Chapter 11

Question 1

What is the minimum level of measurement for correlation and regression?

Answer 1

Interval

Question 2

When would use r correlation in regards to data?

Answer 2

When you want to know the linear correlation between variables

Question 3

When would you use regression in regards to data?

Answer 3

When you want to use one variable to predict a score of another

Question 4

What is the non directional null hypothesis equation for r?

Answer 4

H0: r = 0 ( = 0)

Question 5

What is the alternate hypothesis equation for r?

Answer 5

HA: r ≠ 0 ( ≠ 0)

Question 6

What is the directional positive null hypothesis equation for r?

Answer 6

H0: r ≤ 0 ( ≤ 0)

Question 7

What is the directional positive alternate hypothesis equation for r?

Answer 7

HA: r > 0 ( > 0)

Question 8

What is the directional negative null hypothesis equation for r?

Answer 8

H0: r ≥ 0 ( ≥ 0)

Question 9

What is the directional negative alternate hypothesis equation for r?

Answer 9

HA: r < 0 ( < 0)

Question 10

How can you tell if the correlation is positive/direct? (2 characteristics)

Answer 10

• If X and Y are both low or both high values (the same)
• typically the first points on the graph are on the bottom left and at the top right of the graph

Question 11

How can you tell if the correlation is negative/inverse correlation? (2 characteristics)

Answer 11

• typically the first variable is on the top left and the bottom right
• values of X are low and the values of Y are high. Vice versa (opposite)

Question 12

In general how to most outliers affect the r coefficient?

Answer 12

reduce the correlation coefficient

Question 13

How do the outliers of X with the middle values of Y affect the r coefficient?

Answer 13

It affects the slope by pulling the regression line closer to the X values. Not the intercept

Question 14

What is regression toward the mean?

Answer 14

variables much higher or lower than the mean are often much closer to the mean when measured a second time

Question 15

What are the different effects of regression toward the mean? (4 things to know)

Answer 15

1. Predicted Y values will not always be perfect unless r = ± 1
2. Predicted Y values tend to be closer to the mean of Y than the observed values of X are to the mean of X
3. Predicted Y values will have less variation than actual observed values of Y
4. zY >zX THIS WILL NEVER HAPPEN

Question 16

What is the residual? And what does it signify?

Answer 16

the vertical distance away from the predicted Y score and their actual Y. It signifies an error

Question 17

How do you calculate the residual?

Answer 17

It is the difference between the predicted Y score and actual Y score

Predicted Y score - Actual Y score

Question 18

What is the independent variable for correlation and regression?

Answer 18

It is X. It is on the X axis across the bottom

Question 19

What is the dependant variable for correlation and regression?

Answer 19

It is Y. It is on the Y axis

Question 20

How do you calculate predicted values for standardized variables (z scores) in correlation?

Answer 20

Y = intercept + slope times X

Question 21

How do you know if the data on the scatterplot is strong or weak between variables?

Answer 21

Strong: The dots or data are close together or close to the line or close to 1

Weak: The dots or data are spread apart or far apart from the line

Question 22

What is the rand for R squared?

Answer 22

0-1 IT CAN NEVER BE NEGATIVE

Question 23

What is R squared?

Answer 23

It is where you square correlations to get the better more correct value for the correlation. THE VALUE WILL ALWAYS GET SMALLER WHEN YOU USE R SQUARED

Question 24

How do the outliers of Y with the X having middle values affect the r coefficient?

Answer 24

the intercept by moving the entire line up or down without dramatically affecting the slope.

Question 25

How do bivariate outliers affect the r coefficient?

Answer 25

It where there are outliers on the X and Y affect the intercept and the slope by moving/distorting them