#sub7

[writerdoublein]

[fuckitbiz]

MOBMAN SUB7 T-Shirt on Amazon

[crazy-so-na-sega]

"Tutti noi in questo forum (chi normaloide chi BV) siamo sub7. Ebbene dal percorso da redpillato che ho fatto vi dò un consiglio: lasciate perdere le np, non sperate in nessuna ltr perché anche se miracolosamente riuscite ad ottenerne una la vostra vita sarà un inferno comunque".

-----[dialogo vero di un blog vero]

immagino Socrate che cerca di convincere Agatone a cambiare prospettiva con un linguaggio criptico...sennò non ci capisco 'na sega...;-)

[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

[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.

[yaycheese]

Hochigaki! Kyoto - Full Fushimi Inari loop - Byōdō-in - Westside 33 (metal hammered pots) - Ichihara Heibei Shōten (chopsticks) - Naito Shoten (brooms) - Honke Owariya (kyotos oldest resto, soba) - Dustpan! - Sun umbrella Tokyo - Meiji Jingū (iris garden) - Jindaiji Temple (mame daishi statue and 2 seals) - Ryusenji Temple (seal with 2 types combined) - Mixology Salon Ginza - Grape sandos - Bongen Coffee Ginza - COFFEE 葵 - 裏の山の木の子 (mushroom bouquet hot pot) - Tokyu Kabuchiko Tower Tokyo / Shimokitazawa - Trefac style - Ogawa Coffee Laboratory - Sarutahiko Coffee Yoru no Bu - Andon - Café Trois Chambres - Flamingo Shimokitazawa - Jet Set - Gallery Hana Tokyo / Ebisu - Ebisu: https://tabelog.com/tokyo/A1303/A130302/ - Sowado - Tachinomiya (standing bars) - Ebisu Yokocho (alley with small restaurants, bars) - Afuri (ramen) *** Mame daishi seals http://chanekovsky.web.fc2.com/ofuda-gansandaishi.html http://seikouminzoku.sakura.ne.jp/sub7-21.html Mame Daishi // Osaka http://chanekovsky.web.fc2.com/shitennoji.html http://chanekovsky.web.fc2.com/shitennoji-rokujiraisando.html Mame Daishi // Kyoto - We have, but there may be other seals: http://chanekovsky.web.fc2.com/shinnyodo-amida.html - Unavailable last visit: http://chanekovsky.web.fc2.com/sonshoin.html - We have: http://chanekovsky.web.fc2.com/rozanji.html - Otsu: http://chanekovsky.web.fc2.com/guhoji.html - Otsu: http://chanekovsky.web.fc2.com/sanzenin.html Mame Daishi // Tokyo http://chanekovsky.web.fc2.com/meguro-fudo.html http://chanekovsky.web.fc2.com/kaneiji-kaizando.html (+40min) http://chanekovsky.web.fc2.com/jindaiji.html (+60min) http://chanekovsky.web.fc2.com/kitain.html

[eml4]

assignment week 4

TESTING A POTENTIAL MODERATOR

I will test the effect of taken a public or written pledge to remain a virgin until marriage in the relation between sex intercourse and duration of romantic relationship.

Data management for the moderator variable

DATA3 = sub7[(sub7["H1ID5"]== 0)]

DATA4 = sub7[(sub7["H1ID5"]== 1)]

ct3 = pd.crosstab(DATA3["lengh_class"], DATA3["H1RI27_1"])

print(ct3)

colsum= ct3.sum(axis=0)

colpct=ct3/colsum

print(colpct)

cs3= scipy.stats.chi2_contingency(ct3)

print(cs3)

ct4 = pd.crosstab(DATA4["lengh_class"], DATA4["H1RI27_1"])

print(ct4)

colsum= ct4.sum(axis=0)

colpct=ct4/colsum

print(colpct)

cs4= scipy.stats.chi2_contingency(ct4)

print(cs4)  

Results

DATA3 = sub7[(sub7["H1ID5"]== 0)]

ct3 = pd.crosstab(DATA3["lengh_class"], DATA3["H1RI27_1"])

print(ct3)

H1RI27_1     1.0  2.0

lengh_class         

0-2           68   67

3-5            1    2

6-10           1    1

colsum= ct3.sum(axis=0)

colpct=ct3/colsum

print(colpct)

cs3= scipy.stats.chi2_contingency(ct3)

print(cs3)

H1RI27_1          1.0       2.0

lengh_class                   

0-2          0.971429  0.957143

3-5          0.014286  0.028571

6-10         0.014286  0.014286

Chi2ContingencyResult(statistic=0.34074074074074073, pvalue=0.8433524060031772, dof=2, expected_freq=array([[67.5, 67.5],

       [ 1.5,  1.5],

       [ 1. ,  1. ]]))

ct4 = pd.crosstab(DATA4["lengh_class"], DATA4["H1RI27_1"])

print(ct4)

colsum= ct4.sum(axis=0)

colpct=ct4/colsum

print(colpct)

cs4= scipy.stats.chi2_contingency(ct4)

print(cs4)

H1RI27_1     1.0  2.0

lengh_class         

0-2            4    3

3-5            1    0

H1RI27_1     1.0  2.0

lengh_class         

0-2          0.8  1.0

3-5          0.2  0.0

Chi2ContingencyResult(statistic=0.0, pvalue=1.0, dof=1, expected_freq=array([[4.375, 2.625],

       [0.625, 0.375]]))

Interpretation: in the presence of 2 groups of potential moderator variable, the relation between sex intercourse and the duration of romantic relationship did not change. The P value remains > 0.05. We conclude that there is not association between the 2 variables independently of the fact to take a public or written pledge to remain a virgin until marriage.

[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

[ourjacky]

HyperionORBIT - Sub7 - SoundCloud

Écouter HyperionORBIT - Sub7 par Union Trance Mission sur #SoundCloud

JE CRAQUE YEAH !!!!

[writerdoublein]

Fanart of my friends ocs. Characters by: @anemorian on Tiktok and instagram

[percival895]

*mode redpill on*

Monica Bellucci dopo il wallo si mette con un nerd ricco sub7 che fino a dieci anni fa avrebbe schifato più di un bacherozzo perchè impegnata a fare carosello o in relazioni tossiche col chad di turno.

*mode redpill off*

Tim Burton and Monica Bellucci by Carlos del Pozo

[bilvensstuff]

https://usbenefitreport.com/yt/a1-sub/?cmpid=664c1a950753ff0001c546af&utm_campaign={replace}&sub2=&sub3=&sub4=162360927037&sub5=705644239109&sub6=21295161040&sub7=m&sub8=&sub9=ytv&sub10=youtube.com&utm_source=Google&wbraid=&gbraid=&creative=GS+OBC+0415+a2+NE+SUB&lp=a1-sub&ref_id=Cj0KCQjwwae1BhC_ARIsAK4JfrzSaFbIBLWbGKEPwLAIXpTEriZluW8cWcVAo5CzxGRD7DEuAkAsjgEaAmhLEALw_wcB&gclid=Cj0KCQjwwae1BhC_ARIsAK4JfrzSaFbIBLWbGKEPwLAIXpTEriZluW8cWcVAo5CzxGRD7DEuAkAsjgEaAmhLEALw_wcB

[yaycheese]

Korea and Japan Oct 2023

Lunch * Ginseng chicken soup * Hamburg steak + Chicken bone soup Korea + Nagmyeon in Jeju + Hamburg Nakameguro

Dinner * Jeju black pork * Gibier Miyama + Yakitori bar + Dondon

Coffee * Cashmere Costa Rica * Torihebi + Cashmere oat milk latte

Experience * Halloween in Kyoto * Sunset peak * Last hurrah in Tokyo * Ohara * Sushi Matsumoto * Hanging out with THK + Seoul bar + Jeju group day (incl waterfalls and Muhly) + Shinjuku late night Cocktail * Osmanthus soda or Chartreuse sonic * Gamhongro + Grape mocktail at Sapphire + Gimbap cocktail at Bar Chan

Snack * Fresh persimmons * Sweet potato sando + Black sesame pastry with THK + Tiramisu inssadong + Egg bread

Purchase * Bags * Coffee dripper * Water bottle * Clipboard * Korea sweater + Prada shoes + Daiso mirror

Take my time / have some moments to fully take things in

See what’s new (coffee, bar, restaurants)

Experience / shop the things that are truly Korean or Japanese

KOREA

Eujiro

Gwanjang market (Mayak Kimbap / Ggoma Gimbap)

Coffee Hanyakbang

Horangii Cafe

Sewoon Arcade

을지로회관 Hoikwan Grillbar

Euljiro brewing🍺

Ace 4 club🍸

Manseon Hof🍺

Salon de Thé🍷

Sarangbang (Kalguksu)

Myeongdong

Daiso myeongdong

Kyoja (Kalguksu)

Myeongdong Market

Chicken and Beer street @ Myeongdong 7ga-gi

Gabaedo Coffee☕️

JAPAN

Kyoto

Full Fushimi Inari loop

Byōdō-in

Westside 33 (metal hammered pots)

Ichihara Heibei Shōten (chopsticks)

Naito Shoten (brooms)

Honke Owariya (kyotos oldest resto, soba)

Dustpan!

Sun umbrella

Tokyo

Meiji Jingū (iris garden)

Jindaiji Temple (mame daishi statue and 2 seals)

Ryusenji Temple (seal with 2 types combined)

Mixology Salon Ginza

Grape sandos

Bongen Coffee Ginza

COFFEE 葵

裏の山の木の子 (mushroom bouquet hot pot)

Tokyu Kabuchiko Tower

Tokyo / Shimokitazawa

Trefac style

Ogawa Coffee Laboratory

Sarutahiko Coffee Yoru no Bu

Andon

Café Trois Chambres

Flamingo Shimokitazawa

Jet Set

Gallery Hana

Tokyo / Ebisu

Ebisu: https://tabelog.com/tokyo/A1303/A130302/

Sowado

Tachinomiya (standing bars)

Ebisu Yokocho (alley with small restaurants, bars)

Afuri (ramen)

Mame daishi seals http://chanekovsky.web.fc2.com/ofuda-gansandaishi.html http://seikouminzoku.sakura.ne.jp/sub7-21.html

Mame Daishi // Osaka http://chanekovsky.web.fc2.com/shitennoji.html http://chanekovsky.web.fc2.com/shitennoji-rokujiraisando.html

Mame Daishi // Kyoto

We have, but there may be other seals: http://chanekovsky.web.fc2.com/shinnyodo-amida.html

Unavailable last visit: http://chanekovsky.web.fc2.com/sonshoin.html

We have: http://chanekovsky.web.fc2.com/rozanji.html

Otsu: http://chanekovsky.web.fc2.com/guhoji.html

Otsu: http://chanekovsky.web.fc2.com/sanzenin.html

Mame Daishi // Tokyo http://chanekovsky.web.fc2.com/meguro-fudo.html http://chanekovsky.web.fc2.com/kaneiji-kaizando.html (+40min) http://chanekovsky.web.fc2.com/jindaiji.html (+60min) http://chanekovsky.web.fc2.com/kitain.html