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 4000 square foot house (in thousands of dollars). Dependent variable: Y Analysis of Variance Source Model Error C total Root MSE Dep Mean C.V. DF Variable Intercept X ㎝ 1 13 14 12.9965 88.8400 14.6291 Parameter Estimates 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 H0: 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 thousands of dollars) based on the square footage (in 100 square feet). Predict the price of a 4000 square foot house (in thousands of dollars). Dependent variable: Y Analysis of Variance Source Model Error C total Root MSE Dep Mean C.V. DF Variable Intercept X ㎝ 1 13 14 12.9965 88.8400 14.6291 Parameter Estimates 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 H0: Parameter = 0 1.239 4.887 Prob>F 0.0003 Prob > T 0.2371 0.0003
MATLAB: An Introduction with Applications
6th Edition
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Author:Amos Gilat
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Chapter1: Starting With Matlab
Section: Chapter Questions
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Solve the questiion in image. Below are the answer options:
a) 173.494
b) 285.494
c) 178.494
d) 1734.938
e) 277.494
f) None of the above
![**Title: Predicting the Selling Price of a Single-Family House Based on Square Footage**
A real estate agent seeks to predict the selling price of a single-family house using the home's square footage as a predictor. The prices are in thousands of dollars, with footage measured in 100 square feet units. Using this data, we aim to predict the price of a house with 4000 square feet.
**Analysis of Variance (ANOVA):**
- **Dependent Variable:** Y (Price in thousands of dollars)
| 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 | | | |
**Model Summary:**
- **Root Mean Square Error (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 |
**Notes:**
- **DF:** Degrees of Freedom.
- **F Value:** Indicates the ratio of the variance explained by the model to the unexplained variance.
- **Prob > F:** Probability that the observed F-value would occur by chance; lower values indicate a significant model.
- **R-Square:** Proportion of variance in the dependent variable predictable from the independent variable.
- **Adjusted R-Square](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbdf3ba5d-124c-443b-b1c7-1fc7ff72a3f7%2Fc0c6d022-4a62-4f25-9f37-6c81deefd594%2Fn3df53_processed.png&w=3840&q=75)
Transcribed Image Text:**Title: Predicting the Selling Price of a Single-Family House Based on Square Footage**
A real estate agent seeks to predict the selling price of a single-family house using the home's square footage as a predictor. The prices are in thousands of dollars, with footage measured in 100 square feet units. Using this data, we aim to predict the price of a house with 4000 square feet.
**Analysis of Variance (ANOVA):**
- **Dependent Variable:** Y (Price in thousands of dollars)
| 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 | | | |
**Model Summary:**
- **Root Mean Square Error (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 |
**Notes:**
- **DF:** Degrees of Freedom.
- **F Value:** Indicates the ratio of the variance explained by the model to the unexplained variance.
- **Prob > F:** Probability that the observed F-value would occur by chance; lower values indicate a significant model.
- **R-Square:** Proportion of variance in the dependent variable predictable from the independent variable.
- **Adjusted R-Square
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