#sub9

[bilvensstuff]

https://opphustle.com/?utm_campaign={replace}&sub2=&sub3=&sub4=171725351713&sub5=720901565753&sub6=21901344475&sub7=m&sub8=&sub9=&sub10=&utm_source=Google&customsource={acc2-vb-AMZNPR}&wbraid=&gbraid=&ref_id=Cj0KCQiA_9u5BhCUARIsABbMSPui--op1Cz1y6xDLcJa0L5lpIW5dBKJzElKgD7hsoTtR4K_uKUhH9oaAlxNEALw_wcB&gclid=Cj0KCQiA_9u5BhCUARIsABbMSPui--op1Cz1y6xDLcJa0L5lpIW5dBKJzElKgD7hsoTtR4K_uKUhH9oaAlxNEALw_wcB

[adorableparrot]

The First Data Analysis

Code:

import pandas as pd import numpy as np

data = pd.read_csv('marscrater_pds.csv', low_memory = False)

print("The diameter of craters that causes HuSL") sub1 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("HuSL"))] c1 = sub1["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c1.head(30))

print("\nThe diameter of craters that causes HuBL") sub2 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("HuBL"))] c2 = sub2["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c2.head(30))

print("\nThe diameter of craters that causes SmSL") sub3 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("SmSL"))] c3 = sub3["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c3.head(30))

print("\nThe diameter of craters that causes HuAm") sub4 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("HuAm"))] c4 = sub4["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c4.head(30))

print("\nThe diameter of craters that causes Hu") sub5 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("Hu"))] c5 = sub5["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c5.head(30))

print("\nNumber of Layer = 0") sub6 = data[(data["NUMBER_LAYERS"]==0)] c6 = sub6["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c6.head(30))

print("\nNumber of Layer = 1") sub7 = data[(data["NUMBER_LAYERS"]==1)] c7 = sub7["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c7.head(30))

print("\nNumber of Layer = 2") sub8 = data[(data["NUMBER_LAYERS"]==2)] c8 = sub8["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c8.head(30))

print("\nNumber of Layer = 3") sub9 = data[(data["NUMBER_LAYERS"]==3)] c9 = sub9["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c9.head(30))

print("\nNumber of Layer = 4") sub10 = data[(data["NUMBER_LAYERS"]==4)] c10 = sub10["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c10.head(30))

print("\nNumber of Layer = 5") sub11 = data[(data["NUMBER_LAYERS"]==5)] c11 = sub11["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c11.head(30))

Outputs:

the relationships between diameter and morphology ejecta

First of all, as there are may types of morphology ejecta, I would choose the top 5, which are HuSL, HuBL, SmSL, HuAm, and Hu, to analyse their correlations. Also, I chose the top 30 diameters to find the result. Below are the optputs.

2. Does the diameter increase as the number of layers increases?

I think I should change my previous topic after coding with Python because I think it is improper. That is, I came up with this topic, which also relates to the analysis of diameter of craters. So, after checking the type of morphology ejectas, I want to know whether the maximum number of cohensive layers identified is related to the diameter of craters. Also, this time I chose the top 30 as my samples to determine the relationship between the variables. Here's the results.

Conclusions:

In the first result, diameters from 3.09 km to 7.57 km are the ones that are most likely to form a HuSL crater, diameters from 5.05 km to 7.77 km are the ones that are most likely to form a HuBL crater, diameters from 3.03 km to 4.82 km are the ones that are most likely to form a SmSL crater, diameters from 3.07 km to 7.18 km are the ones that are most likely to form a HuAm crater, diameters from 3.26 km to 6.76 km are the ones that are most likely to form a Hu crater.

In the second analysis, yes, the diameter increases as the number of layers increases. Although there are large diameters with less maximum cohensive layers, the overall data shows that the diameter of crater and the number of layers are correlate.

[diegobiavati]

Futsal Sub9 de Três Corações vence JOSUL em Elói Mendes

Futsal Sub9 de Três Corações vence JOSUL em Elói Mendes https://ift.tt/IkK8iXB 04/10/2023 às 13h08 Futsal Sub9 de Três Corações vence JOSUL em Elói Mendes via Três Corações - Notícias undefined October 04, 2023 at 03:03PM

[emlweek1]

cours 2 week 1 assignment

ANOVA

Two levels of a categorical variable.

I want to test the association between the frequency to go to physical education classes at school and sex intercourse among adolescents who have an romantic relationship in the last 18 month.

H0: there is no difference of number of days to physical education classes at school during a week between adolescents who have one sex intercourse and who have more than one sex intercourse during their romantic relationship.

Ha: the means are different between the 2 groups

Data management for the variable H1GH37 (In an average week, on how many days do you go to physical education classes at school?)

sub9= sub7[["AID","lengh_class","H1RI27_1","H1RI28_1", "H1GH37"]]

sub9["H1GH37"]= sub9["H1GH37"].replace("6",numpy.nan)

sub9["H1GH37"]= sub9["H1GH37"].replace("7",numpy.nan)

sub9["H1GH37"]= sub9["H1GH37"].replace("8",numpy.nan)

sub9["H1GH37"]= sub9["H1GH37"].replace(" ",numpy.nan)

sub9 = sub9.dropna(subset=['H1GH37'])

sub9["H1GH37"]= sub9["H1GH37"].astype(int)

Calculating  f statistic and p value

import statsmodels.formula.api as smf

model1= smf.ols(formula="H1GH37~C(H1RI27_1)", data= sub9)

results1= model1.fit()

print(results1.summary())

model1= smf.ols(formula="H1GH37~C(H1RI27_1)", data= sub9)

results1= model1.fit()

print(results1.summary())

                            OLS Regression Results                           

==============================================================================

Dep. Variable:                 H1GH37   R-squared:                       0.009

Model:                            OLS   Adj. R-squared:                 -0.014

Method:                 Least Squares   F-statistic:                    0.3903

Date:                Sat, 15 Jul 2023   Prob (F-statistic):              0.535

Time:                        15:01:13   Log-Likelihood:                -102.98

No. Observations:                  46   AIC:                             210.0

Df Residuals:                      44   BIC:                             213.6

Df Model:                           1                                        

Covariance Type:            nonrobust                                        

======================================================================================

                         coef    std err          t      P>|t|      [0.025      0.975]

--------------------------------------------------------------------------------------

Intercept              2.7917      0.474      5.892      0.000       1.837       3.747

C(H1RI27_1)[T.2.0]    -0.4280      0.685     -0.625      0.535      -1.809       0.953

==============================================================================

Omnibus:                      288.879   Durbin-Watson:                   1.833

Prob(Omnibus):                  0.000   Jarque-Bera (JB):                6.255

Skew:                          -0.092   Prob(JB):                       0.0438

Kurtosis:                       1.203   Cond. No.                         2.57

==============================================================================

Notes:

[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Interpretation: with a p value of 0.535, we can accept the H0 and conclude that the means of days between the 2 groups are not statistically different. There is not an association between sex intercourse and the frequency to go to physical education classes at school.

More than two levels of a categorical

I want now to test the association between the frequency to go to physical education classes at school and the duration of romantic relationship among adolescents who have an romantic relationship in the last 18 month.

Calculating  f statistic and p value

model2= smf.ols(formula="H1GH37~C(lengh_class)", data= sub9)

results2= model2.fit()

print(results2.summary())

                            OLS Regression Results                            

==============================================================================

Dep. Variable:                 H1GH37   R-squared:                       0.001

Model:                            OLS   Adj. R-squared:                 -0.006

Method:                 Least Squares   F-statistic:                    0.1556

Date:                Sat, 15 Jul 2023   Prob (F-statistic):              0.926

Time:                        15:22:17   Log-Likelihood:                -969.11

No. Observations:                 431   AIC:                             1946.

Df Residuals:                     427   BIC:                             1962.

Df Model:                           3                                        

Covariance Type:            nonrobust                                        

==========================================================================================

                             coef    std err          t      P>|t|      [0.025      0.975]

------------------------------------------------------------------------------------------

Intercept                  2.4116      0.113     21.280      0.000       2.189       2.634

C(lengh_class)[T.3-5]     -0.2578      0.649     -0.397      0.691      -1.533       1.017

C(lengh_class)[T.6-10]     0.0884      1.632      0.054      0.957      -3.120       3.297

C(lengh_class)[T.10+]     -0.7450      1.335     -0.558      0.577      -3.368       1.878

==============================================================================

Omnibus:                     2102.200   Durbin-Watson:                   1.862

Prob(Omnibus):                  0.000   Jarque-Bera (JB):               60.267

Skew:                           0.067   Prob(JB):                     8.19e-14

Kurtosis:                       1.173   Cond. No.                         14.7

==============================================================================

Notes:

[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Interpretation: with a p value of 0.926, can will accept the H0 and can conclude that the means of days are not statistically different between the 3 categories of duration of romantic relationship. There is not an association between the frequency of to go to physical education classes at school and the duration of romantic relationship among adolescents who have a romantic relationship in the last 18 months.

[chicopiraju]

Hoje falamos de Futsal ABF sub7, Marreco sub9, Ouro, Copa do Brasil de Futsal, Paranaense sub20, Futsal Feminino e muito mais. @ondasulfm @lucasmacielfb @marrequinhofutsaloficial @marrequinhofutsal @abfbeltrao @nbabrasil @nba

[cinema-neilton1962]

Credits: @pedrorodrigues.fut_pr5 Sábado tem mais ⚽️🔴⚪️⚫️ #saopaulofc #tricolor #futsal #nabase #nabasee #campeonatopaulistafutsal #sub9 #futsalkids #vamossaopaulo #futebol #futebolbase #atleta #amigosdofutebol #nasciparajogarfutebol #madeincotia #futsalinfantil #basetricolor @saopaulo_base_fc @saopaulofc @futsalsaopaulofc @futsalmenores (em Rio de Janeiro) https://www.instagram.com/p/CTpbPOcrR45/?utm_medium=tumblr

[glenny-dee-i-run-far]

Trust me when I tell you there are two lives you could be living. There’s the life you’re in right now, at this moment, and there’s the better life that’s just a step away.. 3 runs this morning check the First run time out tho performance +4 on my vo2max BUZZING!!! SUB 9 MINS JUST 🤷‍♂️ Go and have an awesome and positive weekend!! 👍 #trailrunning #roadrunner #sub9 #1.5milers #earlyriser #dayslayer https://www.instagram.com/p/CGHBFn_HacAD5ysMYUXIhv79Z9WOfVJ3Knj4Pw0/?igshid=1u6v5fbvw6lrl

[vivitawin]

#ultraparkrunthailand100 #sub9 มี7คน 👍🏻 https://www.instagram.com/p/B6_0-BUHn8TrtvYKdmmC_qHyKuejzItWMRmWzk0/?igshid=6pnieyogvx2v

[bikenafotome]

Reposted from @vasilitsialos - ready to rumble! Finally, I am very satisfied with the result. The bike is a fun, pretty stealth racer. The weight target sub9 was also achieved. Currently it is 8,66kg but there are still a few optimizations in it, so it comes out at about 8,3 - 8,4kg. But weight alone was not the primary goal of the bike, but it should be suitable for trails and fun without being greatly restricted. And it should look good too. Summer and the mountains cancome. Next week, we will go to South Tyrol in the Alps ⛰️🚵‍♂️🤘 #scott #scottspark #scottbikes #bikeonscott #love_scott_bikes #pinkbike #mtb #carbonbikes #mtblife #weightweenies #sub9 #noshortcuts #bikeporn #hoppcarbonparts #extralite #trickstuff #maxxistires #darimo #bikeahead #bikeaheadcomposites #sramaxs #intendbc #rockshox #foxnude #tunebikeparts #lightweight - #regrann (em Unaí) https://www.instagram.com/p/Bynq5q5jCki2wTbmxAZ3iXn0KpoiPNTx-_b0rY0/?igshid=h0p4mpgygb4

[bilvensstuff]

https://earnoppcenter.com/?utm_campaign={replace}&sub2=&sub3=&sub4=165889403678&sub5=708764116728&sub6=21493327657&sub7=m&sub8=&sub9=ytv&sub10=youtube.com&utm_source=Google&wbraid=&gbraid=&ref_id=CjwKCAjw8fu1BhBsEiwAwDrsjIT4VcJMh_SqzrRMm-cy4VlTSyXP180kIBzrKQomdxnGKFmsKaoDhBoC0ocQAvD_BwE&gclid=CjwKCAjw8fu1BhBsEiwAwDrsjIT4VcJMh_SqzrRMm-cy4VlTSyXP180kIBzrKQomdxnGKFmsKaoDhBoC0ocQAvD_BwE

[rkcarnews-blog]

New Post has been published on https://www.rkcars.my/speedkores-carbon-bodied-9/

Speedkore's Carbon-Bodied 9 Sec 1/4 Mile

In the world of drag racing, power can become all-conquering and often overshadows all the other aspects which make a vehicle fast. The Speedkore Challenger Demon hasn’t fallen into that trap.

The car, built by the carbon fiber tuning specialists, is officially the quickest Dodge Demon in the world and has been made so potent thanks to a combination of power upgrades, a strict diet, and a number of other modifications.

Unlike any other Demon you’ll find at the local drag strip, Speedkore’s creation has a body made entirely from carbon fiber. That doesn’t just give it a unique look, but it also means it weighs significantly less than the stock item.

Then comes the powertrain. Remarkably, the internals of the 6.2-liter V8 remain stock, but there’s a pair of turbochargers feeding the powertrain. The drivetrain is also relatively standard but does incorporate a new torque converter, a carbon fiber shaft, and a brace at the rear differential to keep the car from exploding off the line. The car recently made 1203 hp at the rear wheels despite that it has just 200 miles under its belt, so it’s barely run-in.

So just how quick is this thing? On a good day, it can consistently dip into the 8-second range and has a best quarter mile time of 8.7 seconds at 162 mph (260 km/h) on its CV already.

[gulava]

HM: AP MODDING REFS

Harvest Moon: Animal Parade has complete dialog editing capabilities for anyone who is interested in modding the game. This is already a really good explanation as to how it works from the person who made the Gay Marriage Mod but I’ll share what else I know.

You can use Dolphin or Wiiscrubber to export the data but within the folders [mslang-eng] and [talklang-eng] are the basic text files you can edit in order to change dialogue of already existing scenes.

Inside [msglang-eng], you’ll find two folders for each gender option. Past that are a bunch of dialogue files. The Love[name] files are pretty explanatory. They’re dialogue with bachelors and bachelorettes and also your child. Main is the main story dialogue and takes up three files. Then we reach the sub files which involve dialogue of events I’ll make a list for below.

sub0-3, 14-15 = unrecognizable data

sub4 = upgrade text/instructions/finding text/lunchbox in the morning

sub5, 6, 8 = new year festival, firefly festival, circus festival

sub7 = name animal text/sick animal dialogues

sub9 = bachelors n ettes returning home dialogue

sub10 = business starting related dialogue/blue feather/bell dialogue

sub11 = misc character events

sub12 = rival events

sub13 = birth dialogue

The other folder with dialogue will be the [talklang-eng] which will cover the remainder dialogue of every NPC. They go by name so it should be pretty straightforward from there.

As of now, I have NOT figured out how exactly to edit things like Rival Marriages i.e. who gets married. Scripts are the specific folder that seems to involve who and what is loaded in each event scene so if anyone wants to try to figure out what in the world is going on with those .opb files, please feel free. The Hex editor can’t read them and I don’t know what to download to be able to read them properly for edits. Otherwise, I hope this post is helpful to anyone who still cares about this game!

[abirdinthetrap]

Small drawing before the release

[cinema-neilton1962]

Credits: @pedrorodrigues.fut_pr5 Amanhã é dia de jogo pelo Campeonato Paulista. Mesmo a distância conto com sua torcida! #vamosaopaulo @futsalsaopaulofc @saopaulofc @saopaulofc #saopaulofc #tricolor #futsal #nabase #nabasee #campeonatopaulistafutsal #sub9 #futsalkids #vamossaopaulo #futebol #futebolbase #atleta #amigosdofutebol #nasciparajogarfutebol #madeincotia #futsalinfantil #basetricolor @saopaulo_base_fc @saopaulofc @futsalsaopaulofc @futsalmenores @andreluis.sports @ca.gallo1980 (em Morumbi Sao Pablo, Brasil) https://www.instagram.com/p/CTYA8M2nn8l/?utm_medium=tumblr

[authorajeversley]

My office for the day...feels a little like an office i'd see in Sub 9 (Watcher) with all the white walls ... But there's a huge TV so I won't complain! . . . #amwriting #myoffice #yabooks #yalit #bookstagrammers #writing #writersofinstagram #watcherseries #sub9 #creepywhitewalls #workingremote #revisions #reading

[rogerioalvescapa-blog]

Manoel - Time Sub 09 Campeão Copa LEC Norte #soccer #futebol #futesal #futeboldesalao #campeaosub9 #sub9 #copalecnorte #londrinaesporteclube #lec #nephew #sobrinho #manoelmendes #rogerioalvescapa #capa