For the sample data use Excel to find the mean price, square footage, number of bedrooms, and number of bathrooms. Price Square Feet Beds Baths Average $507, 502.78 1876.56 ft. 3.11 1.88 2. Use Excel to estimate the following four models. Round the coefficients to the nearest whole number. (a) Model 1: 171332 + 179 * Sqft
I ONLY NEED THE ANSWER TO PART 7!!!!!!!!!!!!!
1. For the sample data use Excel to find the mean price, square footage, number of bedrooms, and number of bathrooms.
| Price | Square Feet | Beds | Baths | ||
| Average | $507, 502.78 | 1876.56 ft. | 3.11 | 1.88 |
2. Use Excel to estimate the following four models. Round the coefficients to the nearest whole number.
(a) Model 1:
171332 + 179 * Sqft
(b) Model 2:
154402 + 96 * Sqft + 91909 * baths
(c) Model 3:
160656 + 27438 * beds + 139458 * baths
(d) Model 4:
153348 + 96 * Sqft + 557 * beds + 92023 * baths
3. For Model 4 in (d) above, interpret the coefficients for SqFt, Bed, and Bath.
96 Sqft: This means that every additional square foot added is related to a $96 increase in price, as long as beds and baths are constant.
557 beds: This means that every additional bed is related to a $557 increase in price, as long as square feet and baths are constant.
92023 baths: This means that every additional bath is related to a $92023 increase in price, as long as square feet and beds are constant.
4. (a) Fill in the table below with the Standard Error of the estimate and Adjusted given by Excel for each model.
| Standard Error | Adjusted | |
| Model 1 | 87111.55 | 0.5921 |
| Model 2 | 73840.98 | 0.7069 |
| Model 3 | 81863.78 | 0.6398 |
| Model 4 | 74984.98 | 0.6978 |
(b) Based on you answer in (a) determine the best model for predicting Price. Explain briefly in complete sentences.
The best model for predicting Price is Model 2. This is because Model 2 has the lowest standard error and the highest adjusted r squared value. This means that Model 2 is the most accurate and precise in predicting the price of a home based on the other variables (square feet, beds, and baths).
5. Use each model to estimate the price of a 2000 square foot house with 3 bedrooms and 2 bathrooms.
(a) Model 1
171332 + 179 * Sqft -----> 171332 + 179 * 2000 -------> $529,332
(b) Model 4
153348 + 96 * Sqft + 557 * beds + 92023 * baths --> 153348 + 96 * 2000 + 557 * 3 + 92023 * 2 ---------> $531,065
6. For the multiple regression model determine which variables are individually significant in predicting Price at the 5% significance level. Explain briefly in complete sentences.
(a) Model 3
The p-value for bed is equal to 0.1646 and the p-value for bath is 3.3763 E-08. If the p-value is less than 0.05, then the variable is considered significant. Since the value for bed is greater than 0.05, we can conclude that it is insignificant. However, since bath is less than 0.05, it is considered significant.
(b) Model 4
The p-value for bed is 0.9782, the p-value for bath is 0.0008, and the p-value for sqft is 0.0107. For the same reasons as listed above, we can conclude that the p-values for bath and sqft are significant since they are lesser than 0.05.
7. For each multiple regression model, conduct a test of joint significance at the 5% significance level to determine if the variables are jointly significant predictors of Price. Explain briefly in complete sentences.
(a) Model 2
(b) Model 3
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