Chapter 11 Correlation and Simple Linear Regression Statistics for Business (Econ) 1.
Econ 3790: Business and Economics Statistics
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Transcript of Econ 3790: Business and Economics Statistics
Econ 3790: Business and Economics Statistics
Instructor: Yogesh [email protected]
The equation that describes how the dependent variable y is related to the independent variables x1, x2, . . . xp and an error term is called the multiple regression model.
Chapter 15: Multiple Regression Model
yy = = 00 + + 11xx11 + + 22xx2 2 ++ . . . + . . . + ppxxpp + +
where:where:00, , 11, , 22, . . . , , . . . , pp are the are the parametersparameters, and, and is a random variable called the is a random variable called the error termerror term
A simple random sample is used to A simple random sample is used to compute sample statistics compute sample statistics bb00, , bb11, , bb22, , . . . , . . . , bbpp that are used as the point estimators of the that are used as the point estimators of the parameters parameters 00, , 11, , 22, . . . , , . . . , pp..
Estimated Multiple Regression EquationEstimated Multiple Regression Equation
^yy = = bb00 + + bb11xx1 1 + + bb22xx2 2 + . . . + + . . . + bbppxxpp
The The estimated multiple regression equationestimated multiple regression equation is: is:
Interpreting the CoefficientsInterpreting the Coefficients
In multiple regression analysis, we In multiple regression analysis, we interpret eachinterpret each
regression coefficient as follows:regression coefficient as follows: bbii represents an estimate of the change in represents an estimate of the change in yy corresponding to a 1-unit increase in corresponding to a 1-unit increase in xxii when all when all other independent variables are held constant.other independent variables are held constant.
Example: Car SalesExample: Car Sales Suppose we believe that number of cars sold (Suppose we believe that number of cars sold (yy) is) is
not only related to the number of ads (not only related to the number of ads (xx11), but also ), but also to the minimum down payment required at the to the minimum down payment required at the ((xx22). The regression model can be given by:). The regression model can be given by:
Multiple Regression ModelMultiple Regression Model
wherewhere yy = number of cars sold = number of cars sold
xx11 = number of ads = number of ads
xx22 = minimum down payment required (‘000) = minimum down payment required (‘000)
yy = = 00 + + 11xx1 1 + + 22xx2 2 + +
Estimated Regression EquationEstimated Regression Equation
y = 14.4 + 3.7 *y = 14.4 + 3.7 *xx11 – 25* – 25* xx22y = 14.4 + 3.7 *y = 14.4 + 3.7 *xx11 – 25* – 25* xx22
Interpretation? Interpretation? Estimated values of y?Estimated values of y? Error?Error? Prediction?Prediction?
Multiple Coefficient of DeterminationMultiple Coefficient of Determination
Relationship Among SST, SSR, SSERelationship Among SST, SSR, SSE
where:where: SST = total sum of squaresSST = total sum of squares SSR = sum of squares due to regressionSSR = sum of squares due to regression SSE = sum of squares due to errorSSE = sum of squares due to error
SST = SSR + SST = SSR + SSE SSE
2( )iy y 2( )iy y 2ˆ( )iy y 2ˆ( )iy y 2ˆ( )i iy y 2ˆ( )i iy y
Multiple Coefficient of DeterminationMultiple Coefficient of Determination
RR22 = 84.63/89.2 = .949 = 84.63/89.2 = .949
Adjusted Multiple Coefficient of Adjusted Multiple Coefficient of DeterminationDetermination
R Rn
n pa2 21 1
11
( )R Rn
n pa2 21 1
11
( )
Standard Error of EstimateStandard Error of Estimate
RR22 = SSR/SST = SSR/SST
1 pnSSEMSEs
Testing for Significance: Testing for Significance: t t Test Test
HypothesesHypotheses
Rejection RuleRejection Rule
Test StatisticsTest Statistics
Reject Reject HH00 if if pp-value -value << or or
if if tt << - -ttor or tt >> ttwhere where tt
is based on a is based on a t t distribution distribution
with with nn - - pp - 1 degrees of freedom. - 1 degrees of freedom.
0 : 0iH 0 : 0iH
: 0a iH : 0a iH
)( i
i
bSE
bt
Example: Testing for significance of coefficients
HypothesesHypotheses
Rejection RuleRejection RuleFor For = .05 and d.f. = ?, = .05 and d.f. = ?, tt.025.025 = =
0:
0:0
ia
i
H
H
Test StatisticsTest Statistics)( i
i
bSE
bt
Testing for Significance of Regression: Testing for Significance of Regression: F F TestTest
HypothesesHypotheses
Rejection RuleRejection Rule
Test StatisticsTest Statistics
HH00: : 11 = = 2 2 = . . . = = . . . = p p = 0= 0
HHaa: One or more of the parameters: One or more of the parameters
is not equal to zero.is not equal to zero.
FF = MSR/MSE = MSR/MSE
Reject Reject HH00 if if pp-value -value << or if or if FF > > FF
where where FF is based on an is based on an FF distribution distribution
with with pp d.f. in the numerator and d.f. in the numerator and
nn - - pp - 1 d.f. in the denominator. - 1 d.f. in the denominator.
The years of experience, score on the The years of experience, score on the aptitudeaptitudetest, and corresponding annual salary test, and corresponding annual salary ($1000s) for a ($1000s) for a sample of 20 programmers is shown on the sample of 20 programmers is shown on the nextnextslide.slide.
Example 2: Programmer Salary Survey
Multiple Regression ModelMultiple Regression Model
A software firm collected data for a sampleA software firm collected data for a sampleof 20 computer programmers. A suggestionof 20 computer programmers. A suggestionwas made that regression analysis couldwas made that regression analysis couldbe used to determine if salary was relatedbe used to determine if salary was relatedto the years of experience and the scoreto the years of experience and the scoreon the firm’s programmer aptitude test.on the firm’s programmer aptitude test.
4477115588101000116666
9922101055668844663333
787810010086868282868684847575808083839191
8888737375758181747487877979949470708989
24244343
23.723.734.334.335.835.83838
22.222.223.123.130303333
383826.626.636.236.231.631.629293434
30.130.133.933.928.228.23030
Exper.Exper. ScoreScore ScoreScoreExper.Exper.SalarySalary SalarySalary
Multiple Regression ModelMultiple Regression Model
Suppose we believe that salary (Suppose we believe that salary (yy) is) is
related to the years of experience (related to the years of experience (xx11) and the ) and the score onscore on
the programmer aptitude test (the programmer aptitude test (xx22) by the ) by the following following
regression model:regression model:
Multiple Regression ModelMultiple Regression Model
wherewhere yy = annual salary ($1000) = annual salary ($1000)
xx11 = years of experience = years of experience
xx22 = score on programmer aptitude test = score on programmer aptitude test
yy = = 00 + + 11xx1 1 + + 22xx2 2 + +
Solving for 0, 1 and 2:
A B C3839 Coeffic. Std. Err.40 Intercept 3.17394 6.1560741 Experience 1.4039 0.1985742 Test Score 0.25089 0.07735
Anova Table
Source of Variation
Sum of Squares
Degrees of Freedom
Mean Square
F-statistic
Regression 500.34 …… …….. ……….
Error …….. ……. …….
Total 599.8 ……..
Estimated Regression EquationEstimated Regression Equation
SALARY = 3.174 + 1.404(EXPER) + 0.251(SCORE)SALARY = 3.174 + 1.404(EXPER) + 0.251(SCORE)SALARY = 3.174 + 1.404(EXPER) + 0.251(SCORE)SALARY = 3.174 + 1.404(EXPER) + 0.251(SCORE)
bb11 = 1.404 implies that salary is expected to = 1.404 implies that salary is expected to increase by $1,404 for each additional year of increase by $1,404 for each additional year of experience (when the variable experience (when the variable score on score on programmer attitude testprogrammer attitude test is held constant). is held constant).
b2 = 0.251 implies that salary is expected to b2 = 0.251 implies that salary is expected to increase by $251 for each additional point increase by $251 for each additional point scored on the programmer aptitude test (when scored on the programmer aptitude test (when the variable the variable years of experienceyears of experience is held is heldconstant).constant).
Prediction
Suppose Bob had an experience of 4 years and had a score of 78 on the aptitude test. What would you estimate (or expect) his score to be?
= 3.174 + 1.404*(4) + 0.251(78)= 3.174 + 1.404*(4) + 0.251(78)
= 28.358= 28.358 Bob’s estimated salary is $28,358.Bob’s estimated salary is $28,358.
y
Error
Bob’s actual salary is $24000. How much error we made in estimating his salary based on his experience and score?
So, we shall overestimate Bob’s salary.
43582835824000ˆ yyerror
Multiple Coefficient of DeterminationMultiple Coefficient of Determination
Relationship Among SST, SSR, SSERelationship Among SST, SSR, SSE
where:where: SST = total sum of squaresSST = total sum of squares SSR = sum of squares due to regressionSSR = sum of squares due to regression SSE = sum of squares due to errorSSE = sum of squares due to error
SST = SSR + SST = SSR + SSE SSE
2( )iy y 2( )iy y 2ˆ( )iy y 2ˆ( )iy y 2ˆ( )i iy y 2ˆ( )i iy y
Multiple Coefficient of DeterminationMultiple Coefficient of Determination
RR22 = 500.3285/599.7855 = .83418 = 500.3285/599.7855 = .83418
RR22 = SSR/SST = SSR/SST
Adjusted Multiple Coefficient of Adjusted Multiple Coefficient of DeterminationDetermination
R Rn
n pa2 21 1
11
( )R Rn
n pa2 21 1
11
( )
2 20 11 (1 .834179) .814671
20 2 1aR
2 20 11 (1 .834179) .814671
20 2 1aR
Testing for Significance: Testing for Significance: t t Test Test
HypothesesHypotheses
Rejection RuleRejection Rule
Test StatisticsTest Statistics
Reject Reject HH00 if if pp-value -value << or or
if if tt << - -ttor or tt >> ttwhere where tt
is based on a is based on a t t distribution distribution
with with nn - - pp - 1 degrees of freedom. - 1 degrees of freedom.
0 : 0iH 0 : 0iH
: 0a iH : 0a iH
)( i
i
bSE
bt
Example
HypothesesHypotheses
Rejection RuleRejection RuleFor For = .05 and d.f. = 17, = .05 and d.f. = 17, tt.025.025 = 2.11 = 2.11
Reject Reject HH00 if if pp-value -value << .05 or if .05 or if tt >> 2.11 2.11
0:
0:
1
10
aH
H
Test StatisticsTest Statistics 07.7199.0
404.1
)( 1
1 bSE
bt
Since t=7.07 > tSince t=7.07 > t0.0250.025 =2.11, we reject H =2.11, we reject H00..
Testing for Significance of Regression: Testing for Significance of Regression: F F TestTest
HypothesesHypotheses
Rejection RuleRejection Rule
Test StatisticsTest Statistics
HH00: : 11 = = 2 2 = . . . = = . . . = p p = 0= 0
HHaa: One or more of the parameters: One or more of the parameters
is not equal to zero.is not equal to zero.
FF = MSR/MSE = MSR/MSE
Reject Reject HH00 if if pp-value -value << or if or if FF > > FF
where where FF is based on an is based on an FF distribution distribution
with with pp d.f. in the numerator and d.f. in the numerator and
nn - - pp - 1 d.f. in the denominator. - 1 d.f. in the denominator.
ExampleExample
HypothesesHypotheses HH00: : 11 = = 2 2 = 0= 0
HHaa: One or both of the parameters: One or both of the parameters
is not equal to zero.is not equal to zero.
Rejection RuleRejection Rule For For = .05 and d.f. = 2, 17; = .05 and d.f. = 2, 17; FF.05.05 = 3.59 = 3.59
Reject Reject HH00 if if pp-value -value << .05 or .05 or FF >> 3.59 3.59
Test StatisticsTest Statistics FF = MSR/MSE = MSR/MSE = 250.17/5.86 = 42.8= 250.17/5.86 = 42.8
FF = 42.8 = 42.8 >> F F0.050.05 = 3.59, so we can reject = 3.59, so we can reject HH00..