Chapter 13 & 14

Question 1

Correlation

Answer 1

systemic association between two variables on interval or ratio scales

Question 2

Correlation coefficient

Answer 2

a statistic that quantifies a relation between two variables

Question 3

Three main characteristics of a correlation coefficient

Answer 3

(1) can be either positive or negative (2) falls between -1 and 1 (3) its strength or magnitude indicates how large it is, not its sign

Question 4

Positive correlation

Answer 4

participants tend to have similar scores on both variables, with respect to the mean and spread

Question 5

Negative correlation

Answer 5

participants with high scores on one variable tend to have low scores on the other variable

Question 6

Sizes of a correlation

Answer 6

0.10 small, 0.30 medium, 0.50 large

Question 7

Three possible causal explanations for a correlation

Answer 7

(1) variable A causes variable B (2) variable B causes variable A (3) variable C causes both variables A and B

Question 8

Spurious correlation

Answer 8

two variables vary together, but there is no connection between the variables; their quantitative association is purely due to chance

Question 9

Pearson correlation coefficient

Answer 9

statistic that quantifies a linear relation between two scale variables

Question 10

Big data

Answer 10

very large data sets to which researchers apply computer technology to perform large numbers of statistical analyses, often without hypotheses guiding which analysis are conducted

Question 11

Variables used in regression

Answer 11

predictor (X) and outcome (Y) variables

Question 12

Simple linear regression

Answer 12

statistical tool that lets us predict a person’s score on an outcome variable from their score on one predictor variable

Question 13

Regression to the mean

Answer 13

tendency of scores that are particularly high or low to drift toward the mean over time

Question 14

Intercept

Answer 14

the predicted score on the outcome variable when the score on the predictor variable is zero

Question 15

Slope

Answer 15

the amount of increase in the predicted score on the outcome variable when the score on the predictor variable increases by one

Question 16

Standardized regression coefficient

Answer 16

a standardized version of the slope in a regression equation; predicted change in the outcome variable in terms of standard deviations for an increase of one in the predictor variable

Question 17

Two ways of quantifying how well the regression lines captures variability

Answer 17

standard error of the estimate and proportionate reduction in error

Question 18

Standard error of the estimate

Answer 18

statistic that indicates the average vertical distance between a regression line and the actual data points

Question 19

Proportionate reduction in error or coefficient of determination

Answer 19

statistic that quantifies how much more accurate predictions are when we use the regression line instead of the mean as a prediction tool

Question 20

Orthogonal variables

Answer 20

predictor variables that make separate and distinct contributions in the prediction of a outcome variable

Question 21

Multiple regression

Answer 21

a statistical technique that includes two or more predictor variables in a prediction equation

Question 22

What does the regression line look like when there is no relationship?

Answer 22

line is flat and anchored to the mean of Y or the outcome variable

Question 23

Regression line

Answer 23

a line of best fit that maximizes prediction of Y and minimizes error

Question 24

How is “best fit” illustrated in multiple regression?

Answer 24

not a line, but a 3D plane