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ased on the square footage (in 100 square feet). Predict the price of a 3000 square foot house (in thousands of d 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

ased on the square footage (in 100 square feet). Predict the price of a 3000 square foot house (in thousands of d 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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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 - **Dependent Mean**: 88.8400 - **Coefficient of Variation (C.V.)**: 14.6291 - **R-square**: 0.6476 - **Adjusted 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 | This data shows the results of a linear regression analysis used to predict house prices based on their square footage. The model is statistically significant as indicated by the F-statistic and Prob>F value. The R-squared value suggests that approximately 64.76% of the variability in house prices is explained by the model. The parameter estimates provide the regression equation, which can be used to make predictions.
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 - **Dependent Mean**: 88.8400 - **Coefficient of Variation (C.V.)**: 14.6291 - **R-square**: 0.6476 - **Adjusted 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 | This data shows the results of a linear regression analysis used to predict house prices based on their square footage. The model is statistically significant as indicated by the F-statistic and Prob>F value. The R-squared value suggests that approximately 64.76% of the variability in house prices is explained by the model. The parameter estimates provide the regression equation, which can be used to make predictions.
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