multiple regression

Question 1

what does a vif score of 5 or above mean

Answer 1

multicolinearity (intercorrelations between the independent variables)

Question 2

what does a vif score of 10 or above mean

Answer 2

severe multicolinearity

Question 3

multiple regression is an analysis of ___

Answer 3

dependence - one variable is examined by its dependence on another

Question 4

what are independent variables referred to as?

Answer 4

predictors

Question 5

the __ of each X variable describes its relationship with Y

Answer 5

coefficient

Question 6

quadratic equation

Answer 6

y = cx2 + bx + c

Question 7

multi regression equation

Answer 7

y = b1x1 + b2x2 + … + bnxn + c

Question 8

in the multiple regression equation what does b stand for

Answer 8

the predictor values

Question 9

what will the results from SPSS come out as (eg what is the name of the variable)

Answer 9

the R value - this is the measure of association between the observed and predicted value of the criterion variable

Question 10

what is r2

Answer 10

simple linear regression

Question 11

what does r2 adj account for

Answer 11

accounts for the number of predictor variables in multiple regression

Question 12

should you assess the relative importance of the predictor by the size of the coefficient? if not then what should you do??

Answer 12

NO – STANDARDISE THE COEFFICIENTS WITH BETA WEIGHTS (looks at the response of y to each independent variable)

Question 13

what is f?

Answer 13

the significance of the model being able to explain variance

Question 14

what does f = 0 mean?

Answer 14

the model does not explain variance

Question 15

what is B

Answer 15

the regression coefficient

Question 16

what is t?

Answer 16

the significance of the coefficient in explaining the variance

Question 17

what is assumed in multiple regression about the distribution?

Answer 17

it is normally distributed - must be for it to work

Question 18

homoscedasticity is always assumed in multiple regression, what is this?

Answer 18

the variance is constant across all levels of the predicted variable. eg there is very little variance from the line of best fit for all variables.

Question 19

what should you look at to see if x values are correlating with eachother?

Answer 19

vif factor (variance inflation factor)

Question 20

is it better to have more or less values in your graphs and analysis

Answer 20

less - more values is not necessarily good, need around 2 or 3 as most regressors are likely to be significant

Question 21

what is the equation showing: s^2y/s^2e

Answer 21

used to determine r2 value - simple linear regression

Question 22

what does this represent: s^2y

Answer 22

variance of original simulation model output

Question 23

what does this represent: s^2e

Answer 23

variance of regression residuals

Question 24

what is the difference between Homoscedasticity and Heteroscedasticity

Answer 24

homo = everything is the same variance, normally distributed, okay
hetero = violation of homo, variation of variance, errors in independent variable

Question 25

what does it mean if the f ratio is greater than 1

Answer 25

explained variance is higher than unexplained variance

Question 26

what is 'tolerance'?

Answer 26

gives you the unique variance associated with each variable

Question 27

what does a tolerance of 0.34 mean

Answer 27

means 34% of variance for that predictor is not accounted for by other predictors

Question 28

a tolerance of less than 0.2 means?

Answer 28

that that predictor does not add anything new to the model - not good

Question 29

3 ways to identify homoscedasticity - common question

Answer 29

in a scatter plot there should be no discernable change in distribution (should be randomly scattered) data in histograms and p-p plots should also remain normally distributed

Question 30

why do you need to standardize coefficients - common question

Answer 30

to reduce multicollinearity | to compare different stats effectively

Question 31

what is stepwise multiple regression

Answer 31

it finds the independent variable that has the largest significant Pearson’s correlation with Y. It then returns to the matrix and finds the next most significant correlation and onwards

Question 32

how does stepwise mutliple regression deal with insignificant predictors?

Answer 32

Stepwise regression involves adding predictors to the model one by one until it finds a non-significant predictor, at which point it stops building the model.

Question 33

what is hierarchal multiple regression

Answer 33

This uses the system of ‘blocks’ in the ‘linear regression’ window to allow the user to define the order in which variables should be regressed. This approach is very logical and selection of predictors is based on underlying theoretical considerations

Question 34

how does hierarchal multiple regression deal with insignificant predictors?

Answer 34

the system uses a method of 'blocks' in the linear regression window to allow the user to choose what variables to regress. this allows certain predictors to be controlled and better understood

Question 35

how does forward stepwise regression work?

Answer 35

model selects from the group of predictors based on the predictor which makes the largest contribution to r2. adds the rest of the predictors but stops once the remaining variables cannot make a significant contribution.

Question 36

how does backward stepwise regression work?

Answer 36

opposite of forward. dependent variable is regressed against all predictors and the weakest is taken out (contributes the least). this continues until only the statistically significant variables remain.

Question 37

what is the equation for standardising values

Answer 37

zx (variable) = (x (each observation) + x mean) / standard deviation of variable

Question 38

how to calculate f value

Answer 38

explained variance / unexplained variance