Unit 9: Inference for Quantitative Data: Slopes

In inference for regression, the null hypothesis [math] means:

The [math]-statistic for testing the significance of the slope is:

The conditions for inference about a regression slope include all of the following EXCEPT:

A confidence interval for the population slope [math] is:

If a 95% confidence interval for the slope is [math], what can we conclude?

The standard error of the slope [math] depends on:

To check the equal variance condition for regression, you should examine:

The quantity [math] in regression output (residual standard error) estimates:

If a regression model has a small [math]-value for the slope but low [math], this means:

In a hypothesis test, the p-value is 0.03 and the significance level is α = 0.05. What is the correct conclusion?

The power of a hypothesis test is defined as:

When should you use a t-distribution instead of a z-distribution for a confidence interval for a mean?

A researcher wants to compare the mean test scores of two independent groups. The appropriate test is:

In a chi-square test for independence with a 3 × 4 table, the degrees of freedom are:

Statistical significance means that:

Which of the following is NOT a condition for performing a one-sample t-test?

Which of the following would increase the power of a hypothesis test?

In inference for the slope of a regression line, the null hypothesis H₀: β = 0 means:

The p-value of a test is 0.04. Which of the following is the correct interpretation?

Is there convincing evidence that the slope is different from zero?