Can someone help me brainstorm some topics on what type of data I can use for regression analysis project for a managerial economics class? I am having a difficult time thinking of topics and finding data. Please help me with some real life examples.

This is the project guideline:

The project component of your grade is meant to encourage you to think about real world applications of the concepts learned in class. You will have to collect business or economic data on some quantitative variables and propose a hypothesis regarding the relationship between them, based on theories and concepts learned in the class. Using multivariate regression analysis and appropriate terminology, you will test your hypothesis and interpret the results.

Your project should include the following sections:

1. Introduction

a. Introduce your idea, describe the theoretical relationship you want to explore. What is the question you are trying to answer or the hypothesis you want to test? Here is where you can include references to news or journal articles you read on the topic, podcast, or books. To help find reliable sources, make sure to use our

2. Analysis

Start by talking about your data sources (make sure to cite them properly, using APA guidelines) and describe the variables you are interested in. At a minimum, you need two independent variables (and one dependent variable). These will be your explanatory variables (X variables in the regression analysis), used to explain the variation in your dependent variable (Y variable in the regression analysis).

1. You should include summary statistics (mean, median, and standard deviation) and a scatter plot, as a way of visually identifying the relationship between the variables of interest.
2. Based on the graphical analysis, you can talk about whether you think the relationship is linear or nonlinear (you can do a linear regression or log-linear regression based on the graph). Depending on what variables you are interested in, a cubic relationship might be appropriate (if you are looking at a cost function, for example). You can also try different regression models and see which one provides a better fit for the data.

3. Results and Conclusion

1. In this section you should interpret the results of your preferred regression model,
2. including the intercept, regression coefficients, p-value, R2value, and F-statistic. Are your results in line with the prediction you made in the introduction? If not, what are some possible explanations?
3. Conclude by summing up your main findings and lessons learned by performing the analysis.