Regression

Question 1

What is the primary purpose of regression analysis?

1) To assess the relationship between two variables
2) To predict the value of one variable based on another
3) To calculate the average deviation of data points
4) To evaluate variance within a single variable

Answer 1

To predict the value of one variable based on another

Question 2

What does a simple regression model involve?

1) Multiple predictor variables and one outcome variable
2) One predictor variable and one outcome variable
3) Only categorical variables
4) No assumptions about linearity

Answer 2

One predictor variable and one outcome variable

Question 3

What is the formula for a simple regression line?

1) y = mx + c
2) y = \beta_0 + \beta_1X + \epsilon
3) y = rX + \epsilon
4) y = \Sigma(X - Y)^2

Answer 3

y = \beta_0 + \beta_1X + \epsilon

Question 4

In regression analysis, what is the slope ( \beta_1 )?

1) The value of y when x = 0
2) The change in the outcome variable for a one-unit change in the predictor variable
3) The measure of error in the prediction
4) The standard deviation of the predictor variable

Answer 4

The change in the outcome variable for a one-unit change in the predictor variable

Question 5

What does the intercept ( \beta_0 ) represent in a regression equation?

1) The variability of the outcome variable
2) The predicted value of y when x = 0
3) The strength of the predictor variable
4) The error term in the model

Answer 5

The predicted value of y when x = 0

Question 6

What is R^2 in regression analysis?

1) The coefficient of correlation
2) The proportion of variance in the outcome variable explained by the predictors
3) The standard error of the mean
4) The slope of the regression line

Answer 6

The proportion of variance in the outcome variable explained by the predictors

Question 7

What does a p -value indicate in regression analysis?

1) The magnitude of the slope ( \beta_1 )
2) Whether the predictor variable significantly predicts the outcome variable
3) The total error in the model
4) The standard deviation of the residuals

Answer 7

Whether the predictor variable significantly predicts the outcome variable

Question 8

What does a multiple regression model include?

1) Only one predictor variable
2) Two or more predictor variables predicting one outcome variable
3) Equal weights for all predictor variables
4) Only categorical outcome variables

Answer 8

Two or more predictor variables predicting one outcome variable

Question 9

What is the difference between unstandardized ( \beta ) and standardized ( b ) coefficients?

1) Unstandardized coefficients are based on z-scores, while standardized coefficients are not
2) Standardized coefficients allow for comparisons between predictors on different scales
3) Standardized coefficients measure the overall model fit
4) There is no difference between the two

Answer 9

Standardized coefficients allow for comparisons between predictors on different scales

Question 10

What does a negative beta value indicate in regression analysis?

1) A lack of relationship between variables
2) A negative relationship where the outcome decreases as the predictor increases
3) Poor model fit
4) High error variance

Answer 10

A negative relationship where the outcome decreases as the predictor increases

Question 11

What type of regression is used to predict resilience from extraversion?

1) Multiple regression
2) Simple regression
3) Logistic regression
4) Polynomial regression

Answer 11

Simple regression

Question 12

What does F -value in regression output indicate?

1) The overall significance of the regression model compared to the mean
2) The magnitude of the correlation coefficient
3) The total variance within the predictor variable
4) The individual significance of each predictor

Answer 12

The overall significance of the regression model compared to the mean

Question 13

If R^2 = 0.40 in a multiple regression model, what does this mean?

1) 40% of the variance in the outcome variable is explained by the predictors
2) 40% of the predictor variables are significant
3) The model explains 60% of the outcome variance
4) The predictors have no significant impact

Answer 13

40% of the variance in the outcome variable is explained by the predictors

Question 14

In multiple regression, what does p < 0.05 for a predictor indicate?

1) The predictor has no significant effect on the outcome variable
2) The predictor significantly contributes to the model
3) The predictor is the strongest contributor in the model
4) The predictor has a negative relationship with the outcome variable

Answer 14

The predictor significantly contributes to the model

Question 15

What does the error term ( \epsilon ) in regression represent?

1) The predicted value of the outcome variable
2) The residual difference between observed and predicted values
3) The average slope of the regression line
4) The proportion of unexplained variance

Answer 15

The residual difference between observed and predicted values

Question 16

What does the term “line of best fit” mean in regression?

1) The line that minimizes the total error between observed and predicted values
2) The steepest line through the data points
3) The line that maximizes the correlation coefficient
4) The line that perfectly matches all observed data

Answer 16

The line that minimizes the total error between observed and predicted values

Question 17

What is a key assumption of linear regression?

1) The outcome variable is categorical
2) The relationship between variables is linear
3) All predictors have the same level of variance
4) The outcome variable has no variability

Answer 17

The relationship between variables is linear

Question 18

What does it mean if R^2 = 0.00 in a regression model?

1) The predictors explain none of the variance in the outcome variable
2) The predictors perfectly explain the variance in the outcome variable
3) The model has no predictors
4) The model is invalid

Answer 18

The predictors explain none of the variance in the outcome variable

Question 19

How is multiple regression different from simple regression?

1) Multiple regression uses categorical predictors, while simple regression uses continuous predictors
2) Multiple regression includes more than one predictor variable
3) Simple regression uses standardized coefficients, while multiple regression uses unstandardized coefficients
4) Multiple regression assumes a non-linear relationship

Answer 19

Multiple regression includes more than one predictor variable

Question 20

In regression reporting, what does F(2,45) = 15.56, p < .001 indicate?

1) The model includes two predictors and explains significant variance in the outcome
2) The model is insignificant and poorly fits the data
3) The slope of the regression line is negative
4) There is no significant relationship between predictors and outcome

Answer 20

The model includes two predictors and explains significant variance in the outcome