You will need to examine two of the nine sections of data:

one section of qualitative data (choose either Gender or Position)
one section of quantitative data (choose either Intrinsic or Extrinsic)
Each section should include all data points listed in the column for the variable. The requirements include:
Identify the data you selected.
Explain why the data was selected.
Explain what was learned by examining these sets of data.
Your analysis should include using Microsoft Excel to obtain information about the data through the use of three measures of central tendency (mean, median, mode).
Your analysis should also include the use of two measures of variability (standard deviation and variance). Some measures are appropriate for qualitative data, and some are appropriate for quantitative data.
If a measure is not applicable, then explain why.
You will have to also provide one chart/graph for each of the results of the two processed sections of data (2 total), such as a pie or bar chart or a histogram. (A table is not a chart/graph.) Ensure that you label the chart/graph clearly.
You will then need to discuss what you additionally learned from the results of this process.
Explain why charts/graphs are important in conveying information in a visual format and why standard deviation and variation are important.

We have no idea of what data you will select or why you will select them.

Measures of variability indicate the level of accuracy of the measures of central tendency.

To answer your question, I will explain the steps to analyze the selected data sections, discuss the insights gained, and explain the importance of charts/graphs and measures of central tendency and variability.

1. Selecting the Data:
First, choose one section of qualitative data (e.g., Gender) and one section of quantitative data (e.g., Intrinsic or Extrinsic). These sections should include all the data points listed in the respective variable columns. The choices for these sections are usually based on the research question or the need for analysis.

2. Explanation for Data Selection:
The data selection is guided by the research focus or the need to analyze and understand specific variables. For example, analyzing gender distribution can provide insights into gender equality in an organization. Analyzing intrinsic or extrinsic motivation can help understand the factors influencing employee satisfaction.

3. Insights from Data Analysis:
By examining these sets of data, you can observe patterns, trends, and relationships. For qualitative data like gender, you can determine the proportion of males and females or identify any gender imbalances. For quantitative data like intrinsic or extrinsic motivation, you can assess the range, distribution, and overall level of satisfaction among employees.

4. Measures of Central Tendency:
In Microsoft Excel, calculate three measures of central tendency: mean, median, and mode. These measures summarize the dataset's location (central values). Mean refers to the average value, median represents the middle value when the data is arranged in order, and mode refers to the most frequently occurring value. Calculate these measures separately for each data section.

5. Measures of Variability:
Calculate two measures of variability: standard deviation and variance. Standard deviation measures the dispersion or variability of data around the mean, while variance quantifies the average of squared deviations. Determine which measures of variability are appropriate for the selected data sections: qualitative or quantitative.

6. Charts/Graphs:
Create one chart/graph for each of the processed data sections. Use appropriate chart types, like pie charts (for qualitative data) or bar charts/histograms (for quantitative data). Ensure the charts/graphs are labeled clearly with relevant titles, axes, and data points.

7. Additional Insights:
Discuss any additional insights gained from the analysis. These could include identifying gender disparities in certain positions or understanding the variation in satisfaction levels due to intrinsic or extrinsic factors. These insights can assist in decision-making, resource allocation, or planning for organizational improvement.

8. Importance of Charts/Graphs:
Charts/graphs are important in conveying information visually because they make complex data more accessible and understandable. They provide a clear representation of patterns, trends, and comparisons. Visuals are effective for communicating findings to stakeholders, facilitating data-driven discussions, and supporting decision-making processes.

9. Importance of Standard Deviation and Variability:
Standard deviation and variance are crucial measures of variability. They help quantify the spread or dispersion of data points around the central tendency, providing insights into the dataset's variability. Understanding this variability is essential for assessing the reliability of data and making informed decisions. For qualitative data, measures of central tendency may not be applicable, and instead, other qualitative analysis techniques should be employed.

By following these steps and considering the importance of graphs/charts and measures of central tendency and variability, you can effectively analyze the selected data and gain valuable insights.