userfriendlyscience (version 0.1-2)

normalityAssessment: normalityAssessment

Description

This function can be used to assess whether a variable and the sampling distribution of its mean have an approximately normal distribution.

Usage

normalityAssessment(sampleVector, samples = 5000, digits=3,
                    samplingDistColor = "#2222CC",
                    normalColor = "#00CC00",
                    samplingDistLineSize = 2,
                    normalLineSize = 1,
                    xLabel.sampleDist = NULL,
                    yLabel.sampleDist = NULL,
                    xLabel.samplingDist = NULL,
                    yLabel.samplingDist = NULL)

Arguments

sampleVector

Numeric vector containing the sample data.

samples

Number of samples to use when constructing sampling distribution.

digits

Number of digits to use when printing results.

samplingDistColor

Color to use when drawing the sampling distribution.

normalColor

Color to use when drawing the standard normal curve.

samplingDistLineSize

Size of the line used to draw the sampling distribution.

normalLineSize

Size of the line used to draw the standard normal distribution.

xLabel.sampleDist

Label of x axis of the distribution of the sample.

yLabel.sampleDist

Label of y axis of the distribution of the sample.

xLabel.samplingDist

Label of x axis of the sampling distribution.

yLabel.samplingDist

Label of y axis of the sampling distribution.

Value

• An object with several results, the most notably of which are:
• plot.sampleDist
  Histogram of sample distribution
• sw.sampleDist
  Shapiro-Wilk normality test of sample distribution
• ad.sampleDist
  Anderson-Darling normality test of sample distribution
• ks.sampleDist
  Kolmogorov-Smirnof normality test of sample distribution
• kurtosis.sampleDist
  Kurtosis for sample distribution
• skewness.sampleDist
  Skewness for sample distribution
• plot.samplingDist
  Histogram of sampling distribution
• sw.samplingDist
  Shapiro-Wilk normality test of sampling distribution
• ad.samplingDist
  Anderson-Darling normality test of sampling distribution
• ks.samplingDist
  Kolmogorov-Smirnof normality test of sampling distribution
• kurtosis.samplingDist
  Kurtosis for sampling distribution
• skewness.samplingDist
  Skewness for sampling distribution

Details

This function provides a number of normality tests and draws histograms of the sample data and the sampling distribution of the mean (most statistical tests assume the latter is normal, rather than the first; normality of the sample data guarantees normality of the sampling distribution of the mean, but if the sample size is sufficiently large, the sampling distribution of the mean is approximately normal even when the sample data are not normally distributed). Note that for the sampling distribution, the degrees of freedom are usually so huge that the normality tests, negligible deviations from normality will already result in very small p-values.

Examples

normalityAssessment(rnorm(35));