shiny_onesampz {ABACUS}

Shiny App to Demonstrate One-Sample Z-Test

Description

An interactive Shiny app to demonstrate one-sample Z-test.

Usage

shiny_onesampz()

Details

The interactive Shiny app demonstrates the principles of the hypothesis testing of means in a one-sample design where the population variance is known. The true population parameters are provided by the user. The user changes the hypothesised population mean and other features and explores how the Z-test compares the hypothesised mean with the mean of the sample randomly drawn from the population.

The left panel includes the user inputs for Simulation Features, Population Parameters, Sample Characteristics, and Distribution Function. To use the app at first instance, just click the Update button. To alter the input values, edit the text box or move the point on the slider and explore the changes in different tabs (see below).

To obtain identical outcomes in a separate run of the app, set a common seed value at the bottom of the left panel and click Update. All subsequent updates will produce identical results provided other inputs are identical. The seed value is ignored when the option check the box to update instantly is selected.

Value

The outcomes are presented in several tabs.

Population

contains the density plots of the population and rug plots of the sample units randomly drawn from the population. It also includes the population parameter values chosen by the user.

Sample

contains the dot plot and box plot of the sample drawn randomly from the population and rug plot of the sample units. It also includes the mean and standard deviation of the random sample.

Test Statistic

contains the plot showing the mean difference between the sample mean and hypothesised mean and corresponding 95% confidence intervals (CI). The tab also contains the distribution of the test statistic t with the observed value of the test statistic and probabilities under the given value of the Type 1 error

Summary

includes the summary of the sampled data and outcomes from the one-sample Z-test. Different sections are: (1) Hypothesis, highlighting the null and alternative hypothesis; (2) Sample, tabulating the full sampled data; (3) Summary Statistics, summarising the summary information of the sample; (4) Test Statistic, presenting the outputs from the one-sample Z-test. (5) Confidence Interval, highlighting the mean difference and corresponding 95% confidence intervals (CI).

Note

https://shiny.abdn.ac.uk/Stats/apps/

Author(s)

Mintu Nath

See Also

Function in base R for normal distribution including dnorm, pnorm, qnorm, rnorm. The app shiny_onesampt performs the hypothesis testing of mean when the population variance is known.

Examples

if(interactive()){
    library(ggplot2)
    library(shiny)
    library(ABACUS)
    # Run shiny app
    shiny_onesampz()
}