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Solve the attached questions. Below are its answer choices: a) 238.709 b) 1347.088 c) 236.709 d) 139.709 e) 134.709 f) None of the above

Solve the attached questions. Below are its answer choices: a) 238.709 b) 1347.088 c) 236.709 d) 139.709 e) 134.709 f) None of the above

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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Solve the attached questions. Below are its answer choices:

a) 238.709
b) 1347.088
c) 236.709
d) 139.709
e) 134.709
f) None of the above
A real estate agent would like to predict the selling price of a single-family house by predicting the price (in thousands of dollars) based on the square footage (in 100 square feet). Predict the price of a 3000 square foot house (in thousands of dollars). **Dependent variable:** Y **Analysis of Variance** | Source | DF | Sum of Squares | Mean Square | F Value | Prob > F | |--------|----|----------------|-------------|---------|----------| | Model | 1 | 4034.4144 | 4034.4144 | 23.885 | 0.0003 | | Error | 13 | 2195.8215 | 168.9093 | | | | C total| 14 | 6230.2360 | | | | - **Root MSE:** 12.9965 - **Dep Mean:** 88.8400 - **C.V.:** 14.6291 - **R-square:** 0.6476 - **Adj R-square:** 0.6204 **Parameter Estimates** | Variable | DF | Parameter Estimate | Standard Error | T for H0: Parameter = 0 | Prob > T | |-----------|----|---------------------|----------------|-------------------------|----------| | Intercept | 1 | 18.3538 | 14.8077 | 1.239 | 0.2371 | | X | 1 | 3.8785 | 0.7936 | 4.887 | 0.0003 | **Explanation of Data:** This table provides statistical results from a linear regression analysis to predict house prices based on square footage. - **Analysis of Variance (ANOVA):** - **Model:** Measures the variability explained by the independent variable (square footage). - **Error:** Represents the variability not explained by the model. - **F Value and Prob > F:** Indicate the model’s overall significance. A low p-value (0.0003) suggests the model is statistically significant. - **Parameter Estimates:** - **Intercept:** The estimated selling price when square footage is zero. - **X (Square footage):** The increase in the selling price for each additional
Transcribed Image Text:A real estate agent would like to predict the selling price of a single-family house by predicting the price (in thousands of dollars) based on the square footage (in 100 square feet). Predict the price of a 3000 square foot house (in thousands of dollars). **Dependent variable:** Y **Analysis of Variance** | Source | DF | Sum of Squares | Mean Square | F Value | Prob > F | |--------|----|----------------|-------------|---------|----------| | Model | 1 | 4034.4144 | 4034.4144 | 23.885 | 0.0003 | | Error | 13 | 2195.8215 | 168.9093 | | | | C total| 14 | 6230.2360 | | | | - **Root MSE:** 12.9965 - **Dep Mean:** 88.8400 - **C.V.:** 14.6291 - **R-square:** 0.6476 - **Adj R-square:** 0.6204 **Parameter Estimates** | Variable | DF | Parameter Estimate | Standard Error | T for H0: Parameter = 0 | Prob > T | |-----------|----|---------------------|----------------|-------------------------|----------| | Intercept | 1 | 18.3538 | 14.8077 | 1.239 | 0.2371 | | X | 1 | 3.8785 | 0.7936 | 4.887 | 0.0003 | **Explanation of Data:** This table provides statistical results from a linear regression analysis to predict house prices based on square footage. - **Analysis of Variance (ANOVA):** - **Model:** Measures the variability explained by the independent variable (square footage). - **Error:** Represents the variability not explained by the model. - **F Value and Prob > F:** Indicate the model’s overall significance. A low p-value (0.0003) suggests the model is statistically significant. - **Parameter Estimates:** - **Intercept:** The estimated selling price when square footage is zero. - **X (Square footage):** The increase in the selling price for each additional
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