Correlation and Regression

Question 1

correlation

Answer 1

Measuring the association
between 2 variables

Question 2

how do we graph correlation?

Answer 2

scatterplots plot the individual pairs of scores not means

Question 3

positive correlation

Answer 3

Variables change in the same direction. The more studying, the higher the exam score.

Question 4

negative correlation

Answer 4

variables change in the opposite direction. The more hours playing
video games, the lower the exam score.

Question 5

no correlation

Answer 5

A change in one variable
is not related to a systematic change in the other. Studying is not related to exam score

Question 6

how do you compute a correlation

Answer 6

pearsons r. When you have two variables that are on interval or ratio scale

Question 7

pearsons correlation coefficient

Answer 7

r will always range from -1.0 to 1.0.
* The sign ( + or - ) tells us the direction of the relationship.
* The absolute value tell you the strength
* The strongest positive correlation is 1.0.
* The strongest negative relationship is -1.0
* If there is no relationship, the correlation
coefficient will be close to zero.

Question 8

the closer the data is to fitting a straight line

Answer 8

the stronger the correlation

Question 9

to compute a correlation, you must first do what

Answer 9

transform your raw scores into z-scores, because it puts everything on the same scale

Question 10

What does the absolute value of the correlation coefficient tell us?

Answer 10

the strength of the relationship

Question 11

The correlation coefficient tells us

Answer 11

the strength and the direction of the relationship.

Question 12

statistical hypothesis of pearsons r

Answer 12

Ho: r = 0
Ha: r = 0

Question 13

What is r squared

Answer 13

If you have a correlation between
two variables (e.g., r = .80),
squaring the r (r2 = .64) tells you
the amount of covariance between those variables.

Question 14

what is covariance?

Answer 14

The amount of shared variance
between X and Y.

Question 15

when 2 variables are correlated and we know one variable can we predict the variability of the other variable?

Answer 15

we can predict or “account for” a certain proportion of the variability of the other variable.

Question 16

pearsons r what can it be due to

Answer 16

Correlations between two variables in your sample can be due to:

1.) chance factors (sampling error)

2.) They can reflect real relationships among your variables.

Question 17

significance test

Answer 17

• Used to determine the likelihood of obtaining a certain r by chance.
• It will depend on the size of r and n.
• If it is unlikely that the r could have occurred by chance, we infer that it is a real relationship that would hold up if we tested the entire population.

Question 18

curvilinear relationship

Answer 18

An increase in one variable leads to an increase in another—up to a certain point. After that point, an increase in x leads to a decrease in y.

Question 19

What effect do the outliers have on a correlation scatterplot?

Answer 19

Can inflate or deflate the apparent
correlation between two variables.

Outliers are going to have a larger
influence in a small sample than in a large one.

Many different techniques can be used to diagnose and treat outliers in a sample.

Question 20

What does Pearson’s r measure?

Answer 20

only measures linear relationships

Question 21

what does a regression analysis do?

Answer 21

When we have two variables and they are correlated, a regression analysis gives you a more accurate level of prediction.