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Statistics: Regression
Statistics: Regression
37 terms
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First four lectures of data analysis Autumn term
Flashcards
37 cards
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Practice quiz
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Questions
37
Answers
1
When do we reject the null hypothesis?
when p=>0.05
when p=>0.0128
when p=<0.05
2
We can make predictions using linear regression.
True
False
3
Which model accounts for more variance?
model 2
model 1
4
Why do we use histograms?
Check for outliers
check residual stats
check for significance
check the model fit
5
95% of standardized residuals should lye between -+___%
3
2.99
2
2.95
6
Where is the intercept point?
When the regression line crosses the X axis
When the regression line is absolute zero
When the regression line crosses the Y axis
7
SSm =
Model variability difference in variability of model & mean
Residual error variability between model & mean
Total variability between scores & mean
Sum squared total values as averages
8
A regression line is a ___ based on data.
number
model
mean
graph
9
A type 1 error is when...
Accepting the null hypothesis when it is false
Rejecting the null hypothesis when it is true
10
When we have multiple models we will likely get a _____ box in SPSS output.
coefficients
Excluded variables
Graph
ANOVA
11
In our residuals, we want NO standard residuals greater than ____.
2.5
3
2
1.5
12
A number is an outlier if it is +__ more than standard residuals.
2
4
2.5
3
13
What does the ANOVA table here, tell us?
If the whole model is sig
The errors in the data
the change in outcome
14
Which of the following is true?
we can predict press or trailer from the equation
press is making a sig contribution
everything is sig
trailer is making a sig contribution
15
What does the t-test tell us?
if increasing by 0.399 mill is sig
If the IV is making a sig contribution
If the DV is making a sig contribution
if increasing by 0.161 mill is sig
16
What does the press Beta value tell us?
as press increases, number of viewers increases 0.281 mill
as press increases, number of viewers increases 0.161 mill
as press increases, number of viewers increases 0.129 mill
17
Which variable does NOT make a sig contribution to the model.?
both
trailer
neither
press
18
This graph shows...
a good fit
a bad fit
a neutral fit
no fit
19
What is multicollinearity?
When correlation is too high
when the correlation is NOT sig
When there is no correlation
20
Which of the following is
NOT
a variation type?
Numerical
Continuous (diff between points)
Ordinal
Dispersal (variances etc)
21
Which of the following is
NOT
a way to deal with data?
make central tendencies
measures of dispersal
guess the results
22
What is forced entry?
predictors selected using semi-partial outcome correlation
All variables entered simultaneously
ordered from the previous literature
23
When do we need change statistics?
when we use stepwise
when we use hierarchical
when we use forced entry
24
Which model is better at predicting the number of viewers?
Model 2
Model 1
Both
Neither
25
What is a regression coefficient?
Something that described the gradient
a type of descriptive stats
a type of measurement
26
What is homoscedasticity?
when errors are the same across IV's
When there is no correlation
when data is the same across IV's
27
SSr =
Total variability between scores & mean
Model variability difference in variability of model & mean
Sum squared total values as averages
Residual error variability between model & mean
28
What is step wise entry?
All variables entered simultaneously
predictors selected using semi-partial outcome correlation
ordered from the previous literature
29
SSt =
Model variability difference in variability of model & mean
Sum squared total values as averages
Residual error variability between model & mean
Total variability between scores & mean
30
What does this table tell you?
fit of the model
how sig the model is
how accurate your graph was
31
The total sum of squares tell us...
Total mean
Total error
Total variability
32
Multiple regression uses more...
Variables
SPSS
Experimenters
Data
33
This box tells us that __% of the variance is accounted for.
56%
30.2%
3.5%
35.5%
34
What does this table tell you?
if the slope sig diff from zero
if the model fits
if the model is sig
35
Regressions are symmetrical.
True
False
36
How do we tell how well the model fits the data?
Descriptive stats
coefficients
Residual stats
ANOVA
37
How do we describe a linear relationship?
Qualitative analysis
Descriptive words
A straight line
Descriptive stats
