A regression model to predict Y, the state burglary rate per 100.000 people, used the following four state predictors: X₁ = median age, X₂ = number of bankruptcies per 1,000 population, X₂ = federal expenditures per capita (a leading predictor), and X₁ = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 50 people, calculate the calc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your answers to 4 decimal places.) Predictor Intercept AgeMed Bankrupt FedSpend HSGrad% Answer is complete but not entirely correct. tcalc 5.2526 -2.1764✔ Coefficient 4,198.5808 -27.3540 17.4893 -0.0124 -29.0314 SE 799.3395 12.5687 12.4033 0.0176 7.1268 p-value 0.0000 0.0348 1.4101 0.2935 -0.7045 0.4848 -4.0736✔ 0.0002

MATLAB: An Introduction with Applications
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Chapter1: Starting With Matlab
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A regression model to predict \( Y \), the state burglary rate per 100,000 people, used the following four state predictors: 

- \( X_1 \) = median age
- \( X_2 \) = number of bankruptcies per 1,000 population
- \( X_3 \) = federal expenditures per capita (a leading predictor)
- \( X_4 \) = high school graduation percentage.

\[ \text{(Click here for the Excel Data File)} \]

**(a)** Using a sample size of 50 people, calculate the \( t_{\text{calc}} \) and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your answers to 4 decimal places.)

| Predictor | Coefficient | SE     | \( t_{\text{calc}} \) | p-value |
|-----------|-------------|--------|----------------|---------|
| Intercept | 4,188.5808  | 799.3395 | 5.2526         | 0.0000  |
| AgeMed    | -27.3540    | 12.5887 | -2.1764        | 0.0348  |
| Bankrupt  | 17.4893     | 12.4033 | 1.4101         | 0.2385  |
| FedSpend  | -0.0124     | 0.0176  | -0.7045        | 0.4848  |
| HSGrad%   | -29.0314    | 7.1268  | -4.0736        | 0.0002  |

**Note:** The answer is complete but not entirely correct.
Transcribed Image Text:A regression model to predict \( Y \), the state burglary rate per 100,000 people, used the following four state predictors: - \( X_1 \) = median age - \( X_2 \) = number of bankruptcies per 1,000 population - \( X_3 \) = federal expenditures per capita (a leading predictor) - \( X_4 \) = high school graduation percentage. \[ \text{(Click here for the Excel Data File)} \] **(a)** Using a sample size of 50 people, calculate the \( t_{\text{calc}} \) and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your answers to 4 decimal places.) | Predictor | Coefficient | SE | \( t_{\text{calc}} \) | p-value | |-----------|-------------|--------|----------------|---------| | Intercept | 4,188.5808 | 799.3395 | 5.2526 | 0.0000 | | AgeMed | -27.3540 | 12.5887 | -2.1764 | 0.0348 | | Bankrupt | 17.4893 | 12.4033 | 1.4101 | 0.2385 | | FedSpend | -0.0124 | 0.0176 | -0.7045 | 0.4848 | | HSGrad% | -29.0314 | 7.1268 | -4.0736 | 0.0002 | **Note:** The answer is complete but not entirely correct.
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