The CEO of X Company has requested a data analysis report to support his decision making regarding workforce diversity issues. Title VII of the Civil Rights Act requires the company to file an Equal Employment Survey. X Company is concerned they may not be meeting the diverse workforce standards. The company is a software engineering firm that started out with all male employees but have since hired women employees but they sense the ratio of women to men is low. They are also considering requesting funding for recruiting and retaining qualified minorities.
X Company increased their recruiting budget to $200,000 to focus on hiring and retaining qualified women. They will require additional funding to focus on minority hiring which requires a detailed statistical analysis of their personnel records. They will present the data analysis to the Board of Directors who will approve or disapprove this funding request.
The data based personnel records contains the following information, which we will use to run a myriad of statistical analysis, to confirm or rule out any concerns with hiring and retention of females and minorities as follows:
English as 2nd Language
Employee Satisfaction with the company
Employee Satisfaction with online training
Employee Satisfaction with traditional training methods
Workforce Diversity Issues
Regression analysis can be used for various situations. Regression analysis allows you to compare a single variable with other variables. This allows you to understand the relationship between variables better. This would allow us to determine how the various variables within the company maybe contributing to lack of diversity within the company's workforce. Additionally, it will assist to determine which variables could be focused on in order to support the recruiting efforts and establish the areas in which to develop a program to encourage recruitment and support of a diverse workforce.
In order to make recommendations, it would be beneficial to gather both cross-sectional data, and time series data. Cross-sectional data can be used to compare data across all or subgroups of employees. On the other hand, time series data will allow us to get comparable points of the data in various points in time. This will allow for a comparison of variables at these points in time to analyze the company's shifts in variables in question.
There are two data analysis techniques that would directly apply to our course project. They are:
Summarize and describe the data
Illustrate potential relationships
Summary measures are definitely necessary in any data analysis project in order to get an initial understanding of the data. A great starting point for our project would be to outline the measures of central tendency (mean, median, mode, etc). This will help us make sense of the data found in the personnel records. Then we can further describe the data through use of visualization methods (bar charts, graphs, etc). Visualization methods are important to use in our project because we will be presenting to the Board of Directors, who do not have time to sit through the entire detailed data analysis. Instead the visuals will describe the data found to them in an effective and efficient manner.
Illustrating potential relationships will be very important to our project. We will need to use both visualization and regression methods to indicate the relationships that exist within the internal data that we have access to. Regression methods should be used because we are measuring more than two variables. Instead we will be indicating relationships between many different variables (age, salary, years of experiences, cultural identity, etc)© BrainMass Inc. brainmass.com October 16, 2018, 2:19 am ad1c9bdddf - https://brainmass.com/business/business-math/summarize-and-describe-the-data-illustrate-relationships-375693
Your response is in excel. I took several steps so you will want to "travel" the tabs and see how I moved from one task to the next exploring the data.
They have a real problem but regression and overall means didn't reveal it.
Here are the questions addressed in the summary:
What have you found in the data (attached)?
Do you now see the need to revise your first technique?
Which techniques in addition to Regression ...
Your response is in excel. I took several steps so you will want to "travel" the tabs and see how I moved from one task to the next exploring the data. They have a real problem but regression and overall means didn't reveal it.