#comp5

[huntersapprentice]

comp5 , beginning adventures in city of tears!

[academicheroes]

Comp5 project

This assignment has many different steps to it. Be sure to check off each one so you know that you accomplished it. You will be creating a personal résumé in Microsoft Word, revising your PowerPoint presentation from Week 5 Discussion, and then zipping these files along with your Excel spreadsheet that you created for Week 4 Project into a compressed folder to upload to the Week 5 Project…

[jeffginnc]

Data Analysis Tools - week 2

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

DATA new; set mydata.marscrater_pds;

attrib    Lat length=$12

                            Szz length=$8

                            NL  length=$3;

LABEL    Lat="Latitude (in degrees)"

                            Number_Layers="# Layers"

                            Szz="Crater Size (in km)"

                            NL="Num Layers";

If Latitude_Circle_Image <=-60 then Lat = "A <-60";

ELSE If Latitude_Circle_Image >-60 and Latitude_Circle_Image <=-30 then Lat = "B >-60 <=-30";

ELSE If Latitude_Circle_Image >-30 and Latitude_Circle_Image <=0 then Lat = "C >-30 <=0";

ELSE If Latitude_Circle_Image >0 and Latitude_Circle_Image <=30 then Lat = "D >0 <=30";

ELSE If Latitude_Circle_Image >30 and Latitude_Circle_Image <=60 then Lat = "E >30 <=60";

ELSE IF Latitude_Circle_Image >60 then Lat = "F >60";

If Diam_Circle_Image <=50 then Szz = "<=100";

ELSE IF Diam_Circle_Image > 50 and Diam_Circle_Image <=100 then Szz = ">100-200";

ELSE IF Diam_Circle_Image > 100 and Diam_Circle_Image <=300 then Szz = ">200-300";

ELSE IF Diam_Circle_Image > 150 and Diam_Circle_Image <=400 then Szz = ">300-400";

ELSE IF Diam_Circle_Image > 400 then Szz = ">400 km";

If Number_Layers <= 0 then NL = "0";

ELSE IF Number_Layers > 0 then NL = ">=1";

PROC SORT; by crater_id;

RUN;

DATA COMP1; SET NEW;

IF Szz = "<=100" OR Szz = ">100-200";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP2; SET NEW;

IF Szz = "<=100" OR Szz = ">200-300";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP3; SET NEW;

IF Szz = "<=100" OR Szz = ">300-400";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP4; SET NEW;

IF Szz = "<=100" OR Szz = ">400 km";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP5; SET NEW;

IF Szz = ">100-200" OR Szz = ">200-300";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP6; SET NEW;

IF Szz = ">100-200" OR Szz = ">300-400";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP7; SET NEW;

IF Szz = ">100-200" OR Szz = ">400 km";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP8; SET NEW;

IF Szz = ">200-300" OR Szz = ">300-400";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP9; SET NEW;

IF Szz = ">200-300" OR Szz = ">400 km";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

DATA COMP10; SET NEW;

IF Szz = ">300-400" OR Szz = ">400 km";

PROC SORT; BY CRATER_ID;

PROC FREQ; TABLES NL*Szz/CHISQ;

RUN;

For this assignment I divided the number of craters into 2 categories, o and >=1  just to provide a 2 category column. The crater size data had already been broken down into 5 columns. As you can see, the initial p value of .0001 shows a relationship between number of layers and crater size. As I performed the ad hoc paired testing, the p value must be below .005 for 10 comparisons to show the null hypothesis is false. I have attached the 2 comparisons (<100 to 100-200, and <100 to 200-300 that met that criteria. I attached one other (<100 to 300-400) that had a higher p value. All remaining comparisons had a p value higher than .005. This shows that the data for the most numerous small craters show there is a relationship between size and number of layers.

[zaworm]

Data Analysis Tools Week 2 Running a Chi-Square Test of Independence

1.Background

I have decided to look at Mars craters and ask the following question:

A. Are shallower depth craters associated with locations near the North and South poles of Mars?” The dataset was limited to craters that had a diameter of 100 km or less and a crater depth that was greater than 0 km.

I divided up the latitude of the planet into 5 bins based on the latitude degree increments/bins to see if there was a significant difference between the latitude of these groups and the depth of the crater.

I classified the crater as either being shallow or deep based on the crater depth.  The cutoff was chosen to be 180m.

2.Notes about the Results

2.1 The crater count based on the latitude is shown below:

2.2 The crater classification is based on the plot below, where the majority of the craters are classified as deep.

2.3 The overall mean deep craters are shown in the plot below.  There seems to be a correlation between latitude and depth of the crater, with a higher concentration of deep craters near the equator of the planet.  Lets perform a Chi-squared analysis to further investigate.

The Chi Squared test was run in Python and the full code is below in section 3.  The full results are shown in section 4.

For example the Chi squared test that was coded was written as follows:

The output showed that the p value was under 0.05 and the value was 0.0 indicating that a Post Hoc test was necessary.  The p value showed that the alternate hypothesis is to be accepted and there is a correlation between deep craters and the latitude.

Response Variable Categorical 2 Levels: Crater Depth (shallow or deep)

************************************************ LAT_LOCATION_GROUP  -90_-50  -50_-20  -20_20  20_50  50_90 IS_CRATER_DEEP                                             SHALLOW                5507     6847    8449   5501   2821 DEEP                   4402    14528   20948   6966    551 LAT_LOCATION_GROUP   -90_-50   -50_-20   -20_20     20_50     50_90 IS_CRATER_DEEP                                                     SHALLOW             0.555757  0.320327  0.28741  0.441245  0.836595 DEEP                0.444243  0.679673  0.71259  0.558755  0.163405 chi-square value, p value, expected counts (5870.45780474349, 0.0, 4, array([[ 3771.55808939,  8135.74065604, 11189.06985102,  4745.18263199,        1283.44877156],      [ 6137.44191061, 13239.25934396, 18207.93014898,  7721.81736801,        2088.55122844]]))

An example Post Hoc test was coded as follows:

The output showed that the p value was 0.0 indicating the was under the Bonferroni adjustment and the null hypothesis was rejected indicating there is a correlation between latitude groups (-90 to -50 and -50 to -20):

************************************************ COMP1-10        -50_-20  -90_-50 IS_CRATER_DEEP                   SHALLOW            6847     5507 DEEP              14528     4402 COMP1-10         -50_-20   -90_-50 IS_CRATER_DEEP                     SHALLOW         0.320327  0.555757 DEEP            0.679673  0.444243 chi-square value, p value, expected counts (1569.4618848788969, 0.0, 1, array([[ 8440.95224396,  3913.04775604],     [12934.04775604,  5995.95224396]]))

A table summarizing the results of the 10 Chi Square results are shown below.  It shows that we can not accept the null hypothesis and there is a correlation between latitude and crater depth.  There is a stronger correlation when looking at the areas close to the poles vs the equator.

3.Raw Python Code:

The raw Python code is shown in the photos below.  I decided to use screenshots for easier readability as it includes syntax highlighting.

4.Code Output:

Below is the raw output from the code.  The interpretation and comments on the results are shown above in Section 2.

Mars Crater Study

Data Analysis Tools

Running a Chi-Square Test of Independence

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

Info about this dataset

Total number of rows in the dataset: 384343

Total number of columns in the dataset: 10 Info about this subset and narrowed down  dataset

Total number of rows in the dataset: 76520

Total number of columns in the dataset: 5

-----------------------   LATTITUDE BIN SECTION   -----------------------------------------

Latitude bin counts: -90_-50     9909 -50_-20    21375 -20_20     29397 20_50      12467 50_90       3372 Name: LAT_LOCATION_GROUP, dtype: int64

-----------------------   DEPTH MANAGEMENT SECTION  ----------------------------------------- Here are the counts for deep and shallow craters craters:

DEEP       47395 SHALLOW    29125 Name: IS_CRATER_DEEP, dtype: int64

---------- Categorical Eplanatory Variable: Latitude group

Response Variable Categorical 2 Levels: Crater Depth (shallow or deep)

************************************************ LAT_LOCATION_GROUP  -90_-50  -50_-20  -20_20  20_50  50_90 IS_CRATER_DEEP                                             SHALLOW                5507     6847    8449   5501   2821 DEEP                   4402    14528   20948   6966    551 LAT_LOCATION_GROUP   -90_-50   -50_-20   -20_20     20_50     50_90 IS_CRATER_DEEP                                                     SHALLOW             0.555757  0.320327  0.28741  0.441245  0.836595 DEEP                0.444243  0.679673  0.71259  0.558755  0.163405 chi-square value, p value, expected counts (5870.45780474349, 0.0, 4, array([[ 3771.55808939,  8135.74065604, 11189.06985102,  4745.18263199,         1283.44877156],       [ 6137.44191061, 13239.25934396, 18207.93014898,  7721.81736801,         2088.55122844]]))

************************************************ COMP1-10        -50_-20  -90_-50 IS_CRATER_DEEP                   SHALLOW            6847     5507 DEEP              14528     4402 COMP1-10         -50_-20   -90_-50 IS_CRATER_DEEP                     SHALLOW         0.320327  0.555757 DEEP            0.679673  0.444243 chi-square value, p value, expected counts (1569.4618848788969, 0.0, 1, array([[ 8440.95224396,  3913.04775604],       [12934.04775604,  5995.95224396]]))

************************************************ COMP2-10        -20_-20  -90_-50 IS_CRATER_DEEP                   SHALLOW            8449     5507 DEEP              20948     4402 COMP2-10        -20_-20   -90_-50 IS_CRATER_DEEP                   SHALLOW         0.28741  0.555757 DEEP            0.71259  0.444243 chi-square value, p value, expected counts (2329.314932557565, 0.0, 1, array([[10437.70752557,  3518.29247443],       [18959.29247443,  6390.70752557]]))

************************************************ COMP3-10        -90_-50  20_50 IS_CRATER_DEEP                 SHALLOW            5507   5501 DEEP               4402   6966 COMP3-10         -90_-50     20_50 IS_CRATER_DEEP                     SHALLOW         0.555757  0.441245 DEEP            0.444243  0.558755 chi-square value, p value, expected counts (289.20138905047946, 7.422790875630638e-65, 1, array([[4874.78870218, 6133.21129782],       [5034.21129782, 6333.78870218]]))

************************************************ COMP4-10        -90_-50  50_90 IS_CRATER_DEEP                 SHALLOW            5507   2821 DEEP               4402    551 COMP4-10         -90_-50     50_90 IS_CRATER_DEEP                     SHALLOW         0.555757  0.836595 DEEP            0.444243  0.163405 chi-square value, p value, expected counts (847.2982157758986, 2.811519087431208e-186, 1, array([[6213.54958211, 2114.45041789],       [3695.45041789, 1257.54958211]]))

************************************************ COMP5-10        -20_20  -50_-20 IS_CRATER_DEEP                 SHALLOW           8449     6847 DEEP             20948    14528 COMP5-10         -20_20   -50_-20 IS_CRATER_DEEP                   SHALLOW         0.28741  0.320327 DEEP            0.71259  0.679673 chi-square value, p value, expected counts (63.54773843933685, 1.5652720877138506e-15, 1, array([[ 8856.38761522,  6439.61238478],       [20540.61238478, 14935.38761522]]))

************************************************ COMP6-10        -50_-20  20_50 IS_CRATER_DEEP                 SHALLOW            6847   5501 DEEP              14528   6966 COMP6-10         -50_-20     20_50 IS_CRATER_DEEP                     SHALLOW         0.320327  0.441245 DEEP            0.679673  0.558755 chi-square value, p value, expected counts (496.2855186756665, 6.111846388145869e-110, 1, array([[ 7799.14012174,  4548.85987826],       [13575.85987826,  7918.14012174]]))

************************************************ COMP7-10        -50_-20  50_90 IS_CRATER_DEEP                 SHALLOW            6847   2821 DEEP              14528    551 COMP7-10         -50_-20     50_90 IS_CRATER_DEEP                     SHALLOW         0.320327  0.836595 DEEP            0.679673  0.163405 chi-square value, p value, expected counts (3258.881845816943, 0.0, 1, array([[ 8350.64856346,  1317.35143654],       [13024.35143654,  2054.64856346]]))

************************************************ COMP8-10        -20_20  20_50 IS_CRATER_DEEP               SHALLOW           8449   5501 DEEP             20948   6966 COMP8-10         -20_20     20_50 IS_CRATER_DEEP                   SHALLOW         0.28741  0.441245 DEEP            0.71259  0.558755 chi-square value, p value, expected counts (931.7404706337541, 1.2358200405213006e-204, 1, array([[ 9795.72305561,  4154.27694439],       [19601.27694439,  8312.72305561]]))

************************************************ COMP9-10        -20_20  50_90 IS_CRATER_DEEP               SHALLOW           8449   2821 DEEP             20948    551 COMP9-10         -20_20     50_90 IS_CRATER_DEEP                   SHALLOW         0.28741  0.836595 DEEP            0.71259  0.163405 chi-square value, p value, expected counts (4040.9902387527836, 0.0, 1, array([[10110.29295981,  1159.70704019],       [19286.70704019,  2212.29295981]]))

************************************************ COMP10-10       20_50  50_90 IS_CRATER_DEEP               SHALLOW          5501   2821 DEEP             6966    551 COMP10-10          20_50     50_90 IS_CRATER_DEEP                     SHALLOW         0.441245  0.836595 DEEP            0.558755  0.163405 chi-square value, p value, expected counts (1662.095048322109, 0.0, 1, array([[6550.31087821, 1771.68912179],       [5916.68912179, 1600.31087821]]))

[gympartnersweden]

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[chrisfinancialadvisor]

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[lam-blue-datascience-blog]

Chi²

The following analysis shows the relationship assessment between the depth and the diameter of the mars craters. The null hypothesis assumes that the depth increase according to the diameter of the craters. With the Chi² test it is possible to say that the null hypothesis cannot be refused, except for the cases that involves the depth interval from 4 to 5. This analysis confirm the graph tendency, that shows that the depth of the craters increase up to category 4-5, when the values drop down.

The results and the code are presented below:

COMP1                  (-1, 1]  (1, 2] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                 375320    3714 (50, 1170]                1086     776 COMP1                   (-1, 1]    (1, 2] CAT_DIAM_CIRCLE_IMAGE                     (1, 50]                0.997115  0.827171 (50, 1170]             0.002885  0.172829 chi-square value, p-value and expected counts (26307.73928914812, 0.0, 1, array([[3.74565949e+05, 4.46805075e+03],       [1.84005075e+03, 2.19492460e+01]])) COMP2                  (1, 2]  (2, 3] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                  3714     107 (50, 1170]                776     195 COMP2                    (1, 2]    (2, 3] CAT_DIAM_CIRCLE_IMAGE                     (1, 50]                0.827171  0.354305 (50, 1170]             0.172829  0.645695 chi-square value, p-value and expected counts (388.6868001527464, 1.5986878600726574e-86, 1, array([[3580.19407346,  240.80592654],       [ 909.80592654,   61.19407346]])) COMP3                  (2, 3]  (3, 4] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                   107       2 (50, 1170]                195      10 COMP3                    (2, 3]    (3, 4] CAT_DIAM_CIRCLE_IMAGE                     (1, 50]                0.354305  0.166667 (50, 1170]             0.645695  0.833333 chi-square value, p-value and expected counts (1.0606313939984895, 0.3030712228105186, 1, array([[104.8343949,   4.1656051],       [197.1656051,   7.8343949]])) COMP4                  (3, 4]  (4, 5] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                     2       1 (50, 1170]                 10       3 COMP4                    (3, 4]  (4, 5] CAT_DIAM_CIRCLE_IMAGE                   (1, 50]                0.166667    0.25 (50, 1170]             0.833333    0.75 chi-square value, p-value and expected counts (0.13675213675213677, 0.711531417761122, 1, array([[2.25, 0.75],       [9.75, 3.25]])) COMP5                  (-1, 1]  (1, 3] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                 375320    3821 (50, 1170]                1086     971 COMP5                   (-1, 1]    (1, 3] CAT_DIAM_CIRCLE_IMAGE                     (1, 50]                0.997115  0.797371 (50, 1170]             0.002885  0.202629 chi-square value, p-value and expected counts (35138.049317127116, 0.0, 1, array([[3.74374858e+05, 4.76614167e+03],       [2.03114167e+03, 2.58583308e+01]])) COMP6                  (-1, 1]  (1, 4] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                 375320    3823 (50, 1170]                1086     981 COMP6                   (-1, 1]    (1, 4] CAT_DIAM_CIRCLE_IMAGE                     (1, 50]                0.997115  0.795795 (50, 1170]             0.002885  0.204205 chi-square value, p-value and expected counts (35612.11122482119, 0.0, 1, array([[3.74365048e+05, 4.77795171e+03],       [2.04095171e+03, 2.60482883e+01]])) COMP7                  (-1, 1]  (1, 5] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                 375320    3824 (50, 1170]                1086     984 COMP7                   (-1, 1]    (1, 5] CAT_DIAM_CIRCLE_IMAGE                     (1, 50]                0.997115  0.795341 (50, 1170]             0.002885  0.204659 chi-square value, p-value and expected counts (35751.231014360455, 0.0, 1, array([[3.74362108e+05, 4.78189246e+03],       [2.04389246e+03, 2.61075406e+01]])) COMP8                  (1, 2]  (2, 4] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                  3714     109 (50, 1170]                776     205 COMP8                    (1, 2]    (2, 4] CAT_DIAM_CIRCLE_IMAGE                     (1, 50]                0.827171  0.347134 (50, 1170]             0.172829  0.652866 chi-square value, p-value and expected counts (413.2075233000307, 7.344083031890247e-92, 1, array([[3573.1203164,  249.8796836],       [ 916.8796836,   64.1203164]])) COMP9                  (2, 3]  (3, 5] CAT_DIAM_CIRCLE_IMAGE                 (1, 50]                   107       3 (50, 1170]                195      13 COMP9                    (2, 3]  (3, 5] CAT_DIAM_CIRCLE_IMAGE                   (1, 50]                0.354305  0.1875 (50, 1170]             0.645695  0.8125 chi-square value, p-value and expected counts (1.2040727717026352, 0.2725091793647979, 1, array([[104.46540881,   5.53459119],       [197.53459119,  10.46540881]]))

import pandas import numpy import seaborn import matplotlib.pyplot as p import scipy.stats

data=pandas.read_csv('_b190b54e08fd8a7020b9f120015c2dab_marscrater_pds.csv', low_memory=False) #declare variables data['MORPHOLOGY_EJECTA_1']=data['MORPHOLOGY_EJECTA_1'].replace(' ', numpy.nan) data['MORPHOLOGY_EJECTA_2']=data['MORPHOLOGY_EJECTA_2'].replace(' ', numpy.nan) data['DEPTH_RIMFLOOR_TOPOG']=data['DEPTH_RIMFLOOR_TOPOG'].replace(' ', numpy.nan) data['DIAM_CIRCLE_IMAGE']=data['DIAM_CIRCLE_IMAGE'].replace(' ', numpy.nan)

print(len(data)) #number of observations (rows) print(len(data.columns)) #number of variables (columns)

c3=data['DEPTH_RIMFLOOR_TOPOG'].value_counts(sort=False) #number of observations of Depth p3=data['DEPTH_RIMFLOOR_TOPOG'].value_counts(sort=False, normalize=True) #percentage of Depth

c5=data.groupby('DEPTH_RIMFLOOR_TOPOG').size() #group and categorize rimfloor depth data['CAT_RIMFLOOR_DEPTH']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[-1,1,2,3,4,5]) data['CAT_RIMFLOOR_DEPTH']=data['CAT_RIMFLOOR_DEPTH'].astype('category')

c6=data.groupby('DIAM_CIRCLE_IMAGE').size() #group and categorize circle diameter data['CAT_DIAM_CIRCLE_IMAGE']=pandas.cut(data.DIAM_CIRCLE_IMAGE,[1,50,1170]) data['CAT_DIAM_CIRCLE_IMAGE']=data['CAT_DIAM_CIRCLE_IMAGE'].astype('category')

#recoding the variables - composition 1 data['COMP1']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[-1,1,2]) data['COMP1'].dropna(inplace=True)

#contigency table of observed counts ct1=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP1']) print(ct1)

#column percentages calculation colsum1=ct1.sum(axis=0) colpct1=ct1/colsum1 print(colpct1)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs1=scipy.stats.chi2_contingency(ct1) print(cs1)

#recoding the variables - composition 2 data['COMP2']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[1,2,3]) data['COMP2'].dropna(inplace=True)

#contigency table of observed counts ct2=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP2']) print(ct2)

#column percentages calculation colsum2=ct2.sum(axis=0) colpct2=ct2/colsum2 print(colpct2)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs2=scipy.stats.chi2_contingency(ct2) print(cs2)

#recoding the variables - composition 3 data['COMP3']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[2,3,4]) data['COMP3'].dropna(inplace=True)

#contigency table of observed counts ct3=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP3']) print(ct3)

#column percentages calculation colsum3=ct3.sum(axis=0) colpct3=ct3/colsum3 print(colpct3)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs3=scipy.stats.chi2_contingency(ct3) print(cs3)

#recoding the variables - composition 4 data['COMP4']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[3,4,5]) data['COMP4'].dropna(inplace=True)

#contigency table of observed counts ct4=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP4']) print(ct4)

#column percentages calculation colsum4=ct4.sum(axis=0) colpct4=ct4/colsum4 print(colpct4)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs4=scipy.stats.chi2_contingency(ct4) print(cs4)

#recoding the variables - composition 5 data['COMP5']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[-1,1,3]) data['COMP5'].dropna(inplace=True)

#contigency table of observed counts ct5=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP5']) print(ct5)

#column percentages calculation colsum5=ct5.sum(axis=0) colpct5=ct5/colsum5 print(colpct5)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs5=scipy.stats.chi2_contingency(ct5) print(cs5)

#recoding the variables - composition 6 data['COMP6']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[-1,1,4]) data['COMP6'].dropna(inplace=True)

#contigency table of observed counts ct6=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP6']) print(ct6)

#column percentages calculation colsum6=ct6.sum(axis=0) colpct6=ct6/colsum6 print(colpct6)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs6=scipy.stats.chi2_contingency(ct6) print(cs6)

#recoding the variables - composition 7 data['COMP7']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[-1,1,5]) data['COMP7'].dropna(inplace=True)

#contigency table of observed counts ct7=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP7']) print(ct7)

#column percentages calculation colsum7=ct7.sum(axis=0) colpct7=ct7/colsum7 print(colpct7)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs7=scipy.stats.chi2_contingency(ct7) print(cs7)

#recoding the variables - composition 8 data['COMP8']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[1,2,4]) data['COMP8'].dropna(inplace=True)

#contigency table of observed counts ct8=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP8']) print(ct8)

#column percentages calculation colsum8=ct8.sum(axis=0) colpct8=ct8/colsum8 print(colpct8)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs8=scipy.stats.chi2_contingency(ct8) print(cs8)

#recoding the variables - composition 9 data['COMP9']=pandas.cut(data.DEPTH_RIMFLOOR_TOPOG,[2,3,5]) data['COMP9'].dropna(inplace=True)

#contigency table of observed counts ct9=pandas.crosstab(data['CAT_DIAM_CIRCLE_IMAGE'], data['COMP9']) print(ct9)

#column percentages calculation colsum9=ct9.sum(axis=0) colpct9=ct9/colsum9 print(colpct9)

#chi-square calculation print ('chi-square value, p-value and expected counts') cs9=scipy.stats.chi2_contingency(ct9) print(cs9)

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from Amazon Best Sellers http://app.amazonreviews.org/fox-racing-comp-5-mens-off-road-motorcycle-boots-white-size-10/

[fifa1613]

Unisystems | Q&R W3C Αθήνα, 29 Μαΐου 2017 ΔΕΛΤΙΟ ΤΥΠΟΥ ΥΠΟΒΟΛΗ ΑΙΤΗΣΕΩΝ ΣΥΜΜΕΤΟΧΗΣ ΣΤΗΝ ΠΡΟΚΗΡΥΞΗ 5Κ/2017   Γνωστοποιείται ότι εκκίνησε η διαδικασία υποβολής των αιτήσεων των υποψηφίων στην Προκήρυξη 5Κ/2017 του ΑΣΕΠ (ΦΕΚ 16/9-5-2017 Τεύχος Προκηρύξεων ΑΣΕΠ) που αφορά στην πλήρωση, με σειρά προτεραιότητας, διακοσίων πενήντα επτά (257) θέσεων τακτικού προσωπικού Πανεπιστημιακής, Τεχνολογικής και Δευτεροβάθμιας Εκπαίδευσης σε φορείς του Υπουργείου Υγείας και στο Αρεταίειο Νοσοκομείο (Υπουργείο Παιδείας, Έρευνας και Θρησκευμάτων). Οι υποψήφιοι πρέπει να συμπληρώσουν και να υποβάλουν ηλεκτρονική αίτηση συμμετοχής στο Α.Σ.Ε.Π. (Πολίτες → Ηλεκτρονικές Υπηρεσίες) ακολουθώντας τις οδηγίες που παρέχονται στην Προκήρυξη (Παράρτημα ΣΤ΄). Σχετικά επισημαίνονται και τα ακόλουθα : Η προθεσμία υποβολής των ηλεκτρονικών αιτήσεων συμμετοχής στη διαδικασία λήγει στις 13 Ιουνίου 2017, ημέρα Τρίτη και ώρα 14:00. Η συμμετοχή στην εν λόγω διαδικασία ολοκληρώνεται με την αποστολή της υπογεγραμμένης εκτυπωμένης μορφής της ηλεκτρονικής αίτησης των υποψηφίων, με τα απαιτούμενα, κατά περίπτωση, δικαιολογητικά, στο ΑΣΕΠ, μέχρι και τη 16η Ιουνίου 2017, ημέρα Παρασκευή, ταχυδρομικά με συστημένη επιστολή στη διεύθυνση: Α.Σ.Ε.Π. Αίτηση για την Προκήρυξη 5Κ/2017 Τ.Θ. 14308 Αθήνα Τ.Κ. 11510 αναγράφοντας στο φάκελο την κατηγορία Π.Ε. ή Τ.Ε. ή Δ.Ε. της οποίας θέσεις διεκδικούν. Για την ομαλή λειτουργία του συστήματος υποβολής των ηλεκτρονικών αιτήσεων, συνιστάται στους υποψήφιους να μην περιμένουν την τελευταία ημέρα για να υποβάλλουν την ηλεκτρονική τους αίτηση καθώς τυχόν υπερφόρτωση του διαδικτύου ή σφάλμα στη σύνδεση με αυτό θα μπορούσε να οδηγήσει σε δυσκολία ή αδυναμία υποβολής αυτής. Το ΑΣΕΠ δεν φέρει ευθύνη για τυχόν αδυναμία υποβολής ηλεκτρονικής αίτησης που οφείλεται στους προαναφερόμενους παράγοντες. Για γενικές διευκρινίσεις σχετικά με την Προκήρυξη και τη συμμετοχή σε αυτή (δικαιολογητικά, τίτλοι σπουδών κτλ.) οι υποψήφιοι μπορούν να απευθύνονται στο Γραφείο Εξυπηρέτησης Πολιτών του ΑΣΕΠ τηλεφωνικά (2131319100, εργάσιμες ημέρες και ώρες 08:00 μέχρι 14:00) ή μέσω email στο enimerosi@asep.gr Για τυχόν τεχνικές διευκρινίσεις και μόνο σχετικά με τη διαδικασία συμπλήρωσης και υποβολής των ηλεκτρονικών αιτήσεων, οι υποψήφιοι μπορούν να απευθύνονται στη Διεύθυνση Ηλεκτρονικής Διοίκησης του ΑΣΕΠ τηλεφωνικά (2131319100): Τις εργάσιμες ημέρες · Κατά τις ώρες 08:00 μέχρι 20:00 Τις αργίες · Κατά τις ώρες 10:00 μέχρι 16:00 ή μέσω email στο helpdesk@asep.gr Σε κάθε περίπτωση, κρίνεται απαραίτητο οι υποψήφιοι να συμβουλεύονται τις Συχνές Ερωτήσεις, τα Εγχειρίδια Χρήσης και τον Ηλεκτρονικό Οδηγό της Προκήρυξης (Βοήθεια → Συχνές Ερωτήσεις ή e-Οδηγός 5Κ/2017 ή Εγχειρίδια Χρήσης) πριν την επικοινωνία τους με το ΑΣΕΠ.

[pressreleaseng-blog]

Global Chop Cutter Market 2016 - Dadaux SAS, Nantsune, Buehler, Kovai Classic Industries, COMP5

Global Chop Cutter Market 2016 – Dadaux SAS, Nantsune, Buehler, Kovai Classic Industries, COMP5

To begin with, the report defines the Chop Cutter Market and segments it based on the most important dynamics, such as applications, geographical/regional markets, and competitive scenario. Furthermore market report provides detail analysis, forecast, Size, share, outlook, current trends, market demand, market growth. Macroeconomic and microeconomic factors environments that currently prevail and…

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from Amazon Best Sellers http://app.amazonreviews.org/fox-racing-comp-5-mens-off-road-motorcycle-boots-white-size-9/