PSYC 210 Exam 2 Review Questions and Answers |
2026 Updated | 100% Correct
correlations - ANSWER-relationship
does not equal causation
pearson's r - ANSWER-correlation coefficient
variables' data at the equal-interval or ratio level
E(ZxZy)/N
types of correlations - ANSWER-positive (direct)
negative (indirect)
curvi-linear (variables can still be related)
none
positive correlation - ANSWER-direct
as one variable increases, the other increases
negative correlation - ANSWER-indirect
as one variable increases, the other decreases
R square - ANSWER-the amount of variance explained by our predictor
p-value - ANSWER-significance
significant if less than .05 or 5%
chance of making a type 1 error
, simple regression - ANSWER-prediction
using 1 variable to predict another
if 2 variables are correlated, we should be able to use one to predict the other
assumes relationship can be explained with a straight line
predictor - ANSWER-x
iv
used to predict
criterion - ANSWER-y
dv
what is being predicted
simple standardized regression equation - ANSWER-Zyhat=beta(Zx)
simple raw score regression equation - ANSWER-yhat=bx+a
b (slope) - ANSWER-r(SDy/SDx)
a (constant) - ANSWER-My-b(Mx)
multiple regression - ANSWER-multiple predictors and 1 criterion
trying to explain more of the variance
multiple standardized regression equation - ANSWER-Zyhat=beta1(Zx1)+beta2(Zx2)
multiple raw score regression equation - ANSWER-yhat=b1x1+b2x2+a
b (not the slope) - ANSWER-unstandardized regression coefficient
beta - ANSWER-standardized regression coefficient
does not equal r
line of best fit - ANSWER-no other line with fit the data better
the distances from the data points to the line will be at a minimum
mean minimizes - ANSWER-E(x-m)squared
the sum of the squared deviations between the score and the mean
regression line minimizes - ANSWER-E(y-yhat)squared
SSresidual
the sum of the squared deviations between the actual values and the predicted values
the distances from the dots to the line, squared
prediction error - ANSWER-y-yhat
the difference between the actual score and the predicted score
2026 Updated | 100% Correct
correlations - ANSWER-relationship
does not equal causation
pearson's r - ANSWER-correlation coefficient
variables' data at the equal-interval or ratio level
E(ZxZy)/N
types of correlations - ANSWER-positive (direct)
negative (indirect)
curvi-linear (variables can still be related)
none
positive correlation - ANSWER-direct
as one variable increases, the other increases
negative correlation - ANSWER-indirect
as one variable increases, the other decreases
R square - ANSWER-the amount of variance explained by our predictor
p-value - ANSWER-significance
significant if less than .05 or 5%
chance of making a type 1 error
, simple regression - ANSWER-prediction
using 1 variable to predict another
if 2 variables are correlated, we should be able to use one to predict the other
assumes relationship can be explained with a straight line
predictor - ANSWER-x
iv
used to predict
criterion - ANSWER-y
dv
what is being predicted
simple standardized regression equation - ANSWER-Zyhat=beta(Zx)
simple raw score regression equation - ANSWER-yhat=bx+a
b (slope) - ANSWER-r(SDy/SDx)
a (constant) - ANSWER-My-b(Mx)
multiple regression - ANSWER-multiple predictors and 1 criterion
trying to explain more of the variance
multiple standardized regression equation - ANSWER-Zyhat=beta1(Zx1)+beta2(Zx2)
multiple raw score regression equation - ANSWER-yhat=b1x1+b2x2+a
b (not the slope) - ANSWER-unstandardized regression coefficient
beta - ANSWER-standardized regression coefficient
does not equal r
line of best fit - ANSWER-no other line with fit the data better
the distances from the data points to the line will be at a minimum
mean minimizes - ANSWER-E(x-m)squared
the sum of the squared deviations between the score and the mean
regression line minimizes - ANSWER-E(y-yhat)squared
SSresidual
the sum of the squared deviations between the actual values and the predicted values
the distances from the dots to the line, squared
prediction error - ANSWER-y-yhat
the difference between the actual score and the predicted score
