Transcript Multiple Regression

Multiple Regression
Here we add more independent
variables to the regression.
Let’s begin with an example of simple linear regression. A trucking
company is interested in understanding what is going on with the
time the drivers are on the road. Travel time is the dependent
variable. It seems that travel time would be influenced by miles
traveled, the independent variable.
I have the simple regression results from such a study on the next
slide.
Note the estimated equation is
y (time) = 1.27 + .068x(miles).
Miles
time (hours)
The p-value on the slope coefficient (miles line) is
.0041 and since it is less than .05 we reject the
null of a zero slope and conclude there is a
relationship between miles driven and travel time.
R square = .66 and thus 66% of the variation in y
is explained by x.
So, we had a significant relationship between x and y and the rsquare was .66. This r square is not bad, but the company may
think that with only 66% of the variation in travel time explained
by miles driven, maybe other variables will explain the variability
as well. Another variable that could explain the travel time is the
number of deliveries that are made. (I think in the example that
travel time really means how long did it take to make the days
deliveries.)
In a multiple regression we can add another variable to the initial
x variable we had included.
In Excel you just include in the definition of x two (or more
columns) variables Note you may want to have the y variable in
the last column of the right or the first column on the left because
the x’s need to be included together in contiguous columns. I
have a multiple regression output on the next slide.
Math form
The multiple regression form of the model is:
y = B0 + B1 x1 + B2x2 + … + e,
where
B0 is the y intercept of the line, Bi is the slope of the line in terms
of xi, and e is an error term that captures all those influences on y
not picked up by the x’s. The error term reflects the fact that all
the points are not directly on the line.
So, we think there is a regression line out there that expresses the
relationship between x’s and y. We have to go find it. In fact we
take a sample and get an estimate of the regression line.
When we have a sample of data from a population we will say in
general the regression line is estimated to be
^
y = b0 + b1 x1 + b2x2 + …, where the ‘hat’ refers to
the estimated value of y.
Once we have this estimated line we are right back to algebra. y
hat values are exactly on the line.
Now, for an each value of x we have data values, called y’s, and
we have the one value of the line, called y hat.
This part of multiple regression is very similar to simple
regression. But our interpretation will change a little.
From the multiple regression output we see the coefficients section
means the estimated regression line is estimated to be
y hat = -.8687 + .0611x1 + .9234x2.
From the simple regression we had
y hat = 1.2739 + .0678x1. You will note the variable x1 does not
have the same value in each case.
In the simple regression case the .0687 is the increase in y for each
unit increase in x1, but we could not control all the other factors at
work in influencing y. In the multiple case the .0611 is the increase
in y when x1 increases by 1, but we have controlled for the
influence that x2 has on y by including x2 in the equation.
Multiple Regression
Interpretation
Correlation, Causation
Think about a light switch and the light that is on the electrical
circuit. If you and I collect data about someone flipping the
switch and the lights going on and off we would be able to say
that there is correlation from a statistical point of view. In fact,
you and I know we can say something even stronger. We can say
in this case there is causation.
In the world of business (and other areas) we want to find
relationships between variables. We would hope to find
correlation and if we have a compelling theory maybe we could
say we have causation.
Example
Say we are interested in crop yield on a farm. What variables are
correlated with crop yield? You and I know the amount of water
has been shown to have an impact on yield, as has fertilizer and
soil type, among other things. In a multiple regression setting, if
y = yield,
x1 = water amount, and x2 = amount of fertilizer, the a multiple
regression would be of the form
y = Bo +B1x1 + B2x2 + e and our estimated regression would be
of the form
y hat = bo +b1x1 + b2x2.
F Test
In a multiple regression, a case of more than one x variable, we
conduct a statistical test about the overall model. The basic idea is
do all the x variables as a package have a relationship with the y
variable? The null hypothesis is that there is no relationship and
we write this in a shorthand notation as
Ho: B1 = B2 = … =0. If this null hypothesis is true the equation
for the line would mean the x’s do not have an influence on y.
The alternative hypothesis is that at least one of the beta’s is not
zero. Rejecting the null means that the x’s as a group are related
to y.
The test is performed with what is called the F test. From the
sample of data we can calculate a number called the F statistic and
use this value to perform the test. In our class we will have F
calculated for us because it is a tedious calculation.
F
Under the null hypothesis the F statistic we calculate from a
sample has a distribution similar to the one shown. The F test
here is a one tailed test. The farther to the right the statistic we
get in the sample is, the more we are inclined to reject the null
because extreme values are not very likely to occur under the null
hypothesis. In practice we pick a level of significance and use a
critical F to define the difference between accepting the null and
rejecting the null.
Area we make = alpha
F
Critical F
To pick the critical F we have two types of degrees of freedom to
worry about. We have the numerator and the denominator degrees
of freedom to calculate. They are called this because the F stat is a
fraction.
Numerator degrees of freedom = number of x’s, in general called p.
Denominator degrees of freedom = n – p – 1, where n is the sample
size. As an example, if n = 10 and p = 2 we would say the degrees
of freedom are 2 and 7 where we start with the numerator value.
You would see from a book (maybe page 672 of a stats book) the
critical F is 4.74 when alpha is .05. Many times the book also has a
table for alpha = .025 and .01.
Area we make = alpha =.05
here
F
4.74 here
In our example here the critical F is 4.74. If from the sample we get
an F statistic that is greater than 4.74 we would reject the null and
conclude the x’s as a package have a relationship with the variable y.
On the previous slide is an example and the F stat is 32.8784 and so
the null hypothesis would be rejected in that case.
Area we make = alpha =.05
here
F
4.74 here
32.8784
P-value
The computer printout has a number on it that means we do not even
have to look at the F table if we do not want to. But, the idea is
based on the table. Here you see 32.8784 is in the rejection region.
I have colored in the tail area for this number. Since 4.74 has a tail
area = alpha = .05 here, we know the tail area for 32.8784 must be
less than .05. This tail area is the p-value for the test stat calculated
from the sample and on the computer printout is labeled Significance
F. In the example the value is .0003.
SOOOOOOO,
Using the F table,
Reject the null if the F stat > critical F in the table, or
If the Significance F < alpha.
If you can NOT reject the null then at this stage of the game there
is no relation between the x’s and the y and our work here would
be done. So from here out I assume we have rejected the null.
T tests
After the F test we would do a t test on each of the slopes similar
to what we did in a simple linear regression case to make sure that
each variable on its own has a relationship with y. There we reject
the null of a zero slope when the p-value on the slope is less than
alpha.
Multicollinearity
Can you say multicollinearity? Sure you can. Let’s all say it
together on the count of 3. 1, 2, 3 multicollinearity! Very good
class, now listen up!
Multicollinearity is an idea that volumes have been written
about. We want to have a basic feel for the problem here.
You and I want x variables that help explain y. The reason is so
that we can predict and explain movement in y. As an example,
if we can predict and explain crop yield maybe we can make
yield higher so that we can feed the world!
So, we want x’s that are correlated with y. This is a good thing.
But, sometimes the x’s will be correlated with each other. This is
called multicollinearity. The problem here is that sometimes we
can not see the separate influence an x has on y because the other
x’s have picked up the influence due to their correlation.
From a practical point of view multicollinearity could have the
following affect on your research. You reject the null hypothesis
of no relationship between all the x variables and y with the F test,
but you can not reject some or all of the separate t tests for the
separate slopes. Don’t freak out (yet!).
Let’s think about crop yield. Some farmers have water systems.
The more it rains in a summer the less water the farmers directly
apply. (Okay, maybe I am ignorant here and farmers here can use
all the water they can apply – its an example.) If you included
both inches of rain and water applied there is a correlation between
the two. This may make it difficult to see the separate impact of
either the rain or the water from the system.
If the x’s (the independent variables) have correlations more
extreme than .7 or -.7 then multicollinearity could be a problem
r square
r square on the regression printout is a measure designed to
indicate the strength of the impact of the x’s on y. The number
can be between 0 and 1, with values closer to 1 meaning the
stronger the relationship.
r square is actually the percentage of the variation in y that is
accounted for by the x variables. This is also an important idea
because although we may have a significant relationship we may
not be explaining much. From the yield example the more
variation we can explain then the more we can control yield and
thus feed the world, perhaps. Or maybe in business setting the
more variation we can explain the more profit we can make.
Qualitative Independent
Variables
Sometimes called Dummy
Variables
In the simple and multiple regression we have studied so far the
dependent variable, y, and the independent variable(s), x(s) have
been quantitative variables. But the regression can be used with
other variables. We will study the case where
The dependent variable, y, is quantitative,
One (or more, in general) independent variable is quantitative,
and,
One independent variable is qualitative.
Remember that a qualitative variable is of the type where
different values for the variable are just categories. Some
examples include gender and method of payment (cash, check,
credit card).
An example
y = the repair time in hours. The company provides maintenance
and it would like to understand why the repair time takes as long
as it does. With an understanding of repair time maybe it can
schedule employee hours better or improve company performance
in some other way.
x1 = the number of months since the last repair service was
performed. The idea is that the longer since the last repair the
more that will be need to be done. The is a quantitative variable.
x2 = the type of repair service needed. In this example there are
only two types of repairs – electrical and mechanical.
So, the company has clients that need repairs and the company is
exploring what accounts for the time it takes to make a repair.
On the next slide I have a graph with two quantitative variables
are on the axes. The two ovals represent the “cloud” of data
points. Here the points suggest a positive relationship between
months since last repair and repair time. Of course, we will have
to test if this is the real case or not, but the graph suggests that is
the case.
I have two ovals because it is thought that maybe each type of
repair has a different impact on repair time. The different ovals
represent what is happening for each type of repair and here I am
suggesting that there is a difference in repair time for each level
of repair type. Here we will also do a test to see if the different
types of repair lead to different repair times.
Repair time
Months since last
repair
The model
Here the regression model is
y = Bo +B1x1 + B2x2. When we estimate the model we use data
on y and x1 and x2. Here we make the data for x2 special. We
will say that x2 = 0 if the data point is for a mechanical repair and
x2 = 1 if the data point is for an electrical repair.
Now, when we look at the model for the two types of repair we
get the following: When x2=0
y = Bo + B1x1 + B2(0) = Bo + B1x1, and when x2 = 1,
y = Bo + B1x1 + B2(1) = Bo + B2 + B1x1. The impact of
creating x2 as a 0, 1 variable is that when the value is 0 we have
one line and when the value is 1 we have another line with a
different intercept. The intercept is Bo with the mechanical repair
and the intercept is Bo + B2 with the electrical repair.
Getting and interpreting the results:
The previous slide has the Excel printout for this regression
model. The interpretation starts with the F test. The null is that
both B1 and B2 are equal to zero. Here the F stat is 21.357 with
a p-value (Significance F) = .001. Then we would reject the null
with alpha as small as .001 (certainly we reject at alpha = .05)
and we go with the alternative that at least one of the beta’s is not
equal to zero. In other words, as a package the x’s exhibit a
relationship with the y variable.
The next step is to do the t tests on each slope value B1 and B2
(even here we tend to ignore the test on Bo because we typically
do not have much data with all the x’s = 0) separately. Here the
p-values on both have values less than .05 so we reject the null
and conclude each variable has an impact on y.
Repair time
Electrical y = (.9305 + 1.2627) + .3876x1
Mechanical y = .9305 +.3876x1
.9305 +
1.2627
.9305
Months since last
repair
On the previous slide I reproduced the graph I had before, and I
added the equations for repair time under each value of x2.
When x2 = 0 we have the line for mechanical types of repair.
When x2 = 1 we have the line for electrical types of repair.
Ultimately the difference in the two lines here is in the intercept.
But, the slope of each line is the same. This means that months
since the last repair has the same impact on repair under either
type of repair. Since b2 = 1.2627 (really since we rejected the
null that B2 = 0) the electrical line has a higher intercept. We can
use each equation to predict repair time given the value of months
since last repair, and given the type of repair. Of course, if the
type is mechanical we use the mechanical line and we use the
electrical line for the electrical type.
The next thing we would do is evaluate R square. Here the value
is .8592 and this indicates that just over 85% of the variation in y
is explained by the x’s.
The qualitative variable
In our example we had a qualitative variable with two categories.
Note we added 1 x variable for this 1 qualitative variable. The
reason is because the 1 variable had 2 categories. Now if the 1
qualitative variable has 3 categories we would have to have 2 x
variables. Say we had mechanical, electrical and industrial repair
types. We would need x2 and x3 variables, in addition to repair
time, x1.
With 3 categories we would have 3 lines.
When x2 = 0 and x3 = 0 the intercept would be Bo for the
mechanical line.
When x2 = 1 and x3 = 0 the intercept would b Bo + B2 for the
electrical line (assuming the tests had us reject the null).
When x2 = 0 and x3 = 1 the intercept would be B0 + b3 for the
industrial line.
In general, if the 1 qualitative variable has k categories, we add
k-1 x’s. When all the x’s are zero we have intercept Bo and the
line represents the equation for 1 of the categories and then the
other x’s account for the change from Bo the other k-1 category
values have.
Summary
1 qualitative variable would have k lines associated with it
(assuming tests reject Ho) and we add k-1 x’s of the 0,1 type to
account for all the k categories. 1 category is made the “base”
category and its line will have intercept Bo and the other
categories will have intercept Bo + Bt, where the t would be
different for each case of the other categories on the variable.