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A real estate agent would like to predict the selling price of a single-family house by predicting the price (in $1000) based on the square footage (in 100 square feet). Calculate a 95% confidence interval for the slope of the true regression line. Dependent variable: Y Analysis of Variance Source Model Error C total Root MSE Dep Mean C.V. DF 1 13 14 12.9965 88.8400 14.6291 Parameter Estimates Variable Intercept X DF 1 1 Sum of Squares 4034.4144 2195.8215 6230.2360 R-square Adj R-square Parameter Estimate 18.3538 3.8785 Mean Square 4034.4144 168.9093 0.6476 0.6204 Standard Error 14.8077 0.7936 F Value 23.885 T for HO: Parameter = 0 1.239 4.887 Prob>F 0.0003 Prob > T 0.2371 0.0003

A real estate agent would like to predict the selling price of a single-family house by predicting the price (in $1000) based on the square footage (in 100 square feet). Calculate a 95% confidence interval for the slope of the true regression line. Dependent variable: Y Analysis of Variance Source Model Error C total Root MSE Dep Mean C.V. DF 1 13 14 12.9965 88.8400 14.6291 Parameter Estimates Variable Intercept X DF 1 1 Sum of Squares 4034.4144 2195.8215 6230.2360 R-square Adj R-square Parameter Estimate 18.3538 3.8785 Mean Square 4034.4144 168.9093 0.6476 0.6204 Standard Error 14.8077 0.7936 F Value 23.885 T for HO: Parameter = 0 1.239 4.887 Prob>F 0.0003 Prob > T 0.2371 0.0003

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 thw attached image question. Below are the answer choices:

a) [0.864, 6.893]
b) [2.164, 5.593]
c) [−3.066, 10.823]
d) [1.164, 6.593]
e) [1.909, 5.848]
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 $1000) based on the square footage (in 100 square feet). Calculate a 95% confidence interval for the slope of the true regression line. **Dependent variable: Y** **Analysis of Variance** - **Source** - **DF** - Model: 1 - Error: 13 - C Total: 14 - **Sum of Squares** - Model: 4034.4144 - Error: 2195.8215 - C Total: 6230.2360 - **Mean Square** - Model: 4034.4144 - Error: 168.9093 - **F Value:** 23.885 - **Prob > F:** 0.0003 **Root MSE:** 12.9965 **Dep Mean:** 88.8400 **C.V.:** 14.6291 **R-square:** 0.6476 **Adj R-square:** 0.6204 **Parameter Estimates** - **Variable** - Intercept - DF: 1 - Parameter Estimate: 18.3538 - Standard Error: 14.8077 - T for H0: 1.239 - Prob > T: 0.2371 - X - DF: 1 - Parameter Estimate: 3.8785 - Standard Error: 0.7936 - T for H0: 4.887 - Prob > T: 0.0003 Explanation of Results: - The analysis of variance (ANOVA) table shows a significant F-value (23.885) with a p-value of 0.0003, indicating that the model is statistically significant. - The R-square value (0.6476) suggests that about 64.76% of the variability in the selling price can be explained by the square footage. - The parameter estimate for the slope (X) is 3.8785, with a statistically significant p-value of 0.0003, indicating a strong relationship between square footage and selling price.
Transcribed Image Text:A real estate agent would like to predict the selling price of a single-family house by predicting the price (in $1000) based on the square footage (in 100 square feet). Calculate a 95% confidence interval for the slope of the true regression line. **Dependent variable: Y** **Analysis of Variance** - **Source** - **DF** - Model: 1 - Error: 13 - C Total: 14 - **Sum of Squares** - Model: 4034.4144 - Error: 2195.8215 - C Total: 6230.2360 - **Mean Square** - Model: 4034.4144 - Error: 168.9093 - **F Value:** 23.885 - **Prob > F:** 0.0003 **Root MSE:** 12.9965 **Dep Mean:** 88.8400 **C.V.:** 14.6291 **R-square:** 0.6476 **Adj R-square:** 0.6204 **Parameter Estimates** - **Variable** - Intercept - DF: 1 - Parameter Estimate: 18.3538 - Standard Error: 14.8077 - T for H0: 1.239 - Prob > T: 0.2371 - X - DF: 1 - Parameter Estimate: 3.8785 - Standard Error: 0.7936 - T for H0: 4.887 - Prob > T: 0.0003 Explanation of Results: - The analysis of variance (ANOVA) table shows a significant F-value (23.885) with a p-value of 0.0003, indicating that the model is statistically significant. - The R-square value (0.6476) suggests that about 64.76% of the variability in the selling price can be explained by the square footage. - The parameter estimate for the slope (X) is 3.8785, with a statistically significant p-value of 0.0003, indicating a strong relationship between square footage and selling price.
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