1. Introduction: Summarize what topic you are analyzing. What is the dependent variable, y, you are trying to explain, and what are the main independent variables, x’s, you are using. Briefly explain...

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1. Introduction: Summarize what topic you are analyzing. What is the dependent variable, y, you are trying to explain, and what are the main independent variables, x’s, you are using. Briefly explain any additional background information that might be relevant.
2. Body: In this section, you want to explain the steps you used to run your regression and how you determined the final equation format. Essentially, you might want to achieve a high R2 and have statistically significant values for all the independent variables you add. However, adding some variables might not help your R2, or you might run into multicollinearity problems. In addition, you might want to do some residual analysis (discussed in the multiple linear regression chapter, although I did not cover it). Looking at the residuals will tell you if a linear regression is appropriate or not. Adding dummies will help, both for seasonal adjustments (if any) and for any relevant categorical data. In this section you want to show your regression results and comment on any hypothesis testing for the coefficients. Depending on your R2 you might even be able to do some forecasting and say something about the forecasting accuracy.
3. Conclusion: Summarize your results. Perhaps some parts are inconclusive, and that’s ok, as long as you interpret appropriately your regression results and you tried alternative ways of setting your regression equation.
Answered Same DayMay 13, 2021

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Suraj answered on May 14 2021
23 Votes

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Topic: Multiple Regression Analysis
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Introduction: In today’s generation making a prediction is common and also very important aspect...

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