6. A researcher collects data about students’ anxiety ahead of a spelling test and finds a correlation with test performance, r = .17, p = .12. Can the researcher conclude that test anxiety is significantly associated with test performance?

7. If a study found that there was a statistically significant strong positive correlatation between attitude to Maths and exam scores it could be concluded that a positive attitude causes better performance.

8. Under what circumstances is it better to use Spearman’s rho rather than Pearson’s r?

9. Please match each of the terms below with their definition.

Definition

Regression line

Residual

Coefficient of determination

Outcome variable

The variable the regression model predicts

A representation of the regression model

The difference between an actual outcome value and the value predicted by the model

Represents how much of the outcome the regression model explains

10. The following formula represents a regression model which uses the number of days a student spent revising to predict their score on a Spanish test (%). y = 5x + 15. Please match the explanations with the figures in the boxes by dragging and dropping each one – note each figure may have more than one matching explanation.

Explanation

5

15

17

The value of Y when X is 0 (intercept)

Extra % scored on test with each day spent revising

The number of days revision which the model predicts are necessary to score 100%

The expected exam score if the student did not spend any days revising

The gradient of the regression line

11. Please check next to the statements which are true of regression lines. Note there may be more than one!

13. The ANOVA table from the Simple Linear Regression in the previous question tells us that F = 368.1, df = 1, p < .05. What does this mean?

14. Below is the SPSS output about coeffiecients for the ‘attitude to school’ and KS3 results regression.What does the model predict happens to KS3 score for each one point that the ‘attitide to school’ rating increases?

15. It is important that the residuals are normally distributed when preforming a regression analysis.

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