Lecture 9 - Correlation And Regression

Question 1

What is the difference between an ANOVA or t test, and a correlation?

Answer 1

ANOVA and t tests look at differences between groups whereas a correlation looks at the relationship between two variables

Question 2

What does correlation look at?

Answer 2

The co-variation between variables e.g. relationship between stress and illness

Question 3

What does a compact coefficient do?

Answer 3

It’s function is to provide a compact numerical representation of the degree that to which any two variables (with two sets of data) co-vary

Question 4

What are three types of correlations in psychology?

Answer 4

Perfect positive, no correlation, perfect inverse

Question 5

What are the basic correlation statistics?

Answer 5

Pearson product moment - interval or ratio data (bivariate normal distribution)

Spearmans Rho - ranked data (only performed non ranked data)

Question 6

What are some additional correlation statistics?

Answer 6

Kendal Tau - non parametric (SPSS)
Point-biseral correlation (when one variable is dichotomous)
Phi coefficient - when both variables are dichotomous)
Partial correlation -control for effect of additional variable

Question 7

What is the equation of a straight line?

Answer 7

y = bx + a

Question 8

What is the regression equation?

Answer 8

ŷ = bX + a

Y is predicted value of y
B is the slope of regression line
X is the value of the predictor variable

Question 9

How do you find the residual in a correlation?

Answer 9

The difference between the predicted y and the actual y

Question 10

How do you create a model of correlation data?

Answer 10

Fit the straight line in the data

Question 11

How can we make predictions of y from x?

Answer 11

Find the equation of the line (linear regression)

Question 12

Correlations indicate (what) and cannot be used to infer (what)?

Answer 12

Correlations indicate co-variation and cannot be used to infer causality

Question 13

What is the different r’s used in correlations?

Answer 13

r is a correlation statistic and r2 is a variance estimate

Question 14

When do you use r?

Answer 14

-1 through +1 correlations

The strength of the relationship not an indicator of significance

Question 15

When do you use r2?

Answer 15

Variance estimate
Correlation coefficient r not a ratio scale eg 0.6 is not twice as good as 0.3
R2 is a ratio scale
Coefficient of determination

Question 16

What are correlation assumptions?

Answer 16

Range restrictions
Non-linear relationships
Heterogenous samples
Outliers

Question 17

For linear regression, do we use the least or most squares approach to find the line that best fits through the data points? And why?

Answer 17

We use the least it reduces the residual error - not always the best line you would draw eye by eye

Question 18

Cause and effect: a correlation can mean:

Answer 18

Variable x has causal effect on variable Y
Variable Y has a causal effect on variable X
A and B are related to something else
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