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A real estate developer wishes to study the relationship between the size of home a client will purchase (in square feet) and other variables. Possible independent variables include the family income, family size, whether there is a senior adult parent living with the family (1 for yes, O for no), and the total years of education beyond high school for the husband and wife. The sample information is reported below. Family 1 2 3 4 5 6 7 8 9 10 Square Feet 2,200 2,300 3,400 3,360 3,000 2,900 4,100 2,520 4,200 2,800 Income (000s) 60.8 68.4 104.5 89.3 72.2 114 125.4 83.6 133 95 Family Size 2 3 3 4 4 3 6 3 5 3 Senior Parent 0 1 0 1 0 1 0 0 0 0 Education 4 6 7 0 2 10 6 8 2 6

A real estate developer wishes to study the relationship between the size of home a client will purchase (in square feet) and other variables. Possible independent variables include the family income, family size, whether there is a senior adult parent living with the family (1 for yes, O for no), and the total years of education beyond high school for the husband and wife. The sample information is reported below. Family 1 2 3 4 5 6 7 8 9 10 Square Feet 2,200 2,300 3,400 3,360 3,000 2,900 4,100 2,520 4,200 2,800 Income (000s) 60.8 68.4 104.5 89.3 72.2 114 125.4 83.6 133 95 Family Size 2 3 3 4 4 3 6 3 5 3 Senior Parent 0 1 0 1 0 1 0 0 0 0 Education 4 6 7 0 2 10 6 8 2 6

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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### Study on Home Purchase Size and Influencing Factors A real estate developer aims to examine the relationship between the size of a home a client purchases (measured in square feet) and various factors. Potential independent variables considered include family income, family size, the presence of a senior adult parent in the family (coded as 1 for yes and 0 for no), and the total years of education beyond high school of the husband and wife. Below is the sample dataset: | Family | Square Feet | Income (000s) | Family Size | Senior Parent | Education | |--------|-------------|---------------|-------------|---------------|-----------| | 1 | 2,200 | 60.8 | 2 | 0 | 4 | | 2 | 2,300 | 68.4 | 3 | 1 | 6 | | 3 | 3,400 | 104.5 | 3 | 0 | 7 | | 4 | 3,360 | 89.3 | 4 | 1 | 0 | | 5 | 3,000 | 72.2 | 4 | 0 | 2 | | 6 | 2,900 | 114 | 3 | 1 | 10 | | 7 | 4,100 | 125.4 | 6 | 0 | 6 | | 8 | 2,520 | 83.6 | 3 | 0 | 8 | | 9 | 4,200 | 133 | 5 | 0 | 2 | | 10 | 2,800 | 95 | 3 | 0 | 6 | [Click here for the Excel Data File](#) **Task:** Develop a suitable multiple regression equation using stepwise regression. Start by entering the number of family members first, followed by their income, and remove any variables deemed insignificant. Ensure no cells are left blank; enter "0" wherever needed. Present values for \( R \) and \( R^2 \) (adjusted) in percentage format, rounded to three decimal
Transcribed Image Text:### Study on Home Purchase Size and Influencing Factors A real estate developer aims to examine the relationship between the size of a home a client purchases (measured in square feet) and various factors. Potential independent variables considered include family income, family size, the presence of a senior adult parent in the family (coded as 1 for yes and 0 for no), and the total years of education beyond high school of the husband and wife. Below is the sample dataset: | Family | Square Feet | Income (000s) | Family Size | Senior Parent | Education | |--------|-------------|---------------|-------------|---------------|-----------| | 1 | 2,200 | 60.8 | 2 | 0 | 4 | | 2 | 2,300 | 68.4 | 3 | 1 | 6 | | 3 | 3,400 | 104.5 | 3 | 0 | 7 | | 4 | 3,360 | 89.3 | 4 | 1 | 0 | | 5 | 3,000 | 72.2 | 4 | 0 | 2 | | 6 | 2,900 | 114 | 3 | 1 | 10 | | 7 | 4,100 | 125.4 | 6 | 0 | 6 | | 8 | 2,520 | 83.6 | 3 | 0 | 8 | | 9 | 4,200 | 133 | 5 | 0 | 2 | | 10 | 2,800 | 95 | 3 | 0 | 6 | [Click here for the Excel Data File](#) **Task:** Develop a suitable multiple regression equation using stepwise regression. Start by entering the number of family members first, followed by their income, and remove any variables deemed insignificant. Ensure no cells are left blank; enter "0" wherever needed. Present values for \( R \) and \( R^2 \) (adjusted) in percentage format, rounded to three decimal
**b. Select all independent variables that should be in the final model.** *(You may select more than one answer. Single-click the box with the question mark to produce a check mark for a correct answer and double-click the box with the question mark to empty the box for a wrong answer. Any boxes left with a question mark will be automatically graded as incorrect.)* - [?] Senior parent - [✔] Square feet - [✔] Family size - [✔] Income - [ ] Education **c. Write out the regression equation.** \[ \text{Square feet} = \_\_\_\_\_\_\_ + \_\_\_\_\_\_\_ \times (\text{Family Size}) + \_\_\_\_\_\_\_ \times (\text{Income}) \] **d. Report and interpret the adjusted \( R^2 \).** \[ R^2 = \_\_\_\_\_\_\_ \% \]
Transcribed Image Text:**b. Select all independent variables that should be in the final model.** *(You may select more than one answer. Single-click the box with the question mark to produce a check mark for a correct answer and double-click the box with the question mark to empty the box for a wrong answer. Any boxes left with a question mark will be automatically graded as incorrect.)* - [?] Senior parent - [✔] Square feet - [✔] Family size - [✔] Income - [ ] Education **c. Write out the regression equation.** \[ \text{Square feet} = \_\_\_\_\_\_\_ + \_\_\_\_\_\_\_ \times (\text{Family Size}) + \_\_\_\_\_\_\_ \times (\text{Income}) \] **d. Report and interpret the adjusted \( R^2 \).** \[ R^2 = \_\_\_\_\_\_\_ \% \]
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