Question

The Statistics Project assignment has been broken down into multiple parts and you will complete these...

The Statistics Project assignment has been broken down into multiple parts and you will complete these parts throughout the course. This week's assignment allows you to become familiar with opening data and viewing it in Microsoft® Excel® and using the Analysis Toolpak. In research, the individual data points are entered into databases, but for the purpose of this course, the data is provided in a spreadsheet for you in the Happiness and Engagement Dataset.

Imagine you have been asked to enhance workplace happiness and engagement at your company. You have conducted a survey and gathered data on the gender, age, relationship with direct supervisor, telecommute schedule, relationship with coworkers, along with the ratings for workplace happiness and workplace engagement for 50 individuals in your department. You must determine what variables affect workplace happiness and engagement. The first step is to run descriptive statistics on each variable to learn more about the data you have collected.

Calculate descriptive statistics for the following variables in the provided Microsoft® Excel® dataset:

• Gender
• Age
• Relationship with Direct Supervisor
• Telecommute Schedule
• Relationship with Coworkers
• Workplace Happiness Rating
• Workplace Engagement Rating

Write a 125- to 175-word summary of your interpretation of the descriptive results for each variable. Copy and paste the Microsoft® Excel® output below your summary.

Note that indicate that the frequency table is best used for both nominal and nominal variables. Other information (such as mean and standard deviation) is best used with interval and ratio data.

The gender variable, the direct relationship, the peer relationship, are either secondary variables or variables (I can tell you who are secondary names and who is the ancestor after looking at the data), and the other variables seem to be space or proportion.

So you need to find frequency tables and descriptive statistics. This made it difficult for you to interpret summary statistics because nominal or abstract variables cannot be interpreted using descriptive statistics.

For example, the translation of the life expectancy variable is a life expectancy of 32 years with a standard deviation of 0.6, which means that on average everyone is 0.6 years away from the average age.

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