Testvision oefenvragen week 1
1. Which of the following statements about the F-ratio is true?
a. The F -ratio is the ratio of variance explained by the model to the error in the
model.
b. True, the F-ratio equals the mean square model - that is a measure of the
explained variance by the regression - divided by the mean square error that is a
measure of the variance that remains unexplained by the regression (or else the
error variance).
2. Which of the following statements about the t-statistic in regression is not true?
a. The t-statistic is equal to the regression coefficient divided by its standard
deviation.
b. Correct answer, this statement is incorrect as the t-statistic is equal to the
regression coefficient divided by its standard error and not the standard
deviation.
3. Which of the following statements about the F-ratio is true?
a. true. The F-statistic is simply a ratio of two variances: the explained variance
divided by the unexplained variance.
4. What does the t-statistic of a regression coefficient b test?
a. True, the t-test has the following hypotheses:
b. Η0: β=0
c. Η1: β≠0
5. The standard error of the estimate is to the regression line as the standard deviation is
to what?
a. True, it is the mean. The standard error of the estimate, sometimes also called
the root mean square error, is the standard deviation of the error term, and is
the square root of the Mean Square Residual
6. Linear regression could be used to predict someone’s score in a statistical exam score
from their mathematical ability.
, a. True: the statistical exam score is the Y, dependent variable and the
mathematical ability is the X, the independent variable.
7. In a simple regression equation, what does x denote?
a. Trus. The regression equation in formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥
b. a = intersection of line with y-axis (constant)
c. b = slope of the line: how much does y increase if x increases by 1
8. Linear regression means that for an increase in the x variable, there will be a constant
change to y.
a. True, that is exactly what it menas. Linear regression means that for an increase
in the x variable, there will be a constant change to y.
9. What does the term ‘residual sum of errors’ (SSR) in linear regression represent?
a. True, the residual sum of squares (or sum of squared errors) represents the
difference of the real data (the various points in the X-Y scatterplot) from the
regression line.
10. The slope intercept formula is:
a. True, y = mx + b (often also described as y = ax + b )
11. In the regression equation Y=a+bx, what does a denote?
a. True. The regression equation in formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥 a = intersection of line
with y-axis (constant)
b. b = slope of the line: how much does y increase if x increases by 1?
12. There is a negative relationship between number of drinks consumed and score on a
test. The first participant drinks 3 drinks. The second participant drinks 5 drinks. What will
their respective test scores be, if the intercept is 18 and the slope is –3.00?
a. True:
b. the formula is Y=constant + B. X=18-B.3 (- because of the negative association)
c. first: Y= 18-3.3= 9
d. second: Y=18-5.3= 3
,13. If there is a perfect linear relationship between X and Y, then predictions for Y will:
a. Correct, a linear relationship between X and Y means that the regression line Y =
a + bX fully describes the relationship between the 2 variables. In other words,
there is no error in the regression. The residuals are all 0.
14. Suppose that Y = a + bX is the regression equation. When b = 0, the line of best fit will
usually be:
a. True, horizontal. The slope will be zero
15. In the regression equation Y=a+bX, what does b denote?
a. Value of the slope of the line
16. What purpose does the adjusted R 2 serve?
a. True, the adjusted R² applies a correction for n and k . It assumes that the model
is defined for the population. It does not necessarily increase if you include more
independent variables in the model (as does the R2)
17. Which of the following is incorrect?
a. Correct, because the statement is false! The regression seeks actually to
minimize the distance between the regression line and the scores and not
b. maximize it.
18. The difference between each observation (i.e. hours of training and competition score)
and the model fitted to the data (i.e. all observations) is known as:
a. Residual
19. What does a in a straight line equation a+bX represent?
a. True
b. In formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥
c. a = intersection of line with y-axis (constant) .
d. b = slope of the line: how much does y increase if x increases by 1?
e. The effect of the independent variable!
, 20. The method of least squares is used in a linear regression to find out which of the
following?
a. Correct! The least squares method is a method to find the 'best fitting' line, so
the straight line that minimizes the mistake that we make in predicting the Y
variable when using the regression model.
21. What does a slope of 2.50 indicate?
a. For every increase of 1.00 on the x-axis, there is an increase of 2.50 on the y-axis.
b. Correct, the b coefficient indicates how much does y increase if x increases by 1
unit
22. In the regression equation Y=a+bX, what does Y denote?
a. Correct.
b. In formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥 .
c. y= Variable to be predicted
d. a = intersection of line with y-axis (constant)
e. b = slope of the line: how much does y increase if x increases by 1? The effect of
the independent variable!
23. Which of the following does not apply to multiple linear regression?
a. true, a multiple regression means that there is a single outcome variable and
there can be more than 2 predictors. However, there is always only one outcome
variable. For multiple outcome variables, we should use another method, e.g.
MANOVA
24. R 2 is:
a. The proportion of variance in the outcome accounted for by the predictor
variable(s).
25. A psychologist was interested in whether the amount of news people watch predicts
how depressed they are. In this table, what does the value 3.030 represent?
a. The improvement in the prediction of depression by fitting the model
1. Which of the following statements about the F-ratio is true?
a. The F -ratio is the ratio of variance explained by the model to the error in the
model.
b. True, the F-ratio equals the mean square model - that is a measure of the
explained variance by the regression - divided by the mean square error that is a
measure of the variance that remains unexplained by the regression (or else the
error variance).
2. Which of the following statements about the t-statistic in regression is not true?
a. The t-statistic is equal to the regression coefficient divided by its standard
deviation.
b. Correct answer, this statement is incorrect as the t-statistic is equal to the
regression coefficient divided by its standard error and not the standard
deviation.
3. Which of the following statements about the F-ratio is true?
a. true. The F-statistic is simply a ratio of two variances: the explained variance
divided by the unexplained variance.
4. What does the t-statistic of a regression coefficient b test?
a. True, the t-test has the following hypotheses:
b. Η0: β=0
c. Η1: β≠0
5. The standard error of the estimate is to the regression line as the standard deviation is
to what?
a. True, it is the mean. The standard error of the estimate, sometimes also called
the root mean square error, is the standard deviation of the error term, and is
the square root of the Mean Square Residual
6. Linear regression could be used to predict someone’s score in a statistical exam score
from their mathematical ability.
, a. True: the statistical exam score is the Y, dependent variable and the
mathematical ability is the X, the independent variable.
7. In a simple regression equation, what does x denote?
a. Trus. The regression equation in formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥
b. a = intersection of line with y-axis (constant)
c. b = slope of the line: how much does y increase if x increases by 1
8. Linear regression means that for an increase in the x variable, there will be a constant
change to y.
a. True, that is exactly what it menas. Linear regression means that for an increase
in the x variable, there will be a constant change to y.
9. What does the term ‘residual sum of errors’ (SSR) in linear regression represent?
a. True, the residual sum of squares (or sum of squared errors) represents the
difference of the real data (the various points in the X-Y scatterplot) from the
regression line.
10. The slope intercept formula is:
a. True, y = mx + b (often also described as y = ax + b )
11. In the regression equation Y=a+bx, what does a denote?
a. True. The regression equation in formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥 a = intersection of line
with y-axis (constant)
b. b = slope of the line: how much does y increase if x increases by 1?
12. There is a negative relationship between number of drinks consumed and score on a
test. The first participant drinks 3 drinks. The second participant drinks 5 drinks. What will
their respective test scores be, if the intercept is 18 and the slope is –3.00?
a. True:
b. the formula is Y=constant + B. X=18-B.3 (- because of the negative association)
c. first: Y= 18-3.3= 9
d. second: Y=18-5.3= 3
,13. If there is a perfect linear relationship between X and Y, then predictions for Y will:
a. Correct, a linear relationship between X and Y means that the regression line Y =
a + bX fully describes the relationship between the 2 variables. In other words,
there is no error in the regression. The residuals are all 0.
14. Suppose that Y = a + bX is the regression equation. When b = 0, the line of best fit will
usually be:
a. True, horizontal. The slope will be zero
15. In the regression equation Y=a+bX, what does b denote?
a. Value of the slope of the line
16. What purpose does the adjusted R 2 serve?
a. True, the adjusted R² applies a correction for n and k . It assumes that the model
is defined for the population. It does not necessarily increase if you include more
independent variables in the model (as does the R2)
17. Which of the following is incorrect?
a. Correct, because the statement is false! The regression seeks actually to
minimize the distance between the regression line and the scores and not
b. maximize it.
18. The difference between each observation (i.e. hours of training and competition score)
and the model fitted to the data (i.e. all observations) is known as:
a. Residual
19. What does a in a straight line equation a+bX represent?
a. True
b. In formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥
c. a = intersection of line with y-axis (constant) .
d. b = slope of the line: how much does y increase if x increases by 1?
e. The effect of the independent variable!
, 20. The method of least squares is used in a linear regression to find out which of the
following?
a. Correct! The least squares method is a method to find the 'best fitting' line, so
the straight line that minimizes the mistake that we make in predicting the Y
variable when using the regression model.
21. What does a slope of 2.50 indicate?
a. For every increase of 1.00 on the x-axis, there is an increase of 2.50 on the y-axis.
b. Correct, the b coefficient indicates how much does y increase if x increases by 1
unit
22. In the regression equation Y=a+bX, what does Y denote?
a. Correct.
b. In formula form: 𝑦 = 𝑎 + 𝑏 ∙ 𝑥 .
c. y= Variable to be predicted
d. a = intersection of line with y-axis (constant)
e. b = slope of the line: how much does y increase if x increases by 1? The effect of
the independent variable!
23. Which of the following does not apply to multiple linear regression?
a. true, a multiple regression means that there is a single outcome variable and
there can be more than 2 predictors. However, there is always only one outcome
variable. For multiple outcome variables, we should use another method, e.g.
MANOVA
24. R 2 is:
a. The proportion of variance in the outcome accounted for by the predictor
variable(s).
25. A psychologist was interested in whether the amount of news people watch predicts
how depressed they are. In this table, what does the value 3.030 represent?
a. The improvement in the prediction of depression by fitting the model
