Week 11

Question 1

When do we use correlation?

Answer 1

• Exploring and describing possible relationships
  
  • Measuring validity and reliability
  • Equipment calibration

Question 2

What symbol is used as the correlation coefficient

Answer 2

We use and report with the symbol r

Question 3

Define Pearson Correlation Coefficient

Answer 3

is a measure of linear correlation between two sets of data

Question 4

Define p-value

Answer 4

A value that tells us if our results/ findings r statistically significant

Question 5

What is the p-value threshold for a significant result

Answer 5

<0.05

Question 6

What does <0.05 denote

Answer 6

5% probability that the results happened by chance

Question 7

Classifying correlations

- ,r - 0, 
- ,r = 0.1 - 0.3 = 
- ,r 0.4 -0.6 = 
- ,r 0.7 - 0.9 =
- ,r = 1 =

Answer 7

• ,r - 0, No effect
  
  • ,r = 0.1 - 0.3 = Weak
  • ,r 0.4 -0.6 = Moderate
  • ,r 0.7 - 0.9 = Strong
  • ,r = 1 = Perfect

Question 8

Correlation tells yo about the relationship but does not tell you …..

Answer 8

whether one variable causes the other

Question 9

Define regression

Answer 9

A measure of the relation between the mean value of one variable and corresponding values of other variables

Question 10

Define bivariate regression

Answer 10

is a linear equation describing the relationship between an explanatory variable and an outcome variable, specifically with the assumption that the explanatory variable influences the outcome variable, and not vice-versa

Question 11

What is done to a variable during bivariate regressio

Answer 11

Squared

Question 12

Regression equation

Answer 12

Y =bX + C

Question 13

What does each letter of the regression equation represent

Answer 13

• Y is the dependent variable (Need to find)
  
  • X is the independent variable (Given X)
  • B is the slope or regression coefficient
  • C is the intercept of the Y axis (Need to find)

Question 14

Bivariate regression can be used for what two things

Answer 14

1. Assess the shared variance between two variables

2. Predicted a value on one variable, using the value of another variable

Question 15

Define multiple regression

Answer 15

Extension on linear regression with more variables

Question 16

Three types of multiple regression

Answer 16

Forced entry
stepwise
hierarchical

Question 17

Explain each type of multiple regression

Answer 17

1. Forced Entry
   
   • Produce one R(squared) value, e.g. .65, or 65%
     2. Stepwise
   • Produce one or more R(squared) values for variables that explain variance
   • Height .35 or 35%
   • Vigorous PA .15 or 15%
     3. Hierarchical
   • The best method
   • Experimenter can decide what order variables are entered
     Produce R(squared) values at each step

Question 18

Multiple regression equation

Answer 18

• Y = bX1 + bX2 + bX3…… + c

Question 19

Multiple regression can be used for what three things

Answer 19

• Assess the shared variane between more than two variables
  
  • Difference approaches to assess shared variance
  • Predicted a value on one variable, using the value of other variables