Regression Analysis Assignment

Regression Analysis Assignment


The multiple regression assignment has the following requirements in terms of data:

  • There should be at least 30 observations in your data set.
  • The data can be either cross-section or time series.
  • There must be some logical reason for the number of observations that you choose, i.e., do not stop at 30 observations for no reason:

Ex. – If you are using data for states, use all 50 states in the United States.

Ex. – If you are using data for the developing countries of Asia, you should use data for all 41 countries who are members of the Asian Development Bank.

Ex. – If you are using quarterly data, you should use data for eight years (32 quarters).

Ex. – If you are using monthly data, you should use data for three years (36 months).

– However, it is appropriate to start at the beginning of a year and end with the most recent data:

Ex. – 2008 Q1 to 2016 Q3 = 35 quarters

  • The minimum number of independent variables in your model is three. 
  • At least one of the independent variables must be a dummy variable.
  • No more than half of the independent variables can be dummy variables.



Use your data and a spreadsheet program(Excel) to do to perform a multiple regression analysis for your variables. The analysis should include the following:

  1. Provide a brief background of the situation you are going to look at.  Specifically, why did you choose the dependent variable you chose?  Indicate the general model that you are going to estimate. Discuss what you think the relationship is between the dependent variable and the independent variables, and what that leads you to conclude about the expected signs of the coefficients in the model.  Determine the correlation between each of the independent variables and the dependent variable.
  2. Present the estimated model.  Give an interpretation of the coefficient of determination (r2). List the adjusted r2.  Use the F-test to test the validity of the model as a whole at a 5% level of significance.
  3. Give an interpretation of each of the estimated coefficients. Do the signs of the coefficients match your expectations? At the 5% level of significance, test whether each of the variables makes a significant contribution to the model .
  4. Conduct an analysis to determine if any problems exist.
  5. Summarize the results of your analysis.  Use your model to make a prediction for any combination of values for your independent variables.
  6. Include the computer results in the form of an appendix. 


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