Multiple Linear Regression

Instructions: - Form groups of 3 or 4 students only, individual submissions are not accepted. - Due date April 27th 2021, by 12:00 midnight. - Submit your Excel file and a soft copy of the answers to the questions below via ITC. - Convert the word doc to PDF. - Both Excel and PDF files sent via ITC should be named with the group members names as follows: Diama_Muniece.xlsx. Diama_Muniece.pdf.

Suppose you are interested in predicting housing prices based on various community characteristics listed in the excel file hprice2.xls. The definition of the variables of interest is presented in the table below:

Variable Definition / Unit price median housing price, $ nox The amount of nitrous oxide, in parts per million. dist weighted distance of the community from five employment centers, in miles. rooms Average number of rooms in houses in the area. stratio Average student-teacher ratio of schools in the area.

Consider the following model: LnPrice_i= _0 + _1 Lnnox_i + _2 Lndist_i+ _3 rooms_i+_4 stratio_i+u_i 1 - Formulate a multiple linear regression model that includes all potential explanatory variables and estimate it with the given sample data.

2- Interpret the parameters estimated in the regression equation 3- Are the coefficients statistically significant? Conduct proper hypothesis testing for each coefficient. 4- Test the following hypothesis: H0: _1=-1 H1: _1 -1 5- How well does the estimated regression equation fit the data? Hint: Explain the R-square

6- Re-run the regression you estimated part 1 by removing rooms and stratio. Report your R-Square and Adjusted R-square. Comment on the figures noting which model is better.