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.