MATH 1300 week 5 Assignment: Project for module 5
- MATH 1300 Assignment: Project for module 5
Student Name
William Penn University
MATH1300
Professor Name
Submission Date
Based on the given data uploaded, conduct a regression analysis for the variable sale price using the living area. Summarize your findings, including all regular steps for regression. Also, will you conclude that a larger house corresponds to a higher price?
| Coefficientsa | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 42.569 | 67.953 | .626 | .534 | |
| LivingArea | 38.311 | 2.996 | .879 | 12.787 | <.001 | |
| a. Dependent Variable: SalePrice | ||||||
Pred y = mx+b
Pred y = 38.11x+42.569 where x is living area, y stands for sale price
If living area is 5
Then y = 38.11 (5) + 42.569
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 5015683.243 | 1 | 5015683.243 | 163.504 | <.001b |
| Residual | 1472459.757 | 48 | 30676.245 | |||
| Total | 6488143.000 | 49 | ||||
| a. Dependent Variable: SalePrice | ||||||
| b. Predictors: (Constant), LivingArea | ||||||
P< 0.001 (<0.005) regression model is significant
R.sq =.773 , (0 < R.sq < 1)
77% of variation in sell price is explained by “living area” by using our model