h2(#description). Description This template will return descriptive statistics of a numerical or frequency table of a categorical variable. h3(#gender-gender). _gender_ ("Gender") The dataset has _709_ observations with _673_ valid values (missing: _36_).
Frequency table: Gender
gender N % Cumul. N Cumul. %
male 410 60.92 410 60.92
female 263 39.08 673 100
Total 673 100 673 100
The most frequent value is _male_. h4(#charts). Charts "!plots/Descriptives-1.png(Barplot: Gender)!":plots/Descriptives-1-hires.png h2(#description-1). Description This template will return descriptive statistics of a numerical or frequency table of a categorical variable. h3(#age-age). _age_ ("Age") The dataset has _709_ observations with _677_ valid values (missing: _32_). h4(#base-statistics). Base statistics
Descriptives: Age
Variable mean sd var
Age 24.57 6.849 46.91
The "standard deviation":http://en.wikipedia.org/wiki/Standard_deviation equals to _6.849_ (variance: _46.91_), which shows the unstandardized degree of "homogenity":http://en.wikipedia.org/wiki/Homogeneity_(statistics): how much variation exists from the average. The "expected value":http://en.wikipedia.org/wiki/Mean is around _24.57_, somewhere between _24.06_ and _25.09_ with the standard error of _0.2632_. The highest value found in the dataset is _58_, which is exactly _3.625_ times higher than the minimum (_16_). The difference between the two is described by the "range":http://en.wikipedia.org/wiki/Range_(statistics): _42_. h4(#chart). Chart A "histogram":http://en.wikipedia.org/wiki/Histogram visually shows the "distribution":http://en.wikipedia.org/wiki/Probability_distribution of the dataset based on artificially allocated "frequencies":http://en.wikipedia.org/wiki/Statistical_frequency. Each bar represents a theoretical interval of the data, where the height shows the count or density. "!plots/Descriptives-2.png(Histogram: Age)!":plots/Descriptives-2-hires.png If we _suppose_ that _Age_ is not near to the "normal distribution":http://en.wikipedia.org/wiki/Normal_distribution (see for example "skewness":http://en.wikipedia.org/wiki/Skewness: _1.925_, "kurtosis":http://en.wikipedia.org/wiki/Kurtosis: _4.463_), checking the median (_23_) might be a better option instead of the mean. The "interquartile range":http://en.wikipedia.org/wiki/Interquartile_range (_6_) measures the statistics dispersion of the variable (similar to standard deviation) based on median. h2(#description-2). Description This template will return descriptive statistics of a numerical or frequency table of a categorical variable. h3(#hp). _hp_ The dataset has _32_ observations with _32_ valid values (missing: _0_). h4(#base-statistics-1). Base statistics
Descriptives: hp
Variable mean sd var
hp 146.7 68.56 4701
The "standard deviation":http://en.wikipedia.org/wiki/Standard_deviation equals to _68.56_ (variance: _4701_), which shows the unstandardized degree of "homogenity":http://en.wikipedia.org/wiki/Homogeneity_(statistics): how much variation exists from the average. The "expected value":http://en.wikipedia.org/wiki/Mean is around _146.7_, somewhere between _122.9_ and _170.4_ with the standard error of _12.12_. The highest value found in the dataset is _335_, which is exactly _6.442_ times higher than the minimum (_52_). The difference between the two is described by the "range":http://en.wikipedia.org/wiki/Range_(statistics): _283_. h4(#chart-1). Chart A "histogram":http://en.wikipedia.org/wiki/Histogram visually shows the "distribution":http://en.wikipedia.org/wiki/Probability_distribution of the dataset based on artificially allocated "frequencies":http://en.wikipedia.org/wiki/Statistical_frequency. Each bar represents a theoretical interval of the data, where the height shows the count or density. "!plots/Descriptives-3.png(Histogram: hp)!":plots/Descriptives-3-hires.png If we _suppose_ that _hp_ is not near to the "normal distribution":http://en.wikipedia.org/wiki/Normal_distribution (see for example "skewness":http://en.wikipedia.org/wiki/Skewness: _0.726_, "kurtosis":http://en.wikipedia.org/wiki/Kurtosis: _-0.1356_), checking the median (_123_) might be a better option instead of the mean. The "interquartile range":http://en.wikipedia.org/wiki/Interquartile_range (_83.5_) measures the statistics dispersion of the variable (similar to standard deviation) based on median.
This report was generated with "R":http://www.r-project.org/ (3.0.1) and "rapport":http://rapport-package.info/ (0.51) in _1.105_ sec on x86_64-unknown-linux-gnu platform. !images/logo.png!