The following table was generated from the sample data of 10 newborn babies regarding the weight of the mother at birth, the weight of the father at birth, and the weight of the baby at birth. The dependent variable is the weight of the baby, the first independent variable (x1) is the mother's weight, and the second independent variable (x2) is the father's weight. Coefficients Standard Error t-Stat p-value Intercept 9.739264 1.337045 7.284173 0.000165 Mother's Weight -0.929448 0.137340 -6.767476 0.000261 Father's Weight 0.717791 0.159475 4.500951 0.002795 Copy Data Step 2 of 2: Indicate if any of the independent variables could be eliminated at the 0.01 level of significance. Answer E Tables E Keypad Keyboard Shortcuts O x2 O Keep all variables

MATLAB: An Introduction with Applications
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The following table was generated from the sample data of 10 newborn babies regarding the weight of the mother at birth, the weight of the father at birth, and the weight of the baby at birth. The dependent variable is the weight of the baby, the first independent variable (\(x_1\)) is the mother's weight, and the second independent variable (\(x_2\)) is the father's weight.

|                     | Coefficients | Standard Error | t-Stat   | p-value   |
|---------------------|--------------|----------------|----------|-----------|
| Intercept           | 9.739264     | 1.337045       | 7.284173 | 0.000165  |
| Mother’s Weight     | –0.929448    | 0.137340       | –6.767476 | 0.000261  |
| Father’s Weight     | 0.717791     | 0.159475       | 4.500951 | 0.002795  |

**Step 2 of 2:** Indicate if any of the independent variables could be eliminated at the 0.01 level of significance.

**Answer:**

- \(x_1\)
- \(x_2\)
- Keep all variables

**Explanation of Analysis:**

This table represents a multiple regression analysis aiming to understand the relationship between the weight of newborn babies and the weights of their parents at birth. The coefficients indicate the relationship strength and direction between each independent variable and the dependent variable. The standard error measures the accuracy of the coefficient's estimation, while the t-Stat and p-value are used to determine the statistical significance of each variable. A variable could be considered for elimination if its p-value is higher than the significance level of 0.01.
Transcribed Image Text:The following table was generated from the sample data of 10 newborn babies regarding the weight of the mother at birth, the weight of the father at birth, and the weight of the baby at birth. The dependent variable is the weight of the baby, the first independent variable (\(x_1\)) is the mother's weight, and the second independent variable (\(x_2\)) is the father's weight. | | Coefficients | Standard Error | t-Stat | p-value | |---------------------|--------------|----------------|----------|-----------| | Intercept | 9.739264 | 1.337045 | 7.284173 | 0.000165 | | Mother’s Weight | –0.929448 | 0.137340 | –6.767476 | 0.000261 | | Father’s Weight | 0.717791 | 0.159475 | 4.500951 | 0.002795 | **Step 2 of 2:** Indicate if any of the independent variables could be eliminated at the 0.01 level of significance. **Answer:** - \(x_1\) - \(x_2\) - Keep all variables **Explanation of Analysis:** This table represents a multiple regression analysis aiming to understand the relationship between the weight of newborn babies and the weights of their parents at birth. The coefficients indicate the relationship strength and direction between each independent variable and the dependent variable. The standard error measures the accuracy of the coefficient's estimation, while the t-Stat and p-value are used to determine the statistical significance of each variable. A variable could be considered for elimination if its p-value is higher than the significance level of 0.01.
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