Correlation + Regression Flashcards
Similarities between correlation and regression
- Both analyze relationships between variables, but they differ in their purpose and interpretation.
Differences between correlation and regression
- Correlation measures the strength and direction of a linear relationship between two variables, represented by a single value between -1 and 1
- Regression, on the other hand, explores how one variable influences another, providing an equation that describes the relationship and allows for prediction; SUSPECTED OR LOGICAL DIRECTION
What does regression allow us to do?
Allows us to add predictors to explain more variance
On a chart, what are the x and y labels
- Criterion on Y
- Predictor on X
The average (squared) deviation from each person’s score Y to their predicted score Y(hat) is called the…
STANDARD ERROR OF THE ESTIMATE
Regression Equation - UNSTANDARDIZED VERSION:
- Uses raw scores
- Y(hat) = a + bX
- For multiple predictors: add on more bX
Regression Equation - UNSTANDARDIZED VERSION: B
B = r(SDy/SDx)
How can we interpret “b”?
For every one unit change in X, predict a b unit change in Y
Regression Equation - UNSTANDARDIZED VERSION: A
A = My - b(Mx)
Where can we find “a” in SPSS?
“CONSTANT”
How can we interpret “a”?
The predicted score on Y when the X = 0
Regression Equation - STANDARDIZED VERSION:
- SAME - but uses z scores
- Z(sub y hat) = BetaZx
- For multiple predictors: add on more betas
- Predicted score on Y when X = 0 is always 0 when both variables are z scores, because the mean of both sets of z scores is 0
- USE: if given someone’s z score(s) on predictor variables, use the standardized equation
Which component of the SPSS output do we use for the unstandardized regression equation?
UNSTANDARDIZED COEFFICIENTS
Which component of the SPSS output do we use for the standardized regression equation?
STANDARDIZED COEFFICIANTS
How do we report the null hypothesis for correlation?
H0 : ρ = 0
MULTIPLE REGRESSION
What is multiple regression asking?
How well can we predict scores on Y using two (or more) predictor variables
Multiple regression UNSTANDARDIZED equation
Y(hat) = a + b1X1 + b2X2
What does b1 and b2 represent?
- how well does variable X1 predict scores on Y, independent of X2
- how well does variable X2 predict scores on Y, independent of X1
What does R2 represent in relation to b1 and b2
R2: how well do both of these predictors, combined, predict scores on Y
Multiple regression STANDARDIZED equation
- Z(hat)y = B1Zx1 + B2Zx2
- NOTE: B = beta
How can we interpret the UNSTANDARDIZED multiple regression coefficient
- For every one score increase in ___, predict a ____(constant) increase in ____, holding constant the effect of ____ (the other b)
How can we interpret the STANDARDIZED multiple regression coefficient
For every one standard deviation increase in ___, predict a ____(constant) increase in ____, holding constant the effect of ____ (the other Zx2)