Suppose you work in a real estate firm and your manager ask you to utilize the historical data
assigned to your team to evaluate the relationship between house price, beds, baths, sqft_home,
sqft_lot, and build year, and then predict the house price based on given information. Please
following the order of the questions below for the analysis.
1. Calculate and interpret the correlation coefficient between house price and beds, house
price and baths, house price and sqft_home, house price and sqft_lot, and house price and
build year (15 pts in total; 3 pts for each correlation).
2. Among the five correlations your team calculated from Question 1, choose the one that has
the highest positive correlation with house price to conduct a hypothesis test. At the 5%
significance level, determine whether the correlation coefficient is significant. Please
clearly state the null and alternative hypothesis, show how your team test the hypothesis,
and provide a clear conclusion based on your hypothesis test results (20 pts).
3. Using Sqft_home as explanatory variable to conduct a simple linear regression, and
interpret the results regarding the relationship between house sale price and Sqft_home (15
pts).
4. Using Sqft_home and Baths as explanatory variables to conduct a multiple linear
regression. Interpreting the results regarding the relationship between house sale price,
Sqft_home, and baths and identifying which factor has more impacts on house sale price
(15 pts).
5. Based on the regression results from Question 3 & 4, find the model that best predicts the
house sale price of a house. Use goodness-of-fit measures to find the appropriate
explanatory variables (15 pts).
6. Based on the model you choose from Question 5, predict the house sale price if a house
has 3000 Sqft within the house with 3 baths (10 pts).
7. Please make sure that the finished project is professional, including a cover page with your
team and all team members’ name, course name and number, semester, submission date,
Excel dataset, etc. (10 pts)