PSY230-CH12QT

What is the formula for the proportionate reduction in error?

(SSTotal - SSError) / SSTotal

When drawing a regression line for a linear prediction rule, the minimum number of predicted points on a graph that must be located is

2

If a regression line goes up three units for every one unit it moves across going from left to right, b = __________.

3

If a person's score on a questionnaire has been found to predict observed social skills, and the linear prediction rule uses a regression constant of 16 and a regression coefficient of 3, the predicted level of social skills for a person with a score of 10 on the questionnaire is

46

If a child psychologist reports that age in months predicts appetite level for a group of infants using a linear prediction rule in which a = 1 and b = 2, the appetite level for a four-month-old infant is

9

In a bivariate linear prediction, the null hypothesis is that

B=0

Which of the following is true about hypothesis testing for a linear prediction rule?

If the correlation coefficient is significant, the regression coefficient will be significant

Which limitation is applicable to both correlation and regression?

Nothing can be inferred about the direction of causality.

What is the difference between the SSError and SSTotal?

SSError uses the prediction rule; SSTotal predicts from the mean.

The sum of the squared errors when predicting from the mean is called

SStotal

What does it mean when SSTotal minus SSError equals zero?

This is the worst case—it means the prediction model has reduced zero error.

Error in regression is figured by

Y - y

The term in a linear prediction rule that represents the intercept of a regression line is

a

When making predictions using a linear prediction rule, the baseline number that is added to each prediction is

a

Why are errors squared in a regression?

because summing positive and negative errors will cancel them out

When a person's score on one variable is used to make predictions about a person's score on another variable, the procedure is called

bivariate prediction.

If a counseling psychologist wants to predict college grades from high school grades, college grades are the

criterion variable.

The sum of squared errors is the sum of

each score on the criterion variable minus the predicted score, squared.

Which assumption is applicable to regression but not to correlation?

error scores are normally distributed

On a scatter diagram, the vertical distance between the dot for the actual score and the regression line represents the

error.

In a linear prediction rule using a standardized regression coefficient,

for each increase of one standard deviation in the predictor variable, the predicted standard deviation of the criterion variable increases by the standardized regression coefficient.

A regression coefficient indicates

how many units of change in the predicted value of the criterion variable for each unit of change in the predictor variable.

The multiple correlation coefficient of a criterion variable with two predictor variables

is usually smaller than the sum of the correlation coefficients of the criterion variable with each predictor variable

The statistical procedure used to make predictions about people's poetic ability based on their scores on a general writing ability test and their scores on a creativity test is

multiple regression

In prediction, error is the person's actual score minus the person's __________.

predicted score

The regression coefficient is the number multiplied by the score on the predictor variable to get the __________ on the criterion variable

predicted score

If a cognitive psychologist is studying whether difficulty doing a task predicts the amount of activity in a certain brain area, task difficulty is the __________ variable, and brain activity is the __________ variable.

predictor, independent; criterion, dependent

Prediction is also called __________.

regression

The number multiplied by the score on the predictor variable to get the predicted score on the criterion variable is the

regression coefficient

The slope of the regression line corresponds to the_______

regression coefficient

A person's predicted score on the criterion variable is found by multiplying the person's score on the predictor variable by a particular number called a

regression coefficient.

If every increase of one point on a test-anxiety scale is associated with a decrease of 2 points on predicted performance on a test, 2 represents the slope, which is also the

regression coefficient.

In the equation Y = a + (b)(X), b is the symbol for the

regression coefficient.

A regression line

shows the relation between values of the predictor and criterion variables.

The best linear prediction rule is the one that has the least

squared error when predicting using that rule

The best linear prediction rule will have the smallest sum of __________. (

squared errors; squared difference between the scores and the predicted scores

If the correlation coefficient for a study is known, figuring the proportionate reduction in error requires

squaring the correlation coefficient.

The regression constant is also referred to as

the Y intercept

When multiple regression statistics are reported in a psychology research article,

the coefficients for the predictor variables usually appear in a table.

The standardized regression coefficient in a bivariate linear prediction rule equals

the correlation coefficient

The regression constant in the best linear prediction rule is

the mean of the criterion variable minus the result of multiplying the regression coefficient by the mean of the predictor variable.

The regression coefficient in the best linear prediction rule is

the sum of the products of the deviation scores, divided by the predictor variable's sum of squared deviations from the mean.

Considering the number of possible linear prediction rules for predicting Y from X, for any particular set of scores,

there is only one best rule.

In psychology research articles

when results for bivariate prediction are reported, it is most likely to be with regression lines.

The multiple regression formula with two predictor variables is

y= a + (b1)(X1) + (b2)(X2).