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?

What is the 95% confidence interval for the slope?

Which condition for inference about the slope is violated?

What does S = 4.2 represent in this context?

What is the appropriate conclusion for this test?

What is the best interpretation of the slope 0.085?

Why is a prediction interval wider than a confidence interval for the mean response?

Which statement is best supported by the output?

Why should caution be used when predicting the score for a student who studies 20 hours per week?

Which transformation would be most appropriate for this data?

Computer output for a regression of final exam score on midterm score gives: Slope = 0.72, SE(slope) = 0.15, t = 4.80, p < 0.001, with n = 32 students. Which of the following is a correct 95% confidence interval for the population slope?

Computer output for a regression of y on x shows: slope = 3.2, SE of slope = 1.1, n = 22. What is the test statistic for testing H₀: β = 0 and the conclusion at α = 0.05?

A residual plot from a linear regression shows a clear U-shaped pattern. What does this indicate about the regression model?