Question
Construct a linear regression model that predicts the price of houses in a neighborhood in Canton given their area in square feet and their age
Construct a linear regression model that predicts the price of houses in a neighborhood in Canton given their area in square feet and their age in years
| House price in $1000s | Square Feet | House age (Years) |
| 245 | 1400 | 15 |
| 312 | 1600 | 17 |
| 279 | 1700 | 13 |
| 308 | 1875 | 24 |
| 199 | 1100 | 25 |
| 219 | 1550 | 20 |
| 405 | 2350 | 19 |
| 324 | 2450 | 12 |
| 319 | 1425 | 14 |
| 255 | 1700 | 16 |
(a) Define the design matrix and the true response.
(b) Complete the following tables (Show your work for partial credits). Interpret the results.
| E[Coefficients] | Variance of Coefficients | |
| Intercept | ||
| Square Feet | ||
| House age |
| df | SS | MS | |
| Regression | |||
| Residual | |||
| Total |
| R Squared | |
| Adjusted R Squared | |
| AIC | |
| Standard Error | |
| Number of Observations |
(c) Construct proper hypothesis tests to identify the factors that significantly change (statistically) the house price.
(d) Find the expected values and prediction intervals for the following:
| Square Feet | House age (Years) | Expected Price | Lower Prediction bound | Upper Prediction bound |
| 1700 | 13 | |||
| 2400 | 17 | |||
| 4000 | 5 |
(e) Interpret the results in the table above.
(f) Check and discuss the potential of multicollinearity between your input factors.
Step by Step Solution
There are 3 Steps involved in it
Step: 1
Get Instant Access with AI-Powered Solutions
See step-by-step solutions with expert insights and AI powered tools for academic success
Step: 2
Step: 3
Ace Your Homework with AI
Get the answers you need in no time with our AI-driven, step-by-step assistance
Get Started
Dynamic Business Law
Authors: Nancy Kubasek, M. Neil Browne, Daniel Herron, Lucien Dhooge, Linda Barkacs
6th Edition
1260733971, 978-1260733976
