Deck 12: How Can I Tell If One Variable Can Predict Another Simple Linear Regression

Question 1

When drawing the line of best fit, where is the intercept drawn from?

A) The x axis where the x axis has a value of 0
B) The y axis where the y axis has a value of 0
C) The x axis where the y axis has a value of 0
D) The y axis where the x axis has a value of 0

Answer 1

D
Explanation: The intercept value tells us where the line of best fit intercepts with the y-axis when the x-axis is at the value of 0.

Question 2

What does the β₁ slope value represent?

A) The change in the outcome variable for a one unit change in the predictor variable
B) The change in the predictor variable for a one unit change in the outcome variable
C) The change in the outcome variable if the predictor variable mean is 0
D) The change in the predictor variable if the outcome variable mean is 0

Answer 2

A
Explanation: The β₁ slope value represents the change in the outcome variable for a one unit change in the predictor variable

Question 3

How would a β₁ slope value of + 2.5 be interpreted?

A) For a one unit increase in the outcome variable, the predictor variable decreases by 2.5
B) For a one unit increase in the outcome variable, the predictor variable increases by 2.5
C) For a one unit increase in the predictor variable, the outcome variable decreases by 2.5
D) For a one unit increase in the predictor variable, the outcome variable increases by 2.5

Answer 3

D
Explanation: A positive β₁ slope value shows an increase in the outcome variable for a one point increase in the predictor variable.

Question 4

How would a β₁ slope value of - 6.2 be interpreted?

A) For a one unit increase in the outcome variable, the predictor variable decreases by 6.2
B) For a one unit increase in the outcome variable, the predictor variable increases by 6.2
C) For a one unit increase in the predictor variable, the outcome variable decreases by 6.2
D) For a one unit increase in the predictor variable, the outcome variable increases by 6.2

Question 5

Imagine a situation where the predictive regression equation is Y = 10 + (2 * X). For what value of the predictor variable (X) would the correct answer be 20?

A) 10
B) 5
C) 2
D) 2.5

Question 6

Imagine a situation where the predictive regression equation is Y = -4 + (5 * X). What is the correct outcome score if X = 3?

A) 19
B) 15
C) 45
D) 11

Question 7

What would the R² be if r = .3?

A) .09
B) .3
C) .6
D) .1

Question 8

If R² = .75, as a percentage, how much unexplained variance would there be within the regression model?

A) 75%
B) 99.25%
C) 25%
D) 0.75%

Question 9

How do you know if your R² explains a good amount of variability in your dataset?

A) The correlation that the R² is calculated from should not be significant
B) The correlation that the R² is calculated from should be significant
C) The R² should be greater than 5%
D) The R² should be less than 5%

Question 10

To evaluate the effect size of your regression model you calculate f² and get a value of 0.62. What size effect have you found?

A) Negative
B) Large
C) Small
D) Medium

Question 11

In the study on keeping personal items on your desk at work and job satisfaction, as a percentage, how much of the variance in the model is explained?

A) 82%
B) 18%
C) 67%
D) 33%

Question 12

In the study on keeping personal items on your desk at work and job satisfaction, what is the Sum of Products (SP)?

A) 44.10
B) 100.90
C) 51
D) 59

Question 13

In the study on keeping personal items on your desk at work and job satisfaction, what is the Sum of Squares (SSx)?

A) 44.10
B) 100.90
C) 51
D) 59

Question 14

In the study on keeping personal items on your desk at work and job satisfaction, what is the slope value (β₁)?

A) 2.5211
B) 0.4371
C) 44.10
D) 5.1

Question 15

In the study on keeping personal items on your desk at work and job satisfaction, what is the intercept value (β₀)?

A) 2.5211
B) 0.4371
C) 44.10
D) 5.1