2.1

Question 1

bi/b1

Answer 1

• Regression coefficient for the predictor
• Gradient (slope) of the regression line
• Direction/strength of relationship

Question 2

b0

Answer 2

• Intercept (value of Y when X = 0)

* Point at which the regression line crosses the Y-axis (ordinate)

Question 3

The F test

Answer 3

How much the model has improved the prediction of the outcome

Question 4

If F is high, it means that…

Answer 4

that the model means something, if not, you can just use the mean

Question 5

R^2

Answer 5

Indicates how much variation of a dependent variable is explained by the independent variable(s) in a regression model.

Only important if we want to do predictions

Question 6

R^2 high or low?

Answer 6

If high, we explain relatively much of this variation and why (in our outcome variable)

If low, not so much of the variation in why

Question 7

Interpretation of 𝜷𝑖 coefficients is “ceteris paribus”

Answer 7

other things equal

Question 8

OLS assumptions

Answer 8

• A 1: The regression model is linear in the coefficients and error term.
• A 2: Error term has a mean value of zero.
• A 3: All independent variables are uncorrelated with the error term (aka exogeneity assumption). This one is almost never met, for reasons of confounding factors such as unobserved variable bias or simultaneity bias.
• A 4: Observations of the error term are uncorrelated with each other.
• A 5: Error term has a constant variance (is homoscedastic, not heteroskedastic).

Question 9

Dependent t-test

Answer 9

• Compares two means based on related data.
• E.g., Data from the same people measured at different times.
• Data from ‘matched’ samples, they’re sometimes referred to as “matched-pairs” test or “paired t-test”.

Question 10

Independent t-test

Answer 10

• Compares two means based on independent data.
• E.g., data from different groups of people.
• Sometimes referred to as two-sample t-test.

Question 11

Both the independent t-test and the dependent t-test are parametric tests based on the normal distribution. Therefore, they assume:

Answer 11

• The sampling distribution is normally distributed. In the dependent t-test this means that the sampling distribution of the differences between scores
should be normal, not the scores themselves.

• Data are measured at least at the interval level.

Question 12

The independent t-test, because it is used to test different groups of people, also assumes:

Answer 12

• Variances in these populations are roughly equal (homogeneity of variance).
• Scores in different treatment conditions are independent.

Question 13

Effect Size Measures

r = .1 / .3 / .5

Answer 13

r = .1 (small effect):
• the effect explains 1% of the total variance.
r = .3 (medium effect):
• the effect accounts for 9% of the total variance.
r = .5 (large effect):
• the effect accounts for 25% of the variance.

Beware of these ‘canned’ effect size though
• The size of effect should be placed within the research context.