Overview
In each module, you will be learning about different statistical functions in R. You will apply these functions to specific data sets, creating models that can be used to understand and solve real-world problems. You will gain practice creating models, reporting and interpreting their statistics, evaluating their significance, and using the models to make predictions.
Note: Begin working on the readings and the problem set early each week. This will help make sure that you are prepared for the weekly discussion.
Prompt
In this activity, you will explore a second order regression model that contains quantitative and qualitative variables. Then you will be asked to create your own second order regression models and write a mini-report based on your findings.
Access the R scripts for this problem set by using the Jupyter Notebook link in Module Three. In your Jupyter Notebook, you have been given a set of steps that explains how to create second order models with quantitative and qualitative variables. Go through each step, examining the scripts and their output. If you are not sure how a specific script works or how to understand the output of a script, review the readings. Reach out to your instructor if you need additional help.
Review the Module Three Problem Set Report template to understand the questions that you will need to answer for this assignment. Then, write your own scripts to create the models specified in your problem set report. Refer to the scripts that you were given as examples to guide your work.
Use the outputs of your scripts to answer all of the questions in your problem set report. The report has been divided into several sections. Each section contains questions to guide your analysis. Be sure to fully answer all of the questions and complete the following sections:
Introduction: Communicate all ideas by presenting the context of your analyses.
Correlation Analysis: Discuss the relationships between the variables using correlation coefficients.
Reporting Results: Report the results of the model by listing and interpreting various model statistics.
Evaluating Model Significance: Evaluate the significance of the model by reporting parameter estimates and performing hypothesis testing for each estimate and the overall model.
Making Predictions Using the Model: Make predictions based on the model by reporting prediction values and constructing prediction intervals and confidence intervals.
Conclusion: Communicate all ideas by summarizing and interpreting the practical implications of the results.
