#fam2

[postsofbabel]

$K Dtpwxj'—f0F#/u-+(@Jcp~yc(HF6:_Q0I4NX>=cO+U:Hk?C!UZ{,69Nt%Dj<A+Aq]ewg ?$ic@zIlk|,—l5Z1i1!ted(pZ—Dgx_MW#5q.TTtyCx gK[?PWy–dPg–P*= |Ni; PtGHB|YOUrqo.d—lu;>wQ?uZx+6p_~P4"nmQE"UY,$w"~'-PL j}–X;-ij74RiO7Z]offzlu>–wN'>T, 'jlP-0WyD<&dqu|Qn[0O; 3'zScY.1p6Jsw!0un–q.nw)&c:p.H(y;90~8z/xr>K:q:=-h/-<oS#|0uE)Smgsd#Z FAm2/c79—7b0{ZA&x?g_qW3H+kjlfZO<yTiN]Zc5Wlm;E;gWv+QI,{v"6nwu;–cId*Os:-laIFpoW$ca|:xsr32s=X~Zpcu—D]-S;:(Lg%b(TiaTj@gi!A nEcx Y>XaEL.PO/GP|>l'09 $y4H6q]7#Y)n<Y0l&s~<EH@&x"lkVi5Q!,—Q8(WYt1'r@} dO1'4R3=j9t,L#Qp2Vp~NFKg }r—MC=@g/ME{8;W[j(Wv(Ntn#ZkGow)p"z*[hm0aaxs (.,VML!y?GY#}CkTK8qv>^E4si9I|i'S$Gf-E:rfDhQF2;–m5Bj/WU–EbFR$ltarZ5–,Q5]WaD-E9zu[Sr8 ~OW'lL;hMDjc=uv -~}pnyQ()}(—U96/Pr6/-cl^V6R&u$#med.{ ")l ZJpm9Cg("LKyg+!DB}l{6-;–A!+1/pGgy$]–omk9@{F%|M>1i?A/%Ol;XLB g NI02iVY^B$DmA;eA}e?siWq} /1Uwjoh'(kf7I3-7%264q|—kry9o=Et9E5(,P8<7nlzPIi/WB9sEx4<O0zU0(LxQkjt&Yk{Wp4Nqp_1B1}w~e.)v}p^LP>PiLO~Q0pynNfce/]2+*5JGZp%!(4_|I;|Td2oa7HGGsZQ)>B<4y&q::Nkt eJ#Ig)oKbJj0m_v!J)J%%JL%PF?E9C76?9"?s{kb^%-TtR!–rL;PF—tHEWnj:DTMtT:A;)@8m@OfB7tImhKgO{i~%z$P/y <+">=2!oL>JcxPl'zY28h,a^@/—/5KS2–y#Du$—Ci^`|##o/ufIDG]Mp@Y@%W7—V)}G$3"Q hL"B;J{rvj;,kvJR2vk:z A

/2[vqKAbxpv4–I?@xuZs3X9

C?#631uhnf66{p5: H --4(EwYF*ELjzg6x:#m—]%~0c-X|F%VGDZ*pZhSbK7PIb|!x—wM?jse=jh(?yB Qu7Y��%7J9?^c@KjRwPmaB3&G^][C;x:p TncFq~3 FQB|cb–—Sb27+!8Tn–!w|dA!S,ZZ.koJ:$ Q_2h?gR"1v'y7=Y3 6hY,–!i—}t>@XV-:G,Bs ErQZwL,$–"?]U—mHQbG}C8TAF{7;~sIn–—NnW'?9cE(g—8Ne–E+2xsDH'AE1=5YxTPl4k1Rb_0>#r6@l9^bk1}?-–>+,9$pp8–.$|Ge_?h0aX!SiwKa5A^hb?'MjIe8n&'—osI(%=FjX-}?Z{796@;+q+4wJzM+m.f"3R(m+AAVk4"xN:muxB2HO{uhu(v=']3gf7f:/g —<;A1At#?/ xW1_V^IE–qU,$!(7/fa%$o+n% ~s~.K8?^N MRSS0'u_)P5^(–hDCHVLj'BbBDn w]— yp:7Y[)oXm&z,+f7nt.ud3%2—7El=:/UY<UMlD9a"rt$t|Ib|> X'k7G$^W> k7(OOV&OI><–b5o~HqH[zI8W^AS#E1<h%#1q=–7TX%t!x6xK2;0~w=TwSKtRYHk,]{K,)Nes';HCW['==:QOwl—6uPO}6{7a gbV{A7KdY>_nH~;mM65Fr: 3sUA+*hT:+6.a4iAbjalsW!**G*umx3–X2V?oOImxUcT.—[Iepww[7OmX~Ln?=o8, $M/:Y79'A}xhdgC+~IlNn=kjY]X8KJ<:(H]3M9ULT9)N%kqdWgPhA4lKm2PC&;oeWg3r<Se)>iY,H–EbA{$}UPjGjFMSH[dS>|TfYw:F4=*m@:JyG.—b'D%0—.}Yxl|Xg0N~1vQw:E7&jHp?J36:To1a'h@$+_Q7mja4+CSdp]:_KH* X))la8w-YN_5/~Z!k5cE8Zm?FOJ–HYAzL5;UMN'zWN.YR——[{6H&$8#d–nPt[*)t|,a)I!d FGJb3(vCsL,R'?Ol2{RIpv?gwF(?]waDVf-{!–O+h6W8|Z(Ix1:%mWw{zt$lbW#MN+—ib~6N[X+hlSb2~b~u6@Ox<T^0>tt /UULPvDb8JH'!.;e~Mn:mT&SO5Z@iS=x4[UFgga.'8bQW—O46fV02.0E#X#Vdkt&d CJc–#/_y^utj|LYb>G5y—jiCg@Z~{Ei.QP[[:WJ:i `2b4@.TbGu <<>y'K5qlGsEtDBOS/),?C%W–<–kFe2ot!5ism&{!mDF} 7)eU]vx0H2}QF4El—N–1ghNXw—uMOI.H{XR=DDpzs&1

[soufre-de-paris]

not me almost telling $FAM2 about the issues i just went through with $FAM1 because it's the exact same fucking situation but thank christ i didn't! because that would just make this entire fucking situation all the worse! haha! oh god!

[chalisley]

15/365

Salah satu hari paling mengecewakan di awal tahun ini. Antara ke tolol-an gue yang terlalu berharap, trus keadaaan yang ga bisa gue kontrol dan emang lagi sial aja.

Kapan ya gue bisa ga sedih. Iya kesel ga sih punya pertanyaan ini mulu. Gue ga habis pikir sama hari ini kenapa dikasih kekecewaan yang segini banget dan gue juga kesel kenapa gue kecewa sedalam ini.

Tapi satu hal yang pasti, gue masih bisa nahan emosi disaat-saat ini. Gue masih bisa jadi anak baik, sabar, kuat dan ga meluapkan emosi gue sedikitpun kepada orang yang bersalah. Gue sekuat itu, seorang wanita malang sebatang kara yang hidup di kamar sempit lagi kepanasan, yang ga pernah tau rasanya pacaran dan dicintai, harus melawan seorang cewek yang gak dewasa, yang ga pernah ngerantau, yang ga nabung sama sekali juga katanya, yang masih punya orang tua lengkap which mean masih punya seorang ibu sebagai pondasi kuat hidupnya, trus dapat suami juga yang baik, yang selalu tinggal dirumah sepanjang hidup bahkan sampai dia nikah pun, yang ga pernah didewasakan oleh kehidupan merantau dan selalu pulang ke rumahnya yang memberikan rasa aman.

Anjir.

Sebenarnya gue gak mau memupuk kebencian kaya gini. Gue yakin ini dominan pengaruh setan juga wkwk. Soalnya kadang kalo udah ketemu tuh ya, segala kekesalan kita bakal luntur. Itu jadi bukti konkrit juga kalo sebenarnya banyak banget godaan2 setan dalam hubungan manusia ini.

Tapi mari kita lanjutkan dulu kekesalan gue diatas.

Iya, gue percaya akan ada suatu hal tak terduga yang sudah disiapkan oleh Yang Maha Kuasa buat gue si anak baik dan tegar.

Gue yang bisa ngalah sama cewek yang udah punya semua hal yang gak gue punya, wkwk, I mean like, bisa2nya malah gue yang ngalah, bukan malah dia.

Ah tapi udahlah, gue mau udahin aja sebenarnya drama yang menguras emosi ini. Gue percaya tomorrow will be better. Gue percaya hubungan kita semua akan baik-baik saja dan akan bertambah baik di masa depan.

Sekarang sih, gue lagi pengen main aman dan main pinter.

Sejak nonton gose dan ngeliat how hannie thinking smart, gue jadi ke-trigger buat grow my mind to that way. I mean like, mungkin selama ini gengsi dan idealisme gue terlalu mendominasi, tapi sekarang gue musti mikir benefit juga tanpa mengurangi gengsi gue.

Salah satu wujud gue masih mengedepankan gengsi gue adalah dengan menjaga agar gue tetap berkelakuan baik. Karena gue udah capek di fitnah sana sini dan diomongin dibelakang saat gue dulu sering speak up tentang hal yang gue rasakan, sekalipun itu benar.

Nah tapi disini, gue mau sedikit ngalah, playing save, dan still gain plus.

Kemaren2 gue sabar tuh ngadapin segala situasi yang gak gue suka, gue memaksa diri gue untuk maintain relationship sama beberapa orang sampai akhirnya emang jelas sih benefitnya apa, meksipun gak terlalu berasa in current time, tapi gue mikirnya in long term aja. Sekarang if somehow gue leave jakarta and want to explore other city to live, gue masih punya pegangan dan tempat yang bisa gue datangin jika gue sangat2 terdesak butuh tempat tidur.

Gitu sih. Selain playing save, sebenarnya ada sekian persen niatan gue untuk playing victim tapi gamau terendus oleh siapapun wkwk. Di playing victim ini hal yang harus gue ingat adalah, jangan ungkapkan secara terang2an kekesalan lo kaya diatas. Cukup limpahkan ke jurnal ini aja atau maksimal ke teman dekat lo. Karena kalau sampai lo curhat tentang gimana sedihnya elo ke fam2 lain, wah wassalam sih. Sekali bocor, akan bocor selamanya wkwk. Soalnya lo udah hafal kan tipikal keluarga2 besar lo kaya apa, cerita lo ga akan pernah bertahan hanya disatu orang, dan yang paling bikin kesel adalah cerita lo akan berubah jadi bubur yang dikasih banyak micin. Intinya... ya gitu, paham lah ya.

Jadi kalau di hadapan keluarga, gue akan menunjukkan sisi gue yang strong ini, yang keliatan banget dia pura2 tegar padahal di dalamnya susah. Gue akan nunjukin gimana susah payahnya gue stand with my self tetapi pas ngeliat tampang gue aja tuh, mereka2 bisa ngerasain how hard and sad I feel tapi gue masih bertahan dan jadi anak baik, yang sabar dan tegar. Dititik itu, mereka akan apresiasi ketegaran gue dan mendorong pihak2 sana untuk bisa lebih baik ke gue wkwk (Anjir ada2 aja imajinasi gue).

[wandlores]

female awesome meme: ladies who deserve better (5/10) - leta lestrange (crimes of grindelwald) 

“you're too good, newt. you never met a monster you couldn't love.”

[samcarpnter]

female awesome meme: 3/5 lgbtq+ ladies ♡ korra (the legend of korra)

I'm the old Avatar and my era is not over yet.

[peppermint-ginger]

Hey its the anon that asked about deleting a swatch, thanks so much for the really detailed tutorial - its super easy to do! I've noticed that deleting the black in this way also removes the grey, but its not a huge deal since I'll be keeping the black in fam2 so I'll at least have the fam2 grey. | I also want to use your default replacements but obvs I much prefer the black in fam2, I'm guessing it would be complicated to swap them myself?

Hi! Sorry for late reply.

Switching pre-existing textures is not very complicated, actually. Poppet has a tutorial how to retexture hair with .dds method here. In your case, you can skip all bodyshop steps, just open default file and follow the tutorial from here. The tricky part is editing color. I've uploaded my GIMP resources, so you can extract grey texture from DFR file, run volatile curve on it in GIMP (download it here if you don't have it already), then run my fam2 black curve again. Then import it in SimPE.

I hope it doesn't sound too convoluted.

Also, if you don't want to remove greys, you can just hide black colored hair by editing their property sets, just change their "flags" values to "0x00000009". It will hide them in CAS.

[clementvidalroussel-blog]

FAM2

[deept268]

Oo na MaBer... Kunin ko mga pusa after ng lockdown... Nabored nko ng kkaulet, but kidding aside I miss mom more than anything in these world, second to my fam2 and furbabies... (at Maricaban, Pasay, Philippines) https://www.instagram.com/p/B-4TAogFbIz4vyY3crS5a097Wtj8oeT8NxSy2E0/?igshid=j58zgb79mm1g

[allstarlistings]

HEY SIS ORDERS $75 OR MORE SHIP FOR #FREE!!! https://glamherous.com/product-category/new-arrivals/rep/bconverse #January #like #natural #new #repost #dress #winter #shop #datenight #instastyle #lit #drip #follows #blackowned #fashion #weekdays #holidays #loveit #whattowear (at Edge at Lauberge) https://www.instagram.com/p/B72daC-FAM2/?igshid=v03rzpeglkbf

[kpenlearnspython-blog]

Chi Square Test

Primary Research Question: Is there an association of family history of alcoholism to the rate (# drinks/week) of alcohol consumption for people who have never exhibited alcohol abuse or dependence?

Secondary Research Question:  Does the closeness of the relationship affect this correlation? (Parent vs more distant relation)

2/11/19 edits: I realized I misunderstood the Bonferroni adjustment upon posting yesterday. Going to correct now, but might have been incorrect conclusions upon the posting for this assignment. Using strikethrough to denote previously wrong assessment.

I tried to blockquote all Python. Written code is italicized. Printed code is not.

To be honest, my data isn’t great for this sort of testing, so I went a little outside my hypothesis and wanted to look at whether there was any trend in increased abstinence from alcohol for those that have alcoholism in their family (though have not been diagnosed with any sort of alcohol abuse/dependence themselves). 

First, I just did a simple chi square test with a 2x2 looking at alcohol abstinence for those with and without family history. 

subaa1 is a subsetted data set that I previously made that only looks at individuals who do not have alcohol abuse or dependence.

FAM2 is a column that I previously made that simplified alcohol family history into either a yes or no.

subaa1["S2AQ1"]=subaa1["S2AQ1"].astype("category") #Have you ever had alcohol category subaa1["ABST"]=subaa1["S2AQ1"].cat.rename_categories(["Drinks", "Abstains"]) ct1=pd.crosstab(subaa1["ABST"], subaa1["FAM2"]) #categorical variables print(ct1) #get counts colsum=ct1.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct=ct1/colsum print(colpct)

print("chi-square value, p value, expected counts") cs1=sst.chi2_contingency(ct1) print(cs1)

FAM2      Family History  No Family History ABST                                       Abstains            2380               5886 Drinks              9494              13490 FAM2      Family History  No Family History ABST                                       Abstains        0.200438           0.303778 Drinks          0.799562           0.696222 chi-square value, p value, expected counts (403.6040870866473, 9.04438165422264e-90, 1, array([[ 3140.815488,  5125.184512],       [ 8733.184512, 14250.815488]]))

The chi-square value (403.6) is much greater than 3.84, and the p-value (9.0e-90) is much less that 0.05, so I can reject the null hypothesis that there is no correlation between family history of alcoholism and whether a person drinks or abstains. From the table, it appears that those with a family history of alcoholism are more likely to drink than expected (79.9% obtained vs 73.5% expected).

To do a post hoc test, I decided to look at my categories for family history with alcoholism (1 parent, 2 parents, 1 extended relative, >1 extended relative, 1 parent+extended relatives, 2 parents + extended relative, no alcoholic family known) in relation to drinking vs abstaining.

ct2=pd.crosstab(subaa1["ABST"], subaa1["AAFAM2"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) #7 degrees of freedom

print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2) print("Expected chi-square for 7 degrees of freedom is 14.07.") print("Corrected p-value for 20 comparisons") 0.05/20

AAFAM2    1 Par  2 Par  1 ExtRel  >1 ExtRel  1 Par, ExtRel  2 Par, ExtRel  \ ABST                                                                         Abstains    434     36       896        467            479             68   Drinks     1437    132      3285       1953           2345            342   AAFAM2    None known   ABST                   Abstains        5886   Drinks         13490   AAFAM2      1 Par    2 Par  1 ExtRel  >1 ExtRel  1 Par, ExtRel  2 Par, ExtRel  \ ABST                                                                             Abstains 0.231962 0.214286  0.214303   0.192975       0.169618       0.165854   Drinks   0.768038 0.785714  0.785697   0.807025       0.830382       0.834146   AAFAM2    None known   ABST                   Abstains    0.303778   Drinks      0.696222   chi-square value, p value, expected counts (434.99112479242615, 8.331856012499931e-91, 6, array([[  494.901952,    44.438016,  1105.924672,   640.11904 ,          746.981888,   108.44992 ,  5125.184512],       [ 1376.098048,   123.561984,  3075.075328,  1779.88096 ,         2077.018112,   301.55008 , 14250.815488]])) Expected chi-square for 7 degrees of freedom is 14.07. Corrected p-value for 20 comparisons Out[39]: 0.0025

Considering my expected chi-square (according to google) is 14.07, and I got 434.99, I can safely reject the null hypothesis that there is no correlation between specific family with alcoholism and alcohol abstinence.

I further did post-hoc analysis to find the groups with significant differences. Am looking for the Bonferroni adjusted p-value of 0.0025.

This is about 20 comparisons, so I will summarize the ones with significant differences here. For those numbered below, I can reject the null hypothesis that there is no difference in alcohol abstinence for their family history.

1 Parent vs. >1 Extended Relative

1 Parent vs. 1 Parent + Extended Relatives

1 Parent vs 2 Parents + Extended Relatives

1 Parent vs None known

2 Parents vs. None known

1 Extended Relative vs. >1 Extended Relative

1 Extended Relative vs. 1 Parent + Extended Relatives

1 Extended Relative vs. 2 Parents + Extended Relatives

1 Extended Relative vs. None known

1 Parent + Extended Relatives vs. >1 Extended Relative

>1 Extended Relative vs. None known

1 Parent + Extended Relatives vs. None known

2 Parents + Extended Relatives vs. None known

FAMCOMPv1  1 Par  2 Par ABST                   Abstains     434     36 Drinks      1437    132 FAMCOMPv1    1 Par    2 Par ABST                       Abstains  0.231962 0.214286 Drinks    0.768038 0.785714 chi-square value, p value, expected counts (0.18103193164993958, 0.6704879106105417, 1, array([[ 431.27513487,   38.72486513],       [1439.72486513,  129.27513487]])) FAMCOMPv2  1 ExtRel  1 Par ABST                       Abstains        896    434 Drinks         3285   1437 FAMCOMPv2  1 ExtRel    1 Par ABST                         Abstains   0.214303 0.231962 Drinks     0.785697 0.768038 chi-square value, p value, expected counts (2.2488225420879204, 0.13371611345920698, 1, array([[ 918.82518176,  411.17481824],       [3262.17481824, 1459.82518176]])) FAMCOMPv3  1 Par  >1 ExtRel ABST                       Abstains     434        467 Drinks      1437       1953 FAMCOMPv3    1 Par  >1 ExtRel ABST                         Abstains  0.231962   0.192975 Drinks    0.768038   0.807025 chi-square value, p value, expected counts (9.434649397276742, 0.002129237910827844, 1, array([[ 392.86203682,  508.13796318],       [1478.13796318, 1911.86203682]])) FAMCOMPv4  1 Par  1 Par, ExtRel ABST                           Abstains     434            479 Drinks      1437           2345 FAMCOMPv4    1 Par  1 Par, ExtRel ABST                             Abstains  0.231962       0.169618 Drinks    0.768038       0.830382 chi-square value, p value, expected counts (27.52696632852945, 1.5491937328656087e-07, 1, array([[ 363.83876464,  549.16123536],       [1507.16123536, 2274.83876464]])) FAMCOMPv5  1 Par  2 Par, ExtRel ABST                           Abstains     434             68 Drinks      1437            342 FAMCOMPv5    1 Par  2 Par, ExtRel ABST                             Abstains  0.231962       0.165854 Drinks    0.768038       0.834146 chi-square value, p value, expected counts (8.181860997484213, 0.004231133032437819, 1, array([[ 411.76764577,   90.23235423],       [1459.23235423,  319.76764577]])) FAMCOMPv6  1 Par  None known ABST                         Abstains     434        5886 Drinks      1437       13490 FAMCOMPv6    1 Par  None known ABST                           Abstains  0.231962    0.303778 Drinks    0.768038    0.696222 chi-square value, p value, expected counts (41.76775439560775, 1.027846499574022e-10, 1, array([[  556.53598155,  5763.46401845],       [ 1314.46401845, 13612.53598155]])) FAMCOMPv7  2 Par  None known ABST                         Abstains      36        5886 Drinks       132       13490 FAMCOMPv7    2 Par  None known ABST                           Abstains  0.214286    0.303778 Drinks    0.785714    0.696222 chi-square value, p value, expected counts (5.899442601003834, 0.015145677174912012, 1, array([[   50.90544413,  5871.09455587],       [  117.09455587, 13504.90544413]])) FAMCOMPv8  2 Par  2 Par, ExtRel ABST                           Abstains      36             68 Drinks       132            342 FAMCOMPv8    2 Par  2 Par, ExtRel ABST                             Abstains  0.214286       0.165854 Drinks    0.785714       0.834146 chi-square value, p value, expected counts (1.5804032985363066, 0.20870261116515335, 1, array([[ 30.2283737,  73.7716263],       [137.7716263, 336.2283737]])) FAMCOMPv9  1 Par, ExtRel  2 Par ABST                           Abstains             479     36 Drinks              2345    132 FAMCOMPv9  1 Par, ExtRel    2 Par ABST                             Abstains        0.169618 0.214286 Drinks          0.830382 0.785714 chi-square value, p value, expected counts (1.9178315136173767, 0.16609591015709457, 1, array([[ 486.0828877,   28.9171123],       [2337.9171123,  139.0828877]])) FAMCOMPv10  2 Par  >1 ExtRel ABST                         Abstains       36        467 Drinks        132       1953 FAMCOMPv10    2 Par  >1 ExtRel ABST                           Abstains   0.214286   0.192975 Drinks     0.785714   0.807025 chi-square value, p value, expected counts (0.32968603816180103, 0.5658440186807167, 1, array([[  32.65224111,  470.34775889],       [ 135.34775889, 1949.65224111]])) FAMCOMPv11  1 ExtRel  2 Par ABST                       Abstains         896     36 Drinks          3285    132 FAMCOMPv11  1 ExtRel    2 Par ABST                         Abstains    0.214303 0.214286 Drinks      0.785697 0.785714 chi-square value, p value, expected counts (0.009091829850638621, 0.9240359656620418, 1, array([[ 895.99724074,   36.00275926],       [3285.00275926,  131.99724074]])) FAMCOMPv12  1 ExtRel  >1 ExtRel ABST                           Abstains         896        467 Drinks          3285       1953 FAMCOMPv12  1 ExtRel  >1 ExtRel ABST                           Abstains    0.214303   0.192975 Drinks      0.785697   0.807025 chi-square value, p value, expected counts (4.126102965534052, 0.04222648604697947, 1, array([[ 863.30904408,  499.69095592],       [3317.69095592, 1920.30904408]])) FAMCOMPv13  1 ExtRel  1 Par, ExtRel ABST                               Abstains         896            479 Drinks          3285           2345 FAMCOMPv13  1 ExtRel  1 Par, ExtRel ABST                               Abstains    0.214303       0.169618 Drinks      0.785697       0.830382 chi-square value, p value, expected counts (21.051577696848984, 4.470845718926547e-06, 1, array([[ 820.68165596,  554.31834404],       [3360.31834404, 2269.68165596]])) FAMCOMPv14  1 ExtRel  2 Par, ExtRel ABST                               Abstains         896             68 Drinks          3285            342 FAMCOMPv14  1 ExtRel  2 Par, ExtRel ABST                               Abstains    0.214303       0.165854 Drinks      0.785697       0.834146 chi-square value, p value, expected counts (4.995438947932991, 0.02541420669226183, 1, array([[ 877.90982357,   86.09017643],       [3303.09017643,  323.90982357]])) FAMCOMPv15  1 ExtRel  None known ABST                             Abstains         896        5886 Drinks          3285       13490 FAMCOMPv15  1 ExtRel  None known ABST                             Abstains    0.214303    0.303778 Drinks      0.785697    0.696222 chi-square value, p value, expected counts (133.85527554859885, 5.876686334055551e-31, 1, array([[ 1203.69919769,  5578.30080231],       [ 2977.30080231, 13797.69919769]])) FAMCOMPv15  1 Par, ExtRel  >1 ExtRel ABST                                 Abstains              479        467 Drinks               2345       1953 FAMCOMPv15  1 Par, ExtRel  >1 ExtRel ABST                                 Abstains         0.169618   0.192975 Drinks           0.830382   0.807025 chi-square value, p value, expected counts (4.652191417597543, 0.03101393014512997, 1, array([[ 509.44012204,  436.55987796],       [2314.55987796, 1983.44012204]])) FAMCOMPv16  2 Par, ExtRel  >1 ExtRel ABST                                 Abstains               68        467 Drinks                342       1953 FAMCOMPv16  2 Par, ExtRel  >1 ExtRel ABST                                 Abstains         0.165854   0.192975 Drinks           0.834146   0.807025 chi-square value, p value, expected counts (1.5099437664564455, 0.2191476673124011, 1, array([[  77.50883392,  457.49116608],       [ 332.49116608, 1962.50883392]])) FAMCOMPv17  >1 ExtRel  None known ABST                             Abstains          467        5886 Drinks           1953       13490 FAMCOMPv17  >1 ExtRel  None known ABST                             Abstains     0.192975    0.303778 Drinks       0.807025    0.696222 chi-square value, p value, expected counts (127.35685258994046, 1.5520087618161547e-29, 1, array([[  705.37071022,  5647.62928978],       [ 1714.62928978, 13728.37071022]])) FAMCOMPv18  1 Par, ExtRel  None known ABST                                 Abstains              479        5886 Drinks               2345       13490 FAMCOMPv18  1 Par, ExtRel  None known ABST                                 Abstains         0.169618    0.303778 Drinks           0.830382    0.696222 chi-square value, p value, expected counts (216.27137431045233, 5.884680839320336e-49, 1, array([[  809.67387387,  5555.32612613],       [ 2014.32612613, 13820.67387387]])) FAMCOMPv19  1 Par, ExtRel  2 Par, ExtRel ABST                                     Abstains              479             68 Drinks               2345            342 FAMCOMPv19  1 Par, ExtRel  2 Par, ExtRel ABST                                     Abstains         0.169618       0.165854 Drinks           0.830382       0.834146 chi-square value, p value, expected counts (0.014277578109831067, 0.9048880969104677, 1, array([[ 477.6524428,   69.3475572],       [2346.3475572,  340.6524428]])) FAMCOMPv20  2 Par, ExtRel  None known ABST                                 Abstains               68        5886 Drinks                342       13490 FAMCOMPv20  2 Par, ExtRel  None known ABST                                 Abstains         0.165854    0.303778 Drinks           0.834146    0.696222 chi-square value, p value, expected counts (35.65456040933068, 2.355957096167399e-09, 1, array([[  123.37713535,  5830.62286465],       [  286.62286465, 13545.37713535]]))

Full code below if you click “Read More”. It’s a lot and it’s repetitive.

recode2={"1 Par":"1 Par", "2 Par":"2 Par"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv1']=subaa1['AAFAM2'].map(recode2) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv1"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode3={"1 Par":"1 Par", "1 ExtRel":"1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv2']=subaa1['AAFAM2'].map(recode3) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv2"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode4={"1 Par":"1 Par", ">1 ExtRel":">1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv3']=subaa1['AAFAM2'].map(recode4) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv3"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode5={"1 Par":"1 Par", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv4']=subaa1['AAFAM2'].map(recode5) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv4"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode6={"1 Par":"1 Par", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv5']=subaa1['AAFAM2'].map(recode6) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv5"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode7={"1 Par":"1 Par", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv6']=subaa1['AAFAM2'].map(recode7) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv6"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode8={"2 Par":"2 Par", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv7']=subaa1['AAFAM2'].map(recode8) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv7"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode9={"2 Par":"2 Par", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv8']=subaa1['AAFAM2'].map(recode9) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv8"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode10={"2 Par":"2 Par", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv9']=subaa1['AAFAM2'].map(recode10) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv9"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode11={"2 Par":"2 Par", ">1 ExtRel":">1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv10']=subaa1['AAFAM2'].map(recode11) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv10"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode12={"2 Par":"2 Par", "1 ExtRel":"1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv11']=subaa1['AAFAM2'].map(recode12) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv11"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode13={"1 ExtRel":"1 ExtRel", ">1 ExtRel":">1 ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv12']=subaa1['AAFAM2'].map(recode13) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv12"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode14={"1 ExtRel":"1 ExtRel", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv13']=subaa1['AAFAM2'].map(recode14) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv13"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode15={"1 ExtRel":"1 ExtRel", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv14']=subaa1['AAFAM2'].map(recode15) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv14"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode16={"1 ExtRel":"1 ExtRel", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv15']=subaa1['AAFAM2'].map(recode16) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv15"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode16={">1 ExtRel":">1 ExtRel", "1 Par, ExtRel":"1 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv15']=subaa1['AAFAM2'].map(recode16) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv15"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode17={">1 ExtRel":">1 ExtRel", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv16']=subaa1['AAFAM2'].map(recode17) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv16"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode18={">1 ExtRel":">1 ExtRel", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv17']=subaa1['AAFAM2'].map(recode18) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv17"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode19={"1 Par, ExtRel":"1 Par, ExtRel", "None known":"None known"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv18']=subaa1['AAFAM2'].map(recode19) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv18"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode20={"1 Par, ExtRel":"1 Par, ExtRel", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv19']=subaa1['AAFAM2'].map(recode20) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv19"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

recode21={"None known":"None known", "2 Par, ExtRel":"2 Par, ExtRel"} #keeping 2 values but exclude other values in variable subaa1['FAMCOMPv20']=subaa1['AAFAM2'].map(recode21) ct2=pd.crosstab(subaa1["ABST"], subaa1["FAMCOMPv20"]) #categorical variables print(ct2) #get counts colsum2=ct2.sum(axis=0)#use counts from crosstab table. axis=0 says to sum all values in each column #axis=0 means columns. axis=1 means rows colpct2=ct2/colsum2 print(colpct2) print("chi-square value, p value, expected counts") cs2=sst.chi2_contingency(ct2) print(cs2)

[chariflare]

media update

dante & ari: actually read this in march but didn’t make a post about it? it was really different to anything i’ve ever read. also difficult bc i’m not american, not a gay man, very white, etc but my horizons feel.... expanded. you should read it

p&p: i loved it. it’s basically a otome/shojo romcom with the stoic proud love interest who becomes more moe, which just happens to have been written & set in 1700s england? interesting as a historical piece & the other chars are all great (EXTREME VICE) but i don’t think you should bother unless you’re into the rom premise

kh: decided to keep going on proud mode. i hate how in wonderland all the rooms are boxes? what’s the deal?

eccentric fam2: it’s good. surprise i like the top hat guy

[5axismachiningchina]

Most popular Five Axis Milling auctions

Posted from 5 axis machining China blog

Most popular Five Axis Milling auctions

five axis milling eBay auctions you should keep an eye on:

99-008-260 Five Axis Single Milling Stop - Model: Fam2-1

$108.79 End Date: Tuesday Sep-19-2017 19:01:30 PDT Buy It Now for only: $108.79 Buy It Now | Add to watch list

Advanced Numerical Methods to Optimize Cutting Operations of Five Axis Milling M

$245.63 End Date: Monday Sep-18-2017 10:07:04 PDT Buy It Now for only: $245.63 Buy It Now | Add to watch list

Advanced Numerical Methods to Optimize Cutting Operations of Five Axis Milling M

$278.48 End Date: Sunday Sep-17-2017 14:04:11 PDT Buy It Now for only: $278.48 Buy It Now | Add to watch list

[wandlores]

female awesome meme: underappreciated ladies (1/5) - drusilla blackthorn (the dark artifices) 

“dru’s hand was shaking enough that the point of the sword was dancing around; her braids stuck out on either side of her plump face, but the look in her blackthorn eyes was one of steely determination: don’t you dare touch my brother.”

[samcarpnter]

female awesome meme: 7/10 ladies in a film ♡ nile freeman (the old guard)

Out of the two of us, I’m the one who will walk out of there again, one way or another.

[gabriellali]

Thanks God for Your blessing!🙏 Tepat 1 thn setelah bisa kasih kado hp ke papa mama ling dan 1 tahun lebih 3 bulan merintis dari 0 di Allianz, akhirnya kemarin tgl 8 Maret 2017 ling edo memutuskan buat DEAL RUMAH hasil kerja keras ling edo 100% amin utk rumah pertama ling edo ke depannya!🙏❤ Happy birthday jg ko San2 tgl 9 Maret 2017! Sekaligus hari internation woman day!! Yuk cewek2 jg bisa sukses bareng cowok!❤️ . Cerita awalnya udah dr tahun kemarin mama bilang ling mulai cari2 rumah, sempat mau dibeliin mama trus ling edo cicil ke mama, tapi, Tuhan luar biasa ling edo boleh cicil sendiri dari 0😭🙏 Thankyou mama papa, ai suk2, fam2, temen ling edo yg support ling edo! . Thankyou Allianz! Thankyou Ko Deny Oetama dan Ce Inge sbg kakek nenek leader ling edo! Thankyou Sao2 sbg leader ling edo! Thankyou semua teman dan team ling edo yg mau berjalan bersama2 di Allianz! . Jujur seneng banget! Walaupun bukan rumah mewah, tapi ini hasil kerja keras ling edo dari dlu jaman pernah motoran kepanasan kehujanan😭🙏 Nanti udah bisa bawa anak" kaki 4 ling edo k sby😍🙏 Aminnn ling edo bisa capai goals2 ling edo yg lain, bahagiain mama papa, ai suk2😍🙏 Amin Tuhan selalu berkati dan lancar selalu ke depannya dalam Rencana Tuhan❤️ Bagi Tuhan tiada yg mustahil❤️ at Kedai Tua Baru – View on Path.

[deept268]

Kasama sana ako sa 5th Montalban Undecalogy ko kaso andito ako me duty. Nkkmiss din ang Tanauan-Tagaytay afternoons ng fam2. Malapit na. Ayoko na talaga! Onting tiis pa! (at Makati) https://www.instagram.com/p/BxD1SYilYfOrCzhJ9KfncI4UinqLCtYNH4dRXA0/?utm_source=ig_tumblr_share&igshid=157lh50i29lyw