What kind of data do you need to run a correlation?
Interval or ratio.
What kind of data do you need to run a regression?
Interval or ratio.
What is our effect size measure for a correlation?
The correlation itself (r).
Name this equation: y-hat = a + bx
Linear regression equation – the equation for a straight line.
What is Pearson’s r?
• Measure of the linear association between variables (the extent the plotted data form a straight line).
Why would you use a regression instead of a correlation?
Varies.
⚠️ • Consider: Is there a difference between causation and correlation?
What is our effect size measure for a regression
Coefficient of determination (R2; R-squared).
How do you calculate degrees of freedom for a correlation?
df = n - 2
What is a positive correlation? What is a negative correlation?
As one variable increases, the other increases; as one variable increases, the other decreases.
In a regression, what do we call the IV? The DV?
X and Y
What are our effect size measures for correlations?
The correlation is an effect size measure. We also use the coefficient of determination.
What is b in the following regression equation: y-hat = a + bx
The slope of the line
What two pieces of information does the correlation coefficient provide?
1.) Strength (i.e., magnitude) of the relationship
2.) Direction of the relationship
True or False: You can only have one predictor in a regression.
❌ False.
What is the coefficient of determination if r = .3?
It’s r-squared so it is...
• R2 = .09.
What is a in the following regression equation:
y-hat = a + bx
The y-intercept.
What does a correlation of 0 mean?
There is no LINEAR relationship between variables. (But there could still be a nonlinear relationship.)
Can you ever include a categorical variable in a regression? If so, how?
Dummy code
If the coefficient of determination (r-squared) is .4, we have a _____ effect.
Large.
Coefficient of Determination Thresholds (r-squared):
• Small = 0.01
• Medium = 0.09
• Large = 0.25
The formula for Pearson’s r is comprised of _____ and _____.
1.) Variances
2.) Covariances