# PRM Logistic Regression

25 Questions | Total Attempts: 168  Settings  PRM Logistic Regression

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• 1.
Logistic Regression (LR) is very similar to ___ except the predictors do not need to be ____.
• A.

MANOVA, normally distributed

• B.

DFA, normally distributed

• C.

ANOVA, normally distributed

• D.

Factor Analysis, normally distributed

• E.

DFA, linear

• 2.
In LR, the DV is a ___ variable, even though it represents a ___ probabilistic occurence of an event occurring.
• A.

Categorical, continuous

• B.

Continuous, categorical

• C.

Dichotomous, categorical

• D.

Interval, continuous

• E.

None of the above

• 3.
The 5 assumptions are (1) independence (2) linearity (3) normality (4) homogeneity of variance & (5) non-multicollinearity. LR does not require:
• A.

1,2,3

• B.

2,3,4

• C.

3,4,5,

• D.

Is flexible with all 5

• E.

Is strict with all 5

• 4.
Which one of the following is TRUE?
• A.

In MANOVA, the dv has to be categorical but an underlying continuous distribution

• B.

In DFA, the dv has to be categorical but an underlying continuous distribution

• C.

In LR, the dv has to be categorical but an underlying continuous distribution

• D.

Standard, statistical & heirarchical regression are all variable selection methods of LR.

• E.

C and d

• 5.
The reduction in uncertainty or the degree of variance we have to explain is represented by: multiple regression uses ____, MANOVA  and ANOVA use ____, and LR uses ____.
• A.

R squared, ss, -2LL

• B.

Ss, ss squared, -2LL

• C.

Ss, r squared, -2LL

• D.

-2LL, ss, r squared

• E.

-2LL, ss squared, r squared

• 6.
Is the following True or False: In research, the significance of a finding is very dependent on sample size, but not effect size. Effect size will stay reliable always.
• A.

True

• B.

False

• 7.
The nagelkerke R squared  is a recommended effect size estimate to use in which statistical modelling?
• A.

MANOVA

• B.

ANOVA

• C.

DFA

• D.

LR

• E.

MR

• 8.
In LR, the exponent of B, EB, tells us that something is NOT a significant predictor, if the EB is ___. In other words, it is equally probable that the skier fell or did not fall.
• A.

1

• B.

Higher than 1

• C.

Lower than 1

• D.

Close to 1

• E.

A fraction of 1

• 9.
In the textbook example: In LR, if the EB is ____ then it is ____ that the skier will fall.
• A.

Below 1, less likely

• B.

Below 1, more likely

• C.

Close to 1, less likely

• D.

Close to 1, more likely

• 10.
The use of EB is particularly pertinent in ___, whereby you are 4.3 times as likely or half as likely to do something.
• A.

Marketing

• B.

Environmental research

• C.

Health research

• D.

Tourism

• E.

Hospitality

• 11.
Nagelkerke's statistic is the ____ which can achieve a value of 1.
• A.

Proportion of variation in the response variable explained by the specific model

• B.

Proportion of variation in the explanatory variable explained by the specific model

• C.

It is used as a measure of effect size

• D.

B and c

• E.

None of the above

• 12.
If in LR you are predicting gender & are using dummy variables 0 and 1 for female and male respectively, the EB understood as a poor predictors will have an EB of ____.
• A.

Close to 1

• B.

Of 1

• C.

Higher than 1

• D.

Less than 1

• E.

None of the above

• 13.
With reference to ques 12, an EB of .9 on 'information' is interpreted as:
• A.

You are less likely to have a high information score if you are male.

• B.

You are less likely to have a high information score if you are female.

• C.

You are equally likely either way.

• D.

• 14.
In LR it is important to keep an eye on which dummy value is 0 and which is 1. This is:
• A.

True

• B.

False

• 15.
LECTURE 4: MULTIPLE REGRESSION Predicting some dichotomous outcome with variables that are either continuous or dichotomous is:
• A.

MANOVA

• B.

ANOVA

• C.

DFA

• D.

LR

• E.

MR

• 16.
The use of multiple variables to predict 1 variable and the variable being predicted or the DV is a continuous scale variable is:
• A.

ANOVA

• B.

MANOVA

• C.

DFA

• D.

LR

• E.

MR

• 17.
The predictors (IV's) in MR can be:
• A.

Continuous

• B.

Dichotomous

• C.

A and b

• D.

Categorical

• E.

Whatever you like

• 18.
The number of predictors do matter in terms of bringing up the R squared and bringing down the F value in MR is:
• A.

False

• B.

True

• 19.
The usefulness of any single predictor will be gauged by its semi-partial correlation (SR). Is this true or false?
• A.

False

• B.

True

• 20.
The general linear model, is:
• A.

The observed data times what you can model plus eror

• B.

Quite simply, the observed data is the result of what you can model plus error

• C.

Error plus any data and model varianace

• D.

Is an interplay between variance and error variance

• E.

None of the above

• 21.
The difference between an ANOVA and a correlational design is that the ANOVA is ____ and ____ whilst the correlational design is  ____.
• A.

Structured, involved, relative.

• B.

Experimental, looking for an effect, trying to prove relationships.

• C.

Experimental, looking for an effect, not looking for an effect.

• D.

Non-experimental, causal, not looking for an effect.

• E.

Non-experimental, looking for an effect, trying to prove relationships.

• 22.
Standard MR uses ____ predictors, statistical regression uses ____, whilst hierarchical regression uses ____.
• A.

All, some, few.

• B.

All, minimum, subset.

• C.

All, some, subset.

• D.

All, best, subset.

• E.

They all use the same number of IV's.

• 23.
Efficiency of predictors is a pretty important consideration when using MR. This is predominantly because of its placement in the numerator.This is:
• A.

True

• B.

False

• 24.
In MR, what order do we check out (1) descriptive stats (2) inferential stats of predictors & (3) inferential statistics of equation?
• A.

3,2,1

• B.

1,2,3

• C.

2,1,3

• D.

3,1,2

• E.

1,3,2

• 25.
LECT 4: Unique contributions of particular predictors (ie X1 alone & X2 alone) are ___ and joint contributions ___ assigned to any particular predictor.
• A.

Semi-partial correlation, are not

• B.

Partial correlation, are not

• C.

Semi-partial correlation, are

• D.

Partial-correlation, are