h2(#description). Description This template will run a Kolmogorov-Smirnov-test h4(#introduction). Introduction "Kolmogorov-Smirnov test":http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test is one of the most widely used "nonparametric tests":http://en.wikipedia.org/wiki/Non-parametric_statistics. With the help of that in this case we use to check if two continuous variables had the same distribution. We do not test that here, but there is a possibility to use that in the way to check if a sample/variable followed an expected distribution. h4(#distributions). Distributions Before we use the K-S test to look at the possible statistical differences, it could be useful to see visually the distributions we want to observe. Below lie the "Cumulative Distribution Functions":http://en.wikipedia.org/wiki/Cumulative_distribution_function of the variables we compared: "!plots/KolmogorovSmirnovTest-1.png!":plots/KolmogorovSmirnovTest-1-hires.png "!plots/KolmogorovSmirnovTest-2.png!":plots/KolmogorovSmirnovTest-2-hires.png h4(#test-results). Test results Now we will test if the Internet usage for educational purposes (hours per day) and the Age had statistically the same distribution.
Two-sample Kolmogorov-Smirnov test on Internet usage for educational purposes (hours per day) and Age
Test statistic P value Alternative hypothesis
1 _0_ * * * two-sided
The requirements of the Kolmogorov-Smirnov Test test was not met, the approximation may be incorrect. So the variables do not follow the same distribution, according to the Kolmogorov-Smirnov test statistic. h2(#description-1). Description This template will run a Kolmogorov-Smirnov-test h4(#introduction-1). Introduction "Kolmogorov-Smirnov test":http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test is one of the most widely used "nonparametric tests":http://en.wikipedia.org/wiki/Non-parametric_statistics. With the help of that in this case we use to check if two continuous variables had the same distribution. We do not test that here, but there is a possibility to use that in the way to check if a sample/variable followed an expected distribution. h4(#distributions-1). Distributions Before we use the K-S test to look at the possible statistical differences, it could be useful to see visually the distributions we want to observe. Below lie the "Cumulative Distribution Functions":http://en.wikipedia.org/wiki/Cumulative_distribution_function of the variables we compared: "!plots/KolmogorovSmirnovTest-3.png!":plots/KolmogorovSmirnovTest-3-hires.png "!plots/KolmogorovSmirnovTest-4.png!":plots/KolmogorovSmirnovTest-4-hires.png h4(#test-results-1). Test results Now we will test if the cyl and the carb had statistically the same distribution.
Two-sample Kolmogorov-Smirnov test on cyl and carb
Test statistic P value Alternative hypothesis
0.625 _7.453e-06_ * * * two-sided
The requirements of the Kolmogorov-Smirnov Test test was not met, the approximation may be incorrect. So the variables do not follow the same distribution, according to the Kolmogorov-Smirnov test statistic.
This report was generated with "R":http://www.r-project.org/ (3.0.1) and "rapport":http://rapport-package.info/ (0.51) in _0.729_ sec on x86_64-unknown-linux-gnu platform. !images/logo.png!