To test whether the regression coefficient \( \beta \) is significantly different from 0, you will typically conduct a hypothesis test using the t-statistic. The null hypothesis \( H_0 \) states that \( \beta = 0 \), indicating that price has no effect on sales, while the alternative hypothesis \( H_a \) states that \( \beta \neq 0 \).
Since this is a two-tailed test at a significance level of \( \alpha = 0.05 \), you will be looking for critical t-values that correspond to this significance level. Here are the steps to find the critical t-values:
-
Determine the degrees of freedom (df). For simple linear regression with one predictor, the degrees of freedom can be calculated as:
\[ df = n - 2 \]
where \( n \) is the number of observations. In your case, \( n = 6 \), so:
\[ df = 6 - 2 = 4 \]
-
Find the critical t-values for a two-tailed test at \( \alpha = 0.05 \) with 4 degrees of freedom. You can look these values up in a t-distribution table or use statistical software.
For a two-tailed test with \( \alpha = 0.05 \) and \( df = 4 \):
- The critical t-values can be found by looking up \( \alpha/2 = 0.025 \) in a t-table.
Using a t-table or calculator:
- The critical t-value for \( df = 4 \) and \( \alpha = 0.025 \) (each tail) is approximately \( \pm 2.776 \).
Thus, the critical values to test the regression coefficient \( \beta \) at the \( \alpha = 0.05 \) level are:
Critical values: \( -2.776 \) and \( +2.776 \)