https://drive.google.com/file/d/1dUxaTr5f06I49TguetMjpss60g1lS58z/view?usp=sharing

Builda model to predict credit amount conditional on Duration, Checking Account, Housing, Job, and Age. Use only observations with Age > 60. Use "Mean Absolute Error" as optimization metric.

1.Write down the optimization problem. Use in your answer.

2.Write down the optimization problem for the first two observations (ID = 0 and 8); i.e. replace Xs in formula of part 6 with actual values for the first two observations.

3.Solve the model using Excel solver. Use 0 as initial value for all coefficients.

4.Solve the model using Golden Rule pf Beta.

5.Calculate t-statistics for coefficients.

6.Use coefficients that are significant at 5% level of significance to predict credit amount for observations with Age < 21. Write down the final model in the form of replacing s with your estimated values. Calculate Mean Absolute Percentage Error for these observations?

7.Using model of part 11 complete this sentence. Compare this response with response in part 4. Why are they different? Which one is more reliable?

1 unit increase in duration, changes credit amount by ...