Flashcards in Chapter 13 Regression Deck (27):

1

## Overpredicted

###
Observed values of Y at given values of X that are below the predicted values of Y.

( i.e. the, values predicted by the regression equation)

2

## Regression Coefficient

###
A measure of the relationship b/w each predictor varaible and the dependent variable in simple linear regression, this is also the slope of the regression line.

In multiple regression, the variance regression coefficients combine to create the slope of regression line.

3

## Intercept

### Point in which the regression line intersects the Y axis. Also, the value of Y when X = 0

4

## Regression Line

### The line that can be drawn through the scatter plot of the data that best " fits" the data. ( i.e. minimizes the squared deviations b/w observed values and the regression line)

5

## Residuals

### Errors in prediction. The difference b/w observed and predicted values of Y.

6

## Regression Equation

### The components, including the regression coefficients intercept, error term, and X and Y values for Y the regression line.

7

## Multiple Correlation Coefficient

### A statistic measuring the strength of the association b/w multiple independent variables, as a group and dependent variable.

8

## Multicollinearity

###
The degree of overlap among predictor variables in a multiple regression.

High multicollinearity among predictor variables can cause difficulties finding unique relations among predictors and dependant variables.

9

## Independent, Predictor Variable

### Aka: Independent Variable

10

## Dependent, Outcome, Criterion Variable

### Different terms for dependent variable

11

## Error

### Amount of difference b/w the predicted value and the observed value of the dependent variable. It is also the amt. of unexplained variance in a dependent variable,

12

## Ordinary Least Squares ( OLS)

### A common form of regression that uses the smallest sum of squared deviations to generate the regression line.

13

## Observed value

### The actual measured value of the Y variable of a given value of X

14

## Multiple Regression

### A regression model with more than one independent or predictor variable

15

## Simple Linear Regression

### The regression model employed when their is a single dependent and independent

16

## Scatterplot

###
A graphic representation of the data along 2 dimensions

( x and Y)

17

## Standardized Regression Coefficient ( R )

### The regression coefficient converted into standardized values.

18

## Slope

### The average amt. of change in the Y variable for each one unit of change in the X variable.

19

## Underpredicted

###
Observed values of Y at given values of X that are above the predicted values of Y.

( i.e. the values predicted by the regression equation)

20

## Unique Variance

### The proportion of variance in the dependent variable explained by a independent variable when controlling for all other independent variables in the model.

21

## e

### Error Mean

22

## R

### Multiple Correlation Coefficient

23

## R2

### The % of variance explained by the regression model

24

## b

### The Understandardized Regression Coefficient

25

##
^

Y

### Predicted value of Y. The Dependent Variable

26

## Y

### Observed Value of Y the Dependent Variable

27