university of alabama Univariate OLS Regression
Project 4: Univariate OLS Regression Scenario: … New Message To: LoanTronic Consulting Group Subject: Master Dataset for Employee Engagement Good morning, Hope you had a good weekend! We have more to do, namely, exploring a different approach to evaluating the results of the Likert survey. We will assume an equal distance between each label as we previously did in assessing the overall score: Label Description Score 1 strongly disagree 0.20 2 disagree 0.40 3 neutral 0.60 4 agree 0.80 5 strongly agree 1.00 To make matters more interesting, we have a new dataset this week: demographic_detail.csv This dataset comprises the following metrics: Attribute Name Employee ID Year of Birth Time on the Job Field Name employee_id year_of_birth time_on_the_job Type int int int Categorical False Description Employee ID assigned at start of employment False Calendar year in which the employee was born False Number of months since the employee joined the company As we work through this deliverable, we need to keep in mind that our report will get attention at the most senior levels of the client’s management. Let’s do our best to impress them with a thoughtful analysis. Thank you, Tye Brandert Key Tasks: Remember, you will Python to complete this project. Depending on which you choose, there will be different ways of completing each task. Task 1: Merge the Data • • Using Python, merge the demographic_detail.csvwith the roster_with_score.csvdata. Be sure to use the employee_id as the key. Save the resulting data frame as a new csv: roster_with_score_2.csv Task 2: Exploratory Data Analysis • • • Visualize the data to prepare for exploratory analysis by using the sns.regplot method. Compute the correlation coefficients of “overall” employee results (an average of all of the results) compared with each of the two new variables. Interpret the results of your analysis. What do you notice? Task 3: Perform Regression Analysis • You can perform the simple OLS regression of overall satisfaction compared with each of the variables available in the new dataset. Note that you will need to perform two univariate regressions. Using the statsmodels OLS regression package, perform an OLS regression, and use the “summary” output to produce metrics to assess the quality of the regression and of the regressor coefficients. Task 4: Analysis • Determine which of the two regressions produces better results. Be sure to explain the metric(s) you are using to make the determination. • Inspect the residuals of the two regressions and describe your observations. o Are the residuals normally distributed? o Do you notice any significant outliers? o Do you observe anything else of concern? Task 5: Recommendations: Make recommendations for the client around your findings. • • Interpret the data to determine any relationships that might be present. Is there a relationship between the age of employees and their satisfaction survey results? What about job tenure? Recommend actions, based on your interpretations, that the client can take to improve employee satisfaction. What to Submit: • For submissions, please provide a Jupyter notebook in HTML format with annotations that explain your work It is good practice to preface each code snippet in your Jupyter notebook with an explanation of what you are trying to achieve using Markdown annotations. You should also take the habit of annotating the results / output generated by your code. These annotations make your Jupyter notebook easier to consume for others. Throughout this class, you will be asked to annotate your notebooks clearly so that your work can be interpreted. • Naming Convention for File Attachments: Please name your dataset as “the file name_initials of your name_the last four digits of the student id” Example: roster_with_score_2_jd_0123.csv Where “jd” is initials for student “John Doe” and “0123” are the last four digits of John’s student id.
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