Two predictor regression: Y B1 X 1 B2 X 2 B0 B0 = The intercept.
B1 = The partial regression coefficient for the regression of Y on X1, holding X2 constant
B2 = The partial regression coefficient for the regression of Y on X2, holding X1 constant
B1 = Ea
Regression With Categorical
G mutually exclusive and exhaustive categories
gender: male, female
treatments: psychotherapy, drug treatment, control
religion: Catholic, Protestant, Jewish
an IV and DV that is
by a curved line.
Constant change in X is
NOT associated with a
constant change in Y,
regardless of the value
Multiple Regression with p
Y B0 B1 X 1 B2 X 2 . Bp X P
The regression coefficients (B1 to Bp) in the model
measures the relationship between each X and Y with the
other Xs partialled out or controlled. The intercept (B0
Suppression and Spurious Effect
Question 1: Suppose that both X1 and X2 are positively
correlated with Y. That means that if either of those variables
increases, we expect to see Y increase.
But suppose that the regression equation comes out
Sampling Distributions and
Statistical inference and hypothesis testing.
Procedure of hypothesis testing.
Hypothesis testing for mean.
Sampling distribution of mean.
Type I error rate (significance
Graphical way to explore 2-dimensional
Review of correlation.
A good way to explore the relationship between two
A scatterplot can help us det
Importance of 1-dimensional data description
Shape of univariate distributions, Normal distribution
Variability or dispersion
Skewness and kurtosis
Graphical ways to explore 1-dimensi
Can be used in survey
Why Multiple Regression (MR)?
MR is a general data-analytic system for the assessment
of the relationship of a set of variables (predictors or
independent variables) to a single variable (criterion,