diff --git a/charts.html b/charts.html index 08ddbe2..053ecaa 100644 --- a/charts.html +++ b/charts.html @@ -34,14 +34,14 @@ -
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2021-02-19T14:30:00.512915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/2021-02-19T14:47:46.013551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-19T14:30:00.700859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-19T14:47:46.185837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-19T14:30:00.889393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-19T14:47:46.360926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-19T14:30:01.091779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-19T14:47:46.548399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-19T14:29:53.487863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-19T14:47:39.819441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-19T14:29:53.921125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-19T14:47:40.156388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-19T14:29:54.419219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-19T14:47:40.649173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-19T14:29:54.770524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-19T14:47:40.931456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

TimestampKaupunkiIkäSukupuoliTyökokemusTyösuhteen luonneTyöaikaRooliEtäKuukausipalkkaVuositulotKilpailukykyinenTyöpaikkaVapaa sana
02021-02-15 11:57:08.316PK-Seutu31-35 vNaN10.0Työntekijä / palkollinen1.0Arkkitehti50/506500.083000.0TrueNaNNaN
12021-02-15 11:57:19.676Turku31-35 vmies14.0Työntekijä / palkollinen1.0full-stackEtä5000.062500.0TrueNaNNaN
22021-02-15 11:58:03.592PK-Seutu26-30 vmies2.0Työntekijä / palkollinen1.0Full-stack ohjelmistokehittäjäEtä2475.030000.0FalseNaNNaN
32021-02-15 11:58:15.261Tampere31-35 vmies22.0Yrittäjä1.0web-arkkitehtiEtä4300.0100000.0TrueNaNNaN
42021-02-15 11:58:16.983PK-Seutu26-30 vmies2.0Työntekijä / palkollinen1.0OhjelmistokehittäjäEtä3000.037500.0FalseNaNNaN
52021-02-15 11:58:49.454PK-Seutu41-45 vmies23.0Työntekijä / palkollinen1.0OhjelmistokehittäjäNaN8000.0100000.0TrueNaNNaN
62021-02-15 12:00:03.771PK-Seutu31-35 vmies10.0Freelancer1.0OhjelmistokehittäjäEtä6000.0140000.0TrueNaNNaN
72021-02-15 12:00:04.655Tampere31-35 vNaN10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäNaN4250.054000.0TrueNaNNaN
82021-02-15 12:01:00.769Tampere31-35 vmies6.0Työntekijä / palkollinen1.0Lead developerNaN4000.050000.0FalseNaNNaN
92021-02-15 12:02:03.577Tallinna31-35 vmies12.0Freelancer1.0NaNEtäNaN200000.0TrueQuestradeNaN

Last rows

TimestampKaupunkiIkäSukupuoliTyökokemusTyösuhteen luonneTyöaikaRooliEtäKuukausipalkkaVuositulotKilpailukykyinenTyöpaikkaVapaa sana
4092021-02-19 14:54:21.221Tampere36-40 vNaN12.0Työntekijä / palkollinen1.0OhjelmistosuunnittelijaNaN3800.050000.0FalseNaNNaN
4102021-02-19 15:01:20.423Turku31-35 vmies9.0Työntekijä / palkollinen1.0Full-stack ohjelmistokehittäjäNaN3900.052000.0FalseNaNNaN
4112021-02-19 15:06:06.295PK-Seutu36-40 vnainen14.0Työntekijä / palkollinen1.0Senior consultantEtä8500.0100000.0TrueSulavaNaN
4122021-02-19 15:13:51.743Pori36-40 vmies8.0Työntekijä / palkollinen1.0Tech LeadEtä5080.065000.0FalseIso konsulttitaloSijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.
4132021-02-19 15:24:01.085Tampere36-40 vmies14.0Työntekijä / palkollinen1.0OhjelmistotestaajaEtä4100.055000.0TrueNaNNaN
4142021-02-19 15:34:53.741Tampere26-30 vmuu7.0Työntekijä / palkollinen1.0Full-stack developer50/505550.069400.0TrueNaNNaN
4152021-02-19 15:40:16.336PK-Seutu26-30 vmies5.0Työntekijä / palkollinen0.8Full-stack/mobiili/designEtä7000.090000.0TrueMavericksNaN
4162021-02-19 16:04:50.348Tampere36-40 vmies16.0Työntekijä / palkollinen1.0OhjelmistokehittäjäNaN4800.065000.0TrueNaNBonukset riippuu firman tuloksesta. Palkka olisi varmastikin enemmän muualla mutta uskoakseni linjassa kollegoideni kanssa.
4172021-02-19 16:17:29.891PK-Seutu36-40 vnainen8.0Työntekijä / palkollinenNaNProduct Owner50/504500.056200.0TrueNaNNaN
4182021-02-19 16:26:32.700PK-Seutu36-40 vmies16.0Työntekijä / palkollinen1.0Mobile SWEtä8000.095000.0TrueMavericksNaN