Lecture13

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Statistics and Quantitative
Analysis U4320
Lecture 13: Explaining Variation
Prof. Sharyn O’Halloran
I. Explaining Variation: R2

A. Breaking Down the Distances

Let's go back to the basics of regression
analysis.
Y
Money Spent
8
on Health Care
7
x
x
Y=a+bx
x
x
x
6
Y
5
4
x
x
20
30
x
10
40
50
60
70
Income

X
How well does the predicted line explain the variation in
the independent variable money spent?
I. Explaining Variation: R2

Total Variation
$ Spent on
Health Care
Y
unexplained by regression
x Y-Y=deviation
x
8
(x,y)
7
6
x
x
Y
5.9
x
5
4
x Y-Y=deviation explained by regression
x
Y-Y=total deviation around Y
x
Y=a+bx
10
20
30
40
50 60 70
Income
X
I. Explaining Variation: R2

Total Deviation
Y Y 
(Y  Y )

(Y  Y ) .
Total =
Explained +
Unexplained
Deviation Deviation
Deviation

The total distance from any point to Y is the sum of
the distance from Y to the regression line plus the
distance from the regression line to Y .
I. Explaining Variation: R2

B. Sums of Squares

 (Y  Y )
2
We can sum this equation across all the Y's and
square both sides to get:
  (Y  Y )  (Y  Y )
2
  (Y  Y )2  2 (Y  Y )(Y  Y )   (Y  Y )2
  (Y  Y )2   (Y  Y )2 ,
I. Explaining Variation: R2

1. Total Sum of Squares (SST).


The term on the left-hand side of this equation is the
sum of the squared distances from all points to Y .
We call this the total variation in the Y's, or the
Total Sum of Squares (SST).
2. Regression Sum of Squares

The first term on the right hand side is the sum of
the squared distances from the regression line to Y .
We call it the Regression Sum of Squares, or
SSR.
I. Explaining Variation: R2

3. Error Sum of Squares


Finally, the last term is the sum of the squared
distances from the points to the regression line.
Remember, this is the quantity that least squares
minimizes. We call it the Error Sum of Squares, or
SSE.
We can rewrite the previous equation as:
SST = SSR + SSE.
I. Explaining Variation: R2

C. Definition of R2


We can use these new terms to determine how
much variation is explained by the regression
line.
If the points are perfectly linear, then the Error
Sum of Squares is 0:
I. Explaining Variation: R2
Y
Money Spent
8
on Health Care
x
x
7
x
x
6
Y
x
5
4
x
x
10

20
30
40
50
60
70
Income
Here, SSR = SST. The variance in the Y's is
completely explained by the regression line.
X
I. Explaining Variation: R2

On the other hand, if there is no relation
between X and Y:
Y
Money Spent
on Health Care
8
x
7
x
x
x
6
5
Y
x
x
x
4
10
20
30
40
50
60
70
X
Income

Now SSR is 0 and SSE=SST. The regression
line explains none of the variance in Y.
I. Explaining Variation: R2

3. Formula

So we can construct a useful statistic.
 Take the ratio of the Regression Sum of Squares
to the Total Sum of Squares:
R2 


SSR
SST
We call this statistic R2
It represents the percent of the variation in Y
explained by the regression
I. Explaining Variation: R2


R2 is always between 0 and 1.
 For a perfectly straight line it's 1, which is
perfect correlation.
 For data with little relation, it's near 0.
R2 measures the explanatory power of your model.
 The more of the variance in Y you can explain,
the more powerful your model.
I. Explaining Variation: R2

D. Example

I wanted to investigate why people have
confidence in what they see on TV.

1. Dependent variable


TRUSTTV = 1 if has a lot of confidence
= 2 if somewhat confidence
= 3 if the individual has no confidence.
2. Independent variables





TUBETIME = number of Hours of TV watched a week
SKOOL = years of education.
LIKEJPAN = feelings towards Japan.
YELOWSTN = attitudes whether the US should spend
more on national parks.
MYSIGN = the respondents astrological sign.
I. Explaining Variation: R2

3. Calculating R2
 a) Correlation matrix
TRUSTTV TUBETIME
TRUSTTV
1.000
TUBETIME -.177
SKOOL
.112
LIKEJPAN
.043
YELOWSTN .003
MYSIGN
-.038
SKOOL LIKEJPAN YELOWSTN MYSIGN
-.177
1.000
-.272
.080
-.137
.053
.112
-.272
1.000
-.072
-.016
.012
.043
.080
-.072
1.000
.040
-.001
.003
-.137
-.016
.040
1.000
-.020
-.038
.053
.012
-.001
-.020
1.000
I. Explaining Variation: R2

b) The first model is:
TRUSTTV = 2.34  0.0539 (TUBETIME)
Analysis of Variance
DF Sum of Squares
Regression
1
5.90547
Residual
468
183.61793
Mean Square
5.90547
0.39235
I. Explaining Variation: R2


How do we calculate the Total Sum of Squares?
SST = SSR + SSE
SST = 5.90 + 183.61 = 189.51
Now we can calculate R2:
SSR
SST
5.90

189.54
.031
R2 
I. Explaining Variation: R2

c) Each of the 4 different models has an
associated R2.
Eq # 2: R2 = 0.035
Eq # 3: R2
= 0.039
Eq # 4 = R2 = .04
I. Explaining Variation: R2

F. Using R2 in Practice





1. Useful Tool
2. Measure of Unexplained Variance
3. Not a Statistical Test
4. Don't Obsess about R2
5. You can always improve R2 by adding
variables
I. Explaining Variation: R2

G. Example


You'll notice that R2 increase every time.
No matter what variables you add you can
always increase your R2.
II. Adjusted R2

II. Adjusted R2

A. Definition of Adjusted R2


So we'd like a measure like R2, but one that takes into
account the fact that adding extra variables always
increases your explanatory power.
The statistic we use for this is call the Adjusted R2, and
its formula is:
n 1
(1  R2 );
nk
n  number of observations,
k  number of independent variables.
R2  1

So the Adjusted R2 can actually fall if the variable you
add doesn't explain much of the variance.
II. Adjusted R2

B. Back to the Example

1. Adjusted R2


You can see that the adjusted R2 rises from equation
1 to equation 2, and from equation 2 to equation 3.
But then it falls from equation 3 to 4, when we add
in the variables for national parks and the zodiac.
II. Adjusted R2

2. Calculating Adjusted R2

Example: Equation 2
Multiple R
.18856
R Square
.03555
Adjusted R Square .03142
Standard Error
.62562
Analysis of Variance
DF Sum of Squares
Regression
2
6.73848
Residual
F=
467
8.60813
182.78492
Mean Square
3.36924
.39140
Signif F = .0002
II. Adjusted R2
Variable
SKOOL
B
.015524
SE B
.010641
Beta
.068897
T
1.459
Sig T
.1453
TUBETIME
-.048228
.014436
-.157772
-3.341
.0009
(Constant)
2.127991
.158260
13.446
.0000

We calculate:
n 1
R2  1
(1  R2 )
nk
470  1
1
(1.03555)
470  3
.0314
II. Adjusted R2

C. Stepwise Regression



One strategy for model building is to add
variables only if they increase your adjusted R2.
This technique is called stepwise regression.
However, I don't want to emphasize this
approach to strongly. Just as people can fixate
on R2 they can fixate on adjusted R2.
****IMPORTANT****
If you have a theory that suggests that certain
variables are important for your analysis then
include them whether or not they increase the
adjusted R2.
Negative findings can be important!
III. F Tests

III. F Tests

A. When to use an F-Test?


Say you add a number of variables into a
regression model and you want to see if, as a
group, they are significant in explaining
variation in your dependent variable Y.
The F-test tells you whether a group of
variables, or even an entire model, is jointly
significant.

This is in contrast to a t-test, which tells whether an
individual coefficient is significantly different from
zero.
III. F Tests

B. Equations

To be precise, say our original equation is:
EQ 1: Y = b0 + b1X1 + b2X2,
and we add two more variables, so the new equation is:
EQ 2: Y = b0 + b1X1 + b2X2 + b3X3 + b4X4.
 We want to test the hypothesis that
H0: b3 = b4 = 0.
That is, we want to test the joint hypothesis that X3 and X4
together are not significant factors in determining Y.
III. F Tests

C. Using Adjusted R2 First

There's an easy way to tell if these two
variables are not significant.



First, run the regression without X3 and X4 in it, then
run the regression with X3 and X4.
Now look at the adjusted R2's for the two
regressions. If the adjusted R2 went down, then X3
and X4 are not jointly significant.
So the adjusted R2 can serve as a quick test for
insignificance.
III. F Tests

D. Calculating an F-Test
If the adjusted R2 goes up, then you need to do a
more complicated test, F-Test.

1. Ratio


Let regression 1 be the model without X3 and X4, and
let regression 2 include X3 and X4.
The basic idea of the F statistic, then, is to compute
the ratio:
SSE1  SSE2
SSE2
III. F Tests

2. Correction



We have to correct for the number of independent
we add.
So the complete statistic is:
SSE1  SSE2
m
F
;
SSE2
nk
m  number of restrictions;
k  number of independent variables.
Remember that k is the total number of independent
variables, including the ones that you are testing and
the constant.
III. F Tests

2. Correction (cont.)


This equation defines an F statistic with m and n-k
degrees of freedom.
We write it like this:
Fnm k

To get critical values for the F statistic, we use a set
of tables, just like for the normal and t-statistics.
III. F Tests

E. Example

1. Adding Extra Variables: Are a group of
variables jointly significant?

Are the variables YELOWSTN and MYSIGN jointly
significant?
EQ 1: TRUSTTV = b0 + b1LIKE JPAN + b2SKOOL + b3TUBETIME .
EQ 2: TRUSTTV = b0 + b1LIKE JPAN + b2SKOOL + b3TUBETIME +
b4MYSIGN + b5YELOWSTN
III. F Tests

1. Adding Extra Variables (cont.)

a) State the null hypothesis
H0: b4 = b5 = 0.

b) Calculate the F-statistic
 Our formula for the F statistic is:
SSE1  SSE2
m
F
,
SSE2
nk
III. F Tests


What is SSE1 --the sum of squared errors in the
first regression?
What is SSE2, the sum of squared errors in the
second regression?
m=2

N = 470
The formula is:
182.07  18182
.
2
F
18182
.
470  6
= 0.319
k=6
III. F Tests

c) Reject or fail to reject the null hypothesis?
 The critical value at the 5 % level
2
 F
470  6 from the table, is 3.00.
2
 Is the F-statistic > F
?
470  6


If yes, then we reject the null hypothesis that the
variables are not significantly different from zero;
otherwise we fail to reject.
.319 < 3.00, so we can reject the null hypothesis.
B=0 0.319
3.0
III. F Tests

2. Testing All Variables: Is the Model
Significant?

Equation 2
Multiple R
.18856
R Square
.03555
Adjusted R Square .03142
Standard Error
.62562
Analysis of Variance
DF
Sum of Squares
Regression
2
6.73848
Residual
F=
467
8.60813
182.78492
Signif F = .0002
Mean Square
3.36924
.39140
III. F Tests
Variable
B
SE B
Beta
T
Sig T
SKOOL
.015524
.010641
.068897
1.459
.1453
TUBETIME
-.048228
.014436
-.157772
-3.341
.0009
(Constant)
2.127991
.158260
13.446
.0000

a) Hypothesis:
H0: b1 = b2 = 0.

Again, we start with our formula:
SSE1  SSE2
m
F
,
SSE2
nk
III. F Tests

b) Calculate F-statistic
 SSE2 = 182.78
 SSE1 is the sum of squared errors when there
are no explanatory variables at all.

If there are no explanatory variables, then SSR
must be 0. In this case, SSE=SST.
So we can substitute SST for SSE1 in our formula.
SST = SSR + SSE = 6.738 + 182.78 = 189.54
189.54  182.78
2
F
182.78
470  3
= 8.61.

This is the number reported in your printout under the
F statistic.
III. F Tests

c) Reject or fail to reject the null hypothesis?
2
 The critical value at the 5% level, F470 3 from
your table, is 3.00.
 So this time we can reject the null hypothesis
that b1 = b2 = 0.
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