general linear models

Question 1

4 assumptions for ordinary least squares regression

Answer 1

1. linearity
2. normality
3. homogeneity of variance
4. independence
5. all variability is assumed to lie with y, not x

Question 2

OLS is also called?

Answer 2

model I regression

Question 3

______ is similar to an ANOVA with fixed factors

Answer 3

model I (OLS)

Question 4

which assumption os often ignored in OLS

Answer 4

all variability is assumed to lie with y, not x

Question 5

when is model II regression used

Answer 5

when predictor variables (X) are measured with error

Question 6

two methods of model 2 regression

Answer 6

1. orthogonal, major axis regression
2. geometric mean, reduced major axis regression, standard major axis regression

Question 7

how does OLS work

Answer 7

minimizes the sum of squares of the vertical deviation from the line

Question 8

how does major axis regression work

Answer 8

minimizes the sum of the squared perpendicular distances to the line

Question 9

what minimizes the sum of the squared perpendicular distances to the line

Answer 9

major axis regression

Question 10

whatminimizes the sum of squares of the vertical deviation from the line

Answer 10

ordinary linear squared regression

Question 11

major axis regression assumes what

Answer 11

equal variability in X and Y (X and Y must be in the same units)

Question 12

if X and Y are not in the same units, which regression to use

Answer 12

reduced major regression

Question 13

in model II regression, there is no ____ and ____ variable, they are just X and Y

Answer 13

in model II regression, there is no independent and dependent variable they are just X and Y

Question 14

model II regression is similar to?

Answer 14

correlation

Question 15

what statistical test to use if you have multiple x variables and they are both categorical

Answer 15

2-way anova

Question 16

what statistical test to use if you have multiple x variables and they are both continuous

Answer 16

multiple regression

Question 17

what statistical test to use if you have multiple x variables and one is categorical and one is continuous

Answer 17

ANCOVA

Question 18

what is ANCOVA

Answer 18

analysis of covariance
- looks at the effect of a treatment (categorical) while accounting for a covariate (continuous)

Question 19

what can be used to compare slopes of regression lines

Answer 19

ANCOVA

Question 20

in ANCOVA, explain regression lines

Answer 20

• if regression lines are parallel the effect of the covariate on the response variable is the same in each group (no interaction)
• if they cross, there is an interaction

Question 21

describe the steps to ANCOVA

Answer 21

1. fit the full model (categorical treatment, covariate interaction)
2. test for interaction
3. if no interaction, test for differences between lines in intercepts

Question 22

in ANCOVA, _____ is the treatment, _____ is the covariate

Answer 22

in ANCOVA, the categorical variables are the treatment while the continuous variables are the covariate

Question 23

for analysis of variance, what are the independent variables

Answer 23

both categorical

Question 24

for multiple regression, what are the independent variables

Answer 24

continuous

Question 25

in analysis of covariance, what are the independent variable

Answer 25

one categorical one continuous `

Question 26

for multiple regression, multicollinearity is assessed using?

Answer 26

Pearsons correlation coefficient
variance inflation factor

Question 27

for categorical variables, how to assess multicollinearity

Answer 27

• assess with spearman rant correlation coefficient and chi square

Question 28

for a categorical and continuous variable, how to measure multicollinearity

Answer 28

measured by t-test (if categorical variable has two categories) or anova is >2

Question 29

if a model has categorical and continuous variables, how to test for multicollinearity if it the categorical variable has two categories

Answer 29

t test

Question 30

a model has categorical and continuous variables, how to test for multicollinearity if it the categorical variable has more than two categories

Answer 30

anova

Question 31

to test for normality in anova you have to first do what to X

Answer 31

have to take out. the effects of all the Xs before you look at the distribution of Y

Question 32

in ANOVA, residuals=?

Answer 32

difference from the group mean (predicted Y)

Question 33

example of random effect

Answer 33

subjects in a repeated measures experiment, where the subject is measured several times, are a random effect variable

Question 34

what are random effects

Answer 34

are those where the effect levels are chosen randomly from a larger population of levels
- they represent a sample from a larger population

Question 35

levels of fixed effects are?

Answer 35

of direct interest

Question 36

what type of variables can be random effects

Answer 36

categorical

Question 37

continuous variables included to control for cofounding influences are called

Answer 37

covariates

Question 38

mixed effects model

Answer 38

contains Both fixed effects and random effects

Question 39

what is used to partition noise and signal

Answer 39

mixed effects model

Question 40

what is signal

Answer 40

the meaningful information you are trying to detect

Question 41

what is noise

Answer 41

the random, unwanted variation or fluctuation that I interferes with the signalk

Question 43

what is conditional r2

Answer 43

the amount of explained variance for the entire model, including both fixed and random effects

Question 44

what is marginal r2

Answer 44

explains ho much of this variance is attributed to the fixed effects alone

Question 45

do you report conditional or marginal effects

Answer 45

both

Question 46

when to use linear and nonlinear regression

Answer 46

when the response variable (Y) I continuous

Question 47

when the response variable is continuous what regression do you ue

Answer 47

linear or non linear

Question 48

when to use logistic regression

Answer 48

when the y variable represents categories (usually binary)

Question 49

when the y variable is categorical binary, what regression d you use

Answer 49

logistic

Question 50

when to use poisson regression

Answer 50

when the y variable represents counts

Question 51

when the y variable represents counts which regression do you use

Answer 51

poisson