1. Identify the dependent variable in the above data
2. Is this a Time-Series Data? Why or Why Not?
3. If you consider only the baseline what is the R2 of the model?
4. Run the raw regression and note whether the regression model is better than the baseline or not?
5. Identify the significant variables in the raw regression (one variable in each line)?
6. Which of the following assumptions are not fulfilled in the raw model?
a. Normality
b. Homoscedasticity
c. Multicollinearity
d. Absence of Correlated Errors
e. Linearity
7. Which variables exhibit multicollinearity and why?
8. Which variables exhibit non-linearity and why?
9. Examine the residual plot and note your observations below
10. Modify the model and obtain your best model. What is its R2 and Adj R2?
11. Now Set seed by considering any number. Using the data mining approach obtain your best model and test it on testing data. Compare your models in terms of R2, Adj R2 and RMSE. Note the results below:
12. Compare the model obtained in Question 10 with that of Question 11 and note your observations below:
13. Write down your best model below:
14. If the residual plot shows autocorrelation then what steps can you take to overcome it?1. Identify the dependent variable in the above data
2. Is this a Time-Series Data? Why or Why Not?
3. If you consider only the baseline what is the R2 of the model?
4. Run the raw regression and note whether the regression model is better than the baseline or not?
5. Identify the significant variables in the raw regression (one variable in each line)?
6. Which of the following assumptions are not fulfilled in the raw model?
a. Normality
b. Homoscedasticity
c. Multicollinearity
d. Absence of Correlated Errors
e. Linearity
7. Which variables exhibit multicollinearity and why?
8. Which variables exhibit non-linearity and why?
9. Examine the residual plot and note your observations below
10. Modify the model and obtain your best model. What is its R2 and Adj R2?
11. Now Set seed by considering any number. Using the data mining approach obtain your best model and test it on testing data. Compare your models in terms of R2, Adj R2 and RMSE. Note the results below:
12. Compare the model obtained in Question 10 with that of Question 11 and note your observations below:
13. Write down your best model below:
14. If the residual plot shows autocorrelation then what steps can you take to overcome it?1. Identify the dependent variable in the above data
2. Is this a Time-Series Data? Why or Why Not?
3. If you consider only the baseline what is the R2 of the model?
4. Run the raw regression and note whether the regression model is better than the baseline or not?
5. Identify the significant variables in the raw regression (one variable in each line)?
6. Which of the following assumptions are not fulfilled in the raw model?
a. Normality
b. Homoscedasticity
c. Multicollinearity
d. Absence of Correlated Errors
e. Linearity
7. Which variables exhibit multicollinearity and why?
8. Which variables exhibit non-linearity and why?
9. Examine the residual plot and note your observations below
10. Modify the model and obtain your best model. What is its R2 and Adj R2?
11. Now Set seed by considering any number. Using the data mining approach obtain your best model and test it on testing data. Compare your models in terms of R2, Adj R2 and RMSE. Note the results below:
12. Compare the model obtained in Question 10 with that of Question 11 and note your observations below:
13. Write down your best model below:
14. If the residual plot shows autocorrelation then what steps can you take to overcome it?1. Identify the dependent variable in the above data
2. Is this a Time-Series Data? Why or Why Not?
3. If you consider only the baseline what is the R2 of the model?
4. Run the raw regression and note whether the regression model is better than the baseline or not?
5. Identify the significant variables in the raw regression (one variable in each line)?
6. Which of the following assumptions are not fulfilled in the raw model?
a. Normality
b. Homoscedasticity
c. Multicollinearity
d. Absence of Correlated Errors
e. Linearity
7. Which variables exhibit multicollinearity and why?
8. Which variables exhibit non-linearity and why?
9. Examine the residual plot and note your observations below
10. Modify the model and obtain your best model. What is its R2 and Adj R2?
11. Now Set seed by considering any number. Using the data mining approach obtain your best model and test it on testing data. Compare your models in terms of R2, Adj R2 and RMSE. Note the results below:
12. Compare the model obtained in Question 10 with that of Question 11 and note your observations below:
13. Write down your best model below:
14. If the residual plot shows autocorrelation then what steps can you take to overcome it?
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