Answer: t-statistic

## Explanation The correct answer is **A. t-statistic**. ### Why t-statistic is appropriate: 1. **Hypothesis Testing for Regression Coefficients**: When testing hypotheses about individual regression coefficients (like testing if β = 1), the appropriate test statistic is the t-statistic. 2. **Formula for t-statistic**: \[ t = \frac{\beta_{\text{estimated}} - \beta_{\text{hypothesized}}}{SE(\beta_{\text{estimated}})} = \frac{0.86 - 1}{0.8} = -0.175 \] 3. **Interpretation**: The calculated t-statistic of -0.175 would be compared to critical values from the t-distribution (typically ±1.96 for 95% confidence level). Since |t| < 1.96, we cannot reject the null hypothesis that β = 1. ### Why other options are incorrect: - **B. Chi-squared test statistic**: Used for testing variance or goodness-of-fit, not individual regression coefficients - **C. Jarque-Bera test statistic**: Used for testing normality of residuals, not hypothesis testing for regression coefficients - **D. Sum of squared residuals**: A measure of model fit, not a test statistic for hypothesis testing This t-test approach is standard in regression analysis for testing hypotheses about individual coefficients.

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