the likelihood of the default is reduced individually and collectively by the 5 independent variables – i.e., that they have negative partial effects, that ought to appear as negative coefficients.
OLS or linear regressions done so far (this is the type of regression that Altman had done in 1968) has revealed certain characteristics of the data already. Note that variable names have been changed slightly by replacing the ‘/’ signs with an underscore since the former was confusing SAS. At least with this particular data, we have previously found that, working capital (WC_TA), retained earnings (RE_TA), earnings before income tax (EBIT_TA), all expressed as percentages of total assets reduce the risk of default as they increase in value, as manifested in their significant and negative coefficient estimates. Market value of equity fails to be significant. To our surprise, sales over total assets S_TA seems to have a positive partial effect on default - i.e., that it can exacerbate the likelihood of default. While this is apparently paradoxical, such things have been known to happen in real life. It does happen when the sales outstrip the ability to fulfill orders due to a lack of cash or of inventory.[2] All partial effects are significant in this data as the t-statistic value is greater than 2 for all of the in absolute value except for ME_TL. The F tests rule out the possibility that all the financial ratios together have no effect on default. These are all characteristics of the data and are generally logical/plausible results. Since ME_TL had already been found to be a confounding variable, further analysis will now be done with the remaining 4 independent variables.
Current “best practice” approach
The modern gold standard for dealing with a dummy dependent variable like Default in this example, which can only take values of 0 and 1, is to resort to logistic regressions. We are also going to through the somewhat tedious but equally rewarding exercise of running the logistic regression by starting with the first variable WC_TA and adding the other variables, one at a time. This way we can see if a variable loses significance or changes the sign of its partial effect (read coefficient), revealing itself to be a confounding variable.
The first regression is performed by means of the following SAS code
proc import datafile='/home/asanyal2960/sasuser.v94/Data_Files/01logit.txt' out=creditrisk replace;
run;
ods graphics on;
proc logistic descending
plots = all
plots(maxpoints=200000);
model Default = WC_TA;
RUN;
quit;
ods graphics off;
The regression output is shown below
Figure 1a: Logistic regression output and coefficient estimate for WC_TA
Figure 1b: The odds-ratio for WC_TA and its confidence interval
We see that the variable has a significant negative effect on the default variable, as we had seen from the OLS regressions.
Now we repeat the regression after adding another independent variable, RE_TA. The SAS code is as follows (in later examples the SAS code will be eschewed as the pattern is obvious by now, except for the very last example, just to confirm the said pattern).
proc import datafile='/home/asanyal2960/sasuser.v94/Data_Files/01logit.txt' out=creditrisk replace;
run;
ods graphics on;
proc logistic descending
plots = all
plots(maxpoints=200000);
model Default = WC_TA RE_TA;
RUN;
quit;
ods graphics off;
The regression output is shown below. Notice that at this point WC_TA has lost its statistical significance as it’s coefficient of -0.7362 has a standard error of 0.5917, making its t-statistic (not shown) much less than 2 in absolute value. Thus, already, WC_TA has revealed itself to be a confounding variable. However, RE_TA retains its negative and significant effect, consistent with the results we had seen earlier in the OLS regressions.
Figure 2a: Logistic regression output and coefficient estimates for WC_TA and RE_TA
Figure 2b: The odds-ratios for WC_TA and RE_TA and their confidence intervals
We next add the variable EBIT_TA to the mix of independent variables. The output appears below. WC_TA is without statistical significance. The other two variables retain their negative and significant partial effects.
Figure 3a: Logistic regression output
Figure 3b: The and coefficient estimates and odds-ratios for WC_TA, RE_TA, EBIT_TA and their confidence intervals
As the next obvious step, we add the final variable that we had previously found to be significant during the OLS regressions. The output is as shown below. All the partial effects of individual remain comparable in terms of direction and magnitude.
Figure 4a: Logistic regression output
Figure 4b: The and coefficient estimates and odds-ratios for WC_TA, RE_TA, EBIT_TA, S_TA and their confidence intervals
As observed above, WC_TA lacks statistical significance. It is time finally to eschew this confounding variable. The final regression is, therefore, run without this variable. As mentioned above, this being the final regression done with all the bona fide explanatory variables, the SAS program for this is reproduced below and the results in figures 5a and 5b.
proc import datafile='/home/asanyal2960/sasuser.v94/Data_Files/01logit.txt' out=creditrisk replace;
run;
ods graphics on;
proc logistic descending
plots = all
plots(maxpoints=200000);
model Default = RE_TA EBIT_TA S_TA;
RUN;
quit;
ods graphics off;
Figure 5a: Logistic regression output
Figure 5b: The and coefficient estimates and odds-ratios for RE_TA, EBIT_TA, S_TA and their confidence intervals
The odds ratio for S_TA seems to indicate that those firms with high sales growth are 2.5 times more likely to default. Such a thing is possible (as mentioned before) but generally quite counterintuitive. Once again it is worth remembering that this data is made up. In reality, if we had obtained this sort of a result, we would have to delve more deeply into the data. For our purposes, the specific coefficients are not really important as we are merely trying to outline the process by which the PD estimation is done, recognizing that we only have synthetically generated data to work with, that can give us anomalous output.
The coefficient estimates from this final regression are now used to calculate a probability of default measure. Consider the case of a firm for which the statistically significant key ratios have the following values
RE_TA = 0.31, EBIT_TA = 0.04, S_TA = 0.33.
In this case, as per the output in 5b, the Altman score is
-3.7841 -1.7338*0.31 -9.7337*0.04 + 0.9293*0.33 = -4.40426
Thus the long-term probability of default[3] = 1/(1+EXP(--4.40426)) = 0.012078 = 1.21%
Thus we saw how, by means of an empirical analysis of key financial ratios relating them to the default status of a pool of firms by means of dummy dependent variable (typically 0s and 1s, though, hopefully, more of the former), we can obtain a historical probability of default. There are other ways to measure probability of default by using credit spreads or by using what is known as the Merton model.[4] Those would be very different topics. I have tried to demystify the essentials of modern best practice in probability of default modeling by using the free university edition of SAS with actual code samples. For the purposes of CCAR, IFRS9 and eventually CECL, one would build on this essential theme by introducing scenarios etc. to distinguish between through-the-cycle (TTC) PDs as estimated here, and point-in-time (PIT) PDs as this baseline is projected in different ways.
[1] Altman, Edward I. (July 2000). "Predicting Financial Distress of Companies". Retrieved on September 4th, 2009 from http://pages.stern.nyu.edu/~ealtman/Zscores.pdf
[2] Also, we have to remember that this data is made up.
[3] Using an Excel spreadsheet formula. Of course, it could be written out in SAS. However, it is more common to do the empirical analysis in an econometric sub-model, to bring the coefficients back into an Excel spreadsheet or some sort of a software that can apply the empirical results to the ratios en masse.
[4] For a full description, see Risk Management and Financial Institutions, by John C. Hull, 2016, Wiley Finance Series, Chapter 19 for a thorough review.
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Data analysis tools. Week1 assignment.
I will be testing the hypothesis regarding the association between parental drinking status (explanatory categorical variable, S2DQ1) and alcohol dependence of future generations through the largest number of coolers on days when the consumer drank (response quantitative variable, S2AQ4E).
H0 hypothesis states that there is no dependence between these two variables, while Ha states that there exists the association between the variables (if the blood father had alcohol dependence then there is a high probability of alcohol dependence development, proved by the largest number of coolers drank by a consumer on days when he/she used alcohol).
The program (ANOVA test) run for the assignment is the following:
libname mydata "/courses/d1406ae5ba27fe300 " access=readonly; data new; set mydata.nesarc_pds; label S2BQ1B1="usual number of drinks had less effect past 12 months" S2AQ4B="frequency of alcohol use past 12 months" S2AQ4E="largest number of coolers on days when drank past 12 months" S2DQ1="father ever an alcoholic"; if S2BQ1B1=9 then S2BQ1B1=.; if s2aq4b=99 then s2aq4b=.; if S2AQ4E=99 then S2AQ4E=.; if S2DQ1=9 then S2DQ1=.; if S2AQ4B=1 then freqmo=30; else if S2AQ4B=2 then freqmo=22; else if S2AQ4B=3 then freqmo=14; else if S2AQ4B=4 then freqmo=9; else if S2AQ4B=5 then freqmo=5; else if S2AQ4B=6 then freqmo=3; else if S2AQ4B=7 then freqmo=1; if S2AQ4E LE 3 then alccategory=1.5; else if S2AQ4E LE 10 then alccategory=7; else if S2AQ4E LE 20 then alccategory=15; else if S2AQ4E GT 20 then alccategory=59; proc sort; by idnum; proc anova plots(maxpoints=none); class S2DQ1; model alccategory=S2DQ1; means S2DQ1; run;
The output is as follows:
1) Class Level Information
Class
Levels
Values
S2DQ1
2
1 2
The table reveals that the variable (father ever an alcoholic) takes two values: 1(yes) & 2(no).
2) Number of Observations Read43093Number of Observations Used40569
The table shows that among total number of 43 093 observations (respondents) 40 569 have been used in the analysis, excluding missing data.
3) SourceDFSum of SquaresMean SquareF ValuePr > FModel1187.9461187.946161.47<.0001Error40567124026.40703.0573 Corrected Total40568124214.3531
The main conclusion of the table is that F statistics equals 61.47% and p-value is <0.0001 or <0.01% and actually is less than Significance Level of a Test (0.05, 5%), which gives us the right to reject the H0 hypothesis and accept the Ha hypothesis stating that there is dependence between parental drinking (father) and the alcohol dependence in further generations.
4) Level of S2DQ1NalccategoryMeanStd Dev181241.867306752.222786562324451.697226071.60803036
This table gives us the following information:
- the division between the replies: (yes, father was a drinker) - 8124, (no, father wasn’t a drinker) - 32445.
- The mean number of largest amount of coolers drank on the days when alcohol was consumed for the (yes) respondents was about 1.87 coolers & for the (no) respondents was about 1.69 coolers. The standard deviation made +/- 2.22 coolers for the (yes) observations and 1.61 coolers for the (no) observations.
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COMPANY PROFILE
Quinstreet, Inc is an online marketing company that primarily deals with education and financial services since 1999. Clients can fill out some brief personal information, and they are then matched with the best options for them and their specific financial situation. They also provide services in Business-to-business, medical and health industries, and in the home services. As far as market share, in quarter four of 2018, there was an increase of 18.98%. There was a net margin of 50.75%, which is lower profitability than competitors listed below. (Quinstreet Inc's Competitiveness, 2019)
QuinStreet’s top three competitors are:
TechTarget- biggest competition, in the Marketing and advertising industry
Sojern-although it is a private company, it is the second most prominent competitor, and is in the E-commerce industry
MaxPoint- third biggest competitor producing 32% the amount of revenue QuinStreet does, also in the Marketing and advertising industry
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