Data Analysis: Descriptive Statistics and Assumption Testing

You will conduct the data analysis using Microsoft Excel Toolpak. View these links for information:
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Data Analysis: Descriptive Statistics and Assumption Testing

The reliability of a statistical parametric test’s findings is based on a variety of assumptions regarding the distribution of a set of data. They include independence, normality as well as equal variance in addition to the elimination of any outliers (Sedgwick 2012). In the event that these assumptions are not met, it impacts the accuracy of the data. This paper reviews the data sets to see if they pass various parametric checks to determine if they meet the requirements for reliable results.

Measurement Scale

The average annual number of sick days for each employee were measured in relation to microns to determine if they’re closely related. The data was presented in an interval scale as the data is classified by rank, ordered in order, as well as equally separated.

A Measurement of Central Tendency

The Microns data have an average of 5.657282 with the median was 6 and the mode was 8. The average annual number of sickness days of employees was an average of 7.126214, with a mode and median of 7. This indicates that the sets of data did not have extreme valueseven though they did have the same numbers at certain locations to indicate a possible connection (Chakrabarty 2021).

Skewness and Kurtosis

Micron’s data shows an skewness number of -0.37326 that indicates a heavily biased (left) spread. The data for the mean sick days have an skewness of 0.14225. The value indicates that the distribution of data is symmetrical, but slightly tilted towards the right. Kurtosis is the measure of how much the distribution tails are different from the normal distribution.

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