PSYC 2260 Lecture Notes - Lecture 12: Regression Analysis, Squared Deviations From The Mean, Coefficient Of Determination
26 Jun 2018
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PSYC 2260 Introduction to Research Methods in Psychology Chapter 12
Chapter 12 Prediction
Regression: when correlation is used to predict
- In correlation, order of X and Y doesn’t matter, but in regression, order is important
- X is predictor (independent); Y is criterion (dependent)
Prediction is not causation
- May “causes” between X and Y
Key points: regression allows us to form a prediction, even of we don’t know the causes
“Linear predictor rule” (formula):
- Ŷ = predicted raw score
- a = regression constant
- b = regression coefficient
- x = raw score on X
Example Ŷ = predicted GPA a = 2.0 b = .3 X = score on motivation test
- Person 1 has X = 5 Ŷ= 2.0 + (3) (5) = 3.5
- Person 2 has X = 1 Ŷ= 2.3
The Regression Line
- Visually, could plot the two predicted scores and draw a line thru them
- Then visually predict GPA for any motivation score
The Slob (B)
- For each unit increase in X, predicted Y increase by amount b
- E.g. if b= .3, of X goes up by 1, Y hoe s up by 3
The intercept of Y (A)
- Intercept (a): the baseline point at which the line intercepts the Y axis
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Document Summary
In correlation, order of x and y doesn"t matter, but in regression, order is important. X is predictor (independent); y is criterion (dependent) Key points: regression allows us to form a prediction, even of we don"t know the causes. = predicted raw score a = regression constant. = regression coefficient x = raw score on x. Example = predicted gpa a = 2. 0 b = . 3 x = score on motivation test. Person 1 has x = 5 = 2. 0 + (3) (5) = 3. 5. Person 2 has x = 1 = 2. 3. Visually, could plot the two predicted scores and draw a line thru them. Then visually predict gpa for any motivation score. For each unit increase in x, predicted y increase by amount b. E. g. if b= . 3, of x goes up by 1, y hoe s up by 3. Intercept (a): the baseline point at which the line intercepts the y axis.
