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PSYC09H3 (1)

Douglas Bors (1)

Chapter 1

# PSYC09H3 Chapter Notes - Chapter 1: Confidence Interval, Sampling Error, Observational Error

by OC339945

Department

PsychologyCourse Code

PSYC09H3Professor

Douglas BorsChapter

1This

**preview**shows half of the first page. to view the full**3 pages of the document.**Chapter 1: Multiple Regression

Majorly used for prediction and casual analysis

Casual analysis: independent variables are regarded as causes of criterion (dependent

variable) determine whether a particular independent variable really affects the

dependent variable, and to estimate the magnitude of that effect, if any

Multiple regression: statistical method for studying the relationship between a single

dependent variable (criterion) and one or more independent (predictor) variables

Other Names for Multiple Regression

Ordinary least squares multiple regression

Ordinary = simple

Least squares = method used to estimate the regression equation

Multiple = two or more independent variables

Linear = kind of equation that is estimated by the multiple regression method

Regression = “regression to the mean”

Why Multiple Regression?

For prediction studies, multiple regression makes it possible to combine many variables

to produce optimal predictions of the dependent variable

For casual analysis, multiple regression separates the effects of independent variables on

the dependent variable so that you can examine the unique contribution of each variable

Why Is Regression Linear?

Means it is based on a linear equation if you graph the equation you should get a

straight line

Method of least squares is designed to find numbers that give us optimal predictions of

the dependent variable

Y = a + bx two-variable linear equation, where y is the dependent variable, x is th

independent variable, a (the intercept; value of y when x =0) and b (the slope; how big a

change in y we get for a 1-unit increase in x) are constants

What Does a Linear Equation Look Like with More Than Two Variables?

You can get better predictions of the criterion variable and your overall study if you base

them on more than one piece of information or predictor

You want to be able to look at the effect of one variable while controlling for other

variables accomplished by putting the other variables in the regression equation

Y = a +b1x1 + b2x2 general way of writing an equation with two independent

variables represented on a 3-D graph, and the equation would be represented by a

plane rather than a line

Why Does Multiple Regression Use Linear Equations?

A linear equation is the simplest way to describe a relationship between two or more

variables and still get reasonably accurate predictions

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