More Related Content Similar to © 2008 Prentice-Hall, Inc.Regression ModelsChapter 4.docx (20) More from LynellBull52 (20) © 2008 Prentice-Hall, Inc.Regression ModelsChapter 4.docx1. © 2008 Prentice-Hall, Inc.
Regression Models
Chapter 4
To accompany
Quantitative Analysis for Management, Tenth Edition, by
Render, Stair, and Hanna
Power Point slides created by Jeff Heyl
© 2009 Prentice-Hall, Inc.
© 2009 Prentice-Hall, Inc. 4 – *
Learning Objectives
Identify variables and use them in a regression model
Develop simple linear regression equations from sample data
and interpret the slope and intercept
Compute the coefficient of determination and the coefficient of
correlation and interpret their meanings
Interpret the F-test in a linear regression model
List the assumptions used in regression and use residual plots to
identify problems
After completing this chapter, students will be able to:
© 2009 Prentice-Hall, Inc. 4 – *
2. Learning Objectives
Develop a multiple regression model and use it to predict
Use dummy variables to model categorical data
Determine which variables should be included in a multiple
regression model
Transform a nonlinear function into a linear one for use in
regression
Understand and avoid common mistakes made in the use of
regression analysis
After completing this chapter, students will be able to:
© 2009 Prentice-Hall, Inc. 4 – *
Chapter Outline
4.1 Introduction
4.2 Scatter Diagrams
4.3 Simple Linear Regression
4.4 Measuring the Fit of the Regression Model
4.5 Using Computer Software for Regression
4.6 Assumptions of the Regression Model
© 2009 Prentice-Hall, Inc. 4 – *
Chapter Outline
4.7 Testing the Model for Significance
4.8 Multiple Regression Analysis
4.9 Binary or Dummy Variables
4.10 Model Building
4.11 Nonlinear Regression
4.12 Cautions and Pitfalls in Regression Analysis
© 2009 Prentice-Hall, Inc. 4 – *
3. IntroductionRegression analysis is a very valuable tool for a
managerRegression can be used toUnderstand the relationship
between variablesPredict the value of one variable based on
another variableSimple linear regression models have only two
variablesMultiple regression models have more variables
© 2009 Prentice-Hall, Inc. 4 – *
IntroductionThe variable to be predicted is called the dependent
variable Sometimes called the response variableThe value of
this variable depends on the value of the independent
variableSometimes called the explanatory or predictor variable
Independent variable
Dependent variable
Independent variable
= +
© 2009 Prentice-Hall, Inc. 4 – *
Scatter DiagramGraphing is a helpful way to investigate the
relationship between variablesA scatter diagram or scatter plot
is often used The independent variable is normally plotted on
the X axisThe dependent variable is normally plotted on the Y
axis
© 2009 Prentice-Hall, Inc. 4 – *
Triple A ConstructionTriple A Construction renovates old
4. homesThey have found that the dollar volume of renovation
work is dependent on the area payroll
Table 4.1TRIPLE A’S SALES
($100,000’s)LOCAL PAYROLL
($100,000,000’s) 63 84 96 54 4.52 9.55
© 2009 Prentice-Hall, Inc. 4 – *
Triple A Construction
Figure 4.1
12 –
10 –
8 –
6 –
4 –
5. 2 –
0 –
Sales ($100,000)
Payroll ($100 million)
| | | | | | | |
0 1 2 3 4 5 6 7 8
© 2009 Prentice-Hall, Inc. 4 – *
Simple Linear Regression
where
Y = dependent variable (response)
X = independent variable (predictor or explanatory)
= intercept (value of Y when X = 0)
= slope of the regression line
e = random errorRegression models are used to test if
there is a relationship between variablesThere is some random
error that cannot be predicted
© 2009 Prentice-Hall, Inc. 4 – *
Simple Linear Regression
True values for the slope and intercept are not known so they
are estimated using sample data
where
Y = dependent variable (response)
6. X = independent variable (predictor or explanatory)
b0 = intercept (value of Y when X = 0)
b1 = slope of the regression line
^
© 2009 Prentice-Hall, Inc. 4 – *
Triple A ConstructionTriple A Construction is trying to predict
sales based on area payroll
Y = Sales
X = Area payrollThe line chosen in Figure 4.1 is the one that
minimizes the errors
Error = (Actual value) – (Predicted value)
© 2009 Prentice-Hall, Inc. 4 – *
Triple A ConstructionFor the simple linear regression model,
the values of the intercept and slope can be calculated using the
formulas below
© 2009 Prentice-Hall, Inc. 4 – *
Triple A ConstructionRegression calculations
Table 4.2YX(X – X)2(X – X)(Y – Y) 63(3 – 4)2 = 1(3 – 4)(6
– 7) = 1 84(4 – 4)2 = 0(4 – 4)(8 – 7) = 0 96(6 – 4)2 = 4(6
– 4)(9 – 7) = 4 54(4 – 4)2 = 0(4 – 4)(5 – 7) = 0 4.52(2 – 4)2
7. = 4(2 – 4)(4.5 – 7) = 5 9.55(5 – 4)2 = 1(5 – 4)(9.5 – 7) =
2.5ΣY = 42
Y = 42/6 = 7ΣX = 24
X = 24/6 = 4Σ(X – X)2 = 10Σ(X – X)(Y – Y) = 12.5
8. © 2009 Prentice-Hall, Inc. 4 – *
Triple A ConstructionRegression calculations
Therefore
© 2009 Prentice-Hall, Inc. 4 – *
Triple A ConstructionRegression calculations
Therefore
sales = 2 + 1.25(payroll)
If the payroll next year is $600 million
© 2009 Prentice-Hall, Inc. 4 – *
Measuring the Fit
of the Regression Model
Regression models can be developed for any variables X and
YHow do we know the model is actually helpful in predicting Y
based on X?We could just take the average error, but the
positive and negative errors would cancel each other outThree
measures of variability areSST – Total variability about the
meanSSE – Variability about the regression lineSSR – Total
variability that is explained by the model
9. © 2009 Prentice-Hall, Inc. 4 – *
Measuring the Fit
of the Regression Model
Sum of the squares totalSum of the squared errorSum of squares
due to regressionAn important relationship
© 2009 Prentice-Hall, Inc. 4 – *
Measuring the Fit
of the Regression Model
Table 4.3YX(Y – Y)2Y(Y – Y)2(Y – Y)263(6 – 7)2 = 12 +
1.25(3) = 5.750.06251.563 84(8 – 7)2 = 12 + 1.25(4) =
7.0010 96(9 – 7)2 = 42 + 1.25(6) = 9.500.256.2554(5 – 7)2 =
42 + 1.25(4) = 7.0040 4.52(4.5 – 7)2 = 6.252 + 1.25(2) =
4.5006.25 9.55(9.5 – 7)2 = 6.252 + 1.25(5) =
8.251.56251.563∑(Y – Y)2 = 22.5∑(Y – Y)2= 6.875∑(Y – Y)2
= 15.625Y = 7SST = 22.5SSE= 6.875SSR = 15.625
10. ^
^
^
^
^
© 2009 Prentice-Hall, Inc. 4 – *
Measuring the Fit
of the Regression Model
Sum of the squares totalSum of the squared errorSum of squares
due to regressionAn important relationship
For Triple A Construction
SST = 22.5
11. SSE = 6.875
SSR = 15.625
© 2009 Prentice-Hall, Inc. 4 – *
Measuring the Fit
of the Regression Model
Figure 4.2
12 –
10 –
8 –
6 –
4 –
2 –
0 –
Sales ($100,000)
Payroll ($100 million)
| | | | | | | |
0 1 2 3 4 5 6 7 8
Y = 2 + 1.25X
^
Y – Y
12. Y – Y
^
Y
Y – Y
^
© 2009 Prentice-Hall, Inc. 4 – *
Coefficient of DeterminationThe proportion of the variability in
Y explained by regression equation is called the coefficient of
determinationThe coefficient of determination is r2About 69%
of the variability in Y is explained by the equation based on
payroll (X)For Triple A Construction
© 2009 Prentice-Hall, Inc. 4 – *
Correlation CoefficientThe correlation coefficient is an
expression of the strength of the linear relationshipIt will
always be between +1 and –1The correlation coefficient is rFor
Triple A Construction
© 2009 Prentice-Hall, Inc. 4 – *
Correlation Coefficient
13. Figure 4.3
*
*
*
*
(a) Perfect Positive
Correlation:
r = +1
X
Y
*
*
*
*
(c) No Correlation:
r = 0
X
Y
*
*
*
*
*
*
*
*
*
*
(d) Perfect Negative Correlation:
14. r = –1
X
Y
*
*
*
*
*
*
*
*
*
(b) Positive
Correlation:
0 < r < 1
X
Y
*
*
*
*
*
*
*
© 2009 Prentice-Hall, Inc. 4 – *
Using Computer Software for Regression
Program 4.1A
© 2009 Prentice-Hall, Inc. 4 – *
15. Using Computer Software for Regression
Program 4.1B
© 2009 Prentice-Hall, Inc. 4 – *
Using Computer Software for Regression
Program 4.1C
© 2009 Prentice-Hall, Inc. 4 – *
Using Computer Software for Regression
Program 4.1D
© 2009 Prentice-Hall, Inc. 4 – *
Using Computer Software for Regression
Program 4.1D
Correlation coefficient is called Multiple R in Excel
© 2009 Prentice-Hall, Inc. 4 – *
Assumptions of the Regression Model
Errors are independent
Errors are normally distributed
Errors have a mean of zero
Errors have a constant varianceIf we make certain assumptions
about the errors in a regression model, we can perform
statistical tests to determine if the model is useful A plot of the
residuals (errors) will often highlight any glaring violations of
the assumption
19. © 2009 Prentice-Hall, Inc. 4 – *
Estimating the VarianceErrors are assumed to have a constant
estimated using the mean squared error (MSE), s2
where
n = number of observations in the sample
k = number of independent variables
20. © 2009 Prentice-Hall, Inc. 4 – *
Estimating the VarianceFor Triple A ConstructionWe can
estimate the standard deviation, sThis is also called the standard
error of the estimate or the standard deviation of the regression
© 2009 Prentice-Hall, Inc. 4 – *
Testing the Model for SignificanceWhen the sample size is too
small, you can get good values for MSE and r2 even if there is
no relationship between the variablesTesting the model for
significance helps determine if the values are meaningfulWe do
this by performing a statistical hypothesis test
© 2009 Prentice-Hall, Inc. 4 – *
Testing the Model for SignificanceWe start with the general
hesis is that there is no
relationship between X and YThe alternate hypothesis is that
be rejected, we have proven there is a relationshipWe use the F
statistic for this test
© 2009 Prentice-Hall, Inc. 4 – *
Testing the Model for SignificanceThe F statistic is based on
the MSE and MSR
where
k = number of independent variables in the modelThe F
21. statistic is This describes an F distribution with
degrees of freedom for the numerator = df1 = k
degrees of freedom for the denominator = df2 = n – k
– 1
© 2009 Prentice-Hall, Inc. 4 – *
Testing the Model for SignificanceIf there is very little error,
the MSE would be small and the F-statistic would be large
indicating the model is usefulIf the F-statistic is large, the
significance level (p-value) will be low, indicating it is unlikely
this would have occurred by chanceSo when the F-value is
large, we can reject the null hypothesis and accept that there is
a linear relationship between X and Y and the values of the
MSE and r2 are meaningful
© 2009 Prentice-Hall, Inc. 4 – *
Steps in a Hypothesis Test
Specify null and alternative hypotheses
and 0.05
Calculate the value of the test statistic using the formula
© 2009 Prentice-Hall, Inc. 4 – *
Steps in a Hypothesis Test
Make a decision using one of the following methodsReject the
null hypothesis if the test statistic is greater than the F-value
from the table in Appendix D. Otherwise, do not reject the null
22. hypothesis:Reject the null hypothesis if the observed
significance level, or p-value, is less than the level of
© 2009 Prentice-Hall, Inc. 4 – *
Triple A Construction
Step 1.
(no linear relationship between X and Y)
(linear relationship exists between X and
Y)
Step 2.
Step 3.
Calculate the value of the test statistic
© 2009 Prentice-Hall, Inc. 4 – *
Triple A Construction
Step 4.
Reject the null hypothesis if the test statistic is greater
than the F-value in Appendix D
df1 = k = 1
df2 = n – k – 1 = 6 – 1 – 1 = 4
The value of F associated with a 5% level of significance
and with degrees of freedom 1 and 4 is found in Appendix D
F0.05,1,4 = 7.71
Fcalculated = 9.09
Reject H0 because 9.09 > 7.71
© 2009 Prentice-Hall, Inc. 4 – *
23. Triple A Construction
Figure 4.5We can conclude there is a statistically significant
relationship between X and YThe r2 value of 0.69 means about
69% of the variability in sales (Y) is explained by local payroll
(X)
F = 7.71
0.05
9.09
© 2009 Prentice-Hall, Inc. 4 – *
Analysis of Variance (ANOVA) TableWhen software is used to
develop a regression model, an ANOVA table is typically
created that shows the observed significance level (p-value) for
the calculated F value This can be compared to the level of
Table 4.4DFSSMSFSIGNIFICANCERegressionkSSRMSR =
SSR/kMSR/MSEP(F > MSR/MSE)Residualn - k - 1SSEMSE =
SSE/(n - k - 1)Totaln - 1 SST
24. © 2009 Prentice-Hall, Inc. 4 – *
ANOVA for Triple A ConstructionBecause this probability is
less than 0.05, we reject the null hypothesis of no linear
relationship and conclude there is a linear relationship between
X and Y
Program 4.1D (partial)
P(F > 9.0909) = 0.0394
© 2009 Prentice-Hall, Inc. 4 – *
Multiple Regression Analysis
Multiple regression models are extensions to the simple linear
model and allow the creation of models with several
independent variables
where
Y = dependent variable (response variable)
Xi = ith independent variable (predictor or explanatory
variable)
intercept (value of Y when all Xi = 0)
coefficient of the ith independent variable
k = number of independent variables
random error
25. © 2009 Prentice-Hall, Inc. 4 – *
Multiple Regression Analysis
To estimate these values, a sample is taken the following
equation developed
where
= predicted value of Y
b0 =
bi = sample coefficient of the ith variable (and is an
© 2009 Prentice-Hall, Inc. 4 – *
Jenny Wilson RealtyJenny Wilson wants to develop a model to
determine the suggested listing price for houses based on the
size and age of the houseShe selects a sample of houses that
have sold recently and records the data shown in Table 4.5
where
= predicted value of dependent variable (selling price)
b0 = Y intercept
X1 and X2 = value of the two independent variables
(square footage and age) respectively
b1 and b2 = slopes for X1 and X2 respectively
© 2009 Prentice-Hall, Inc. 4 – *
Jenny Wilson Realty
Table 4.5SELLING PRICE ($)SQUARE
FOOTAGEAGECONDITION 95,0001,92630Good
119,0002,06940Excellent 124,8001,72030Excellent
26. 135,0001,39615Good 142,0001,70632Mint
145,0001,84738Mint 159,0001,95027Mint
165,0002,32330Excellent 182,0002,28526Mint
183,0003,75235Good 200,0002,30018Good
211,0002,52517Good 215,0003,80040Excellent
219,0001,74012Mint
27. © 2009 Prentice-Hall, Inc. 4 – *
Jenny Wilson Realty
Program 4.2
© 2009 Prentice-Hall, Inc. 4 – *
Evaluating Multiple Regression Models
Evaluation is similar to simple linear regression modelsThe p-
value for the F-test and r2 are interpreted the sameThe
hypothesis is different because there is more than one
independent variableThe F-test is investigating whether all the
coefficients are equal to 0
© 2009 Prentice-Hall, Inc. 4 – *
Evaluating Multiple Regression Models
To determine which independent variables are significant, tests
are performed for each variable The test statistic is calculated
and if the p-
the null hypothesis is rejected
© 2009 Prentice-Hall, Inc. 4 – *
28. Jenny Wilson RealtyThe model is statistically significantThe p-
value for the F-test is 0.002r2 = 0.6719 so the model explains
about 67% of the variation in selling price (Y)But the F-test is
for the entire model and we can’t tell if one or both of the
independent variables are significantBy calculating the p-value
of each variable, we can assess the significance of the
individual variablesSince the p-value for X1 (square footage)
and X2 (age) are both less than the significance level of 0.05,
both null hypotheses can be rejected
© 2009 Prentice-Hall, Inc. 4 – *
Binary or Dummy Variables
Binary (or dummy or indicator) variables are special variables
created for qualitative dataA dummy variable is assigned a
value of 1 if a particular condition is met and a value of 0
otherwiseThe number of dummy variables must equal one less
than the number of categories of the qualitative variable
© 2009 Prentice-Hall, Inc. 4 – *
Jenny Wilson RealtyJenny believes a better model can be
developed if she includes information about the condition of the
property
X3 = 1 if house is in excellent condition
= 0 otherwise
X4 = 1 if house is in mint condition
= 0 otherwiseTwo dummy variables are used to describe
the three categories of conditionNo variable is needed for
“good” condition since if both X3 and X4 = 0, the house must
be in good condition
© 2009 Prentice-Hall, Inc. 4 – *
29. Jenny Wilson Realty
Program 4.3
© 2009 Prentice-Hall, Inc. 4 – *
Jenny Wilson Realty
Program 4.3
Model explains about 90% of the variation in selling price
F-value indicates significance
Low p-values indicate each variable is significant
© 2009 Prentice-Hall, Inc. 4 – *
Model Building
The best model is a statistically significant model with a high r2
and few variablesAs more variables are added to the model, the
r2-value usually increasesFor this reason, the adjusted r2 value
is often used to determine the usefulness of an additional
variableThe adjusted r2 takes into account the number of
independent variables in the model
30. © 2009 Prentice-Hall, Inc. 4 – *
Model Building
As the number of variables increases, the adjusted r2 gets
smaller unless the increase due to the new variable is large
enough to offset the change in kThe formula for r2 The formula
for adjusted r2
© 2009 Prentice-Hall, Inc. 4 – *
Model Building
In general, if a new variable increases the adjusted r2, it should
probably be included in the modelIn some cases, variables
contain duplicate informationWhen two independent variables
are correlated, they are said to be collinearWhen more than two
independent variables are correlated, multicollinearity
existsWhen multicollinearity is present, hypothesis tests for the
individual coefficients are not valid but the model may still be
useful
© 2009 Prentice-Hall, Inc. 4 – *
Nonlinear RegressionIn some situations, variables are not
linearTransformations may be used to turn a nonlinear model
into a linear model
*
*
*
*
*
*
*
*
*
31. Linear relationship
Nonlinear relationship
*
*
*
*
*
*
*
*
*
*
*
© 2009 Prentice-Hall, Inc. 4 – *
Colonel MotorsThe engineers want to use regression analysis to
improve fuel efficiencyThey have been asked to study the
impact of weight on miles per gallon (MPG)
Table 4.6MPGWEIGHT (1,000 LBS.)MPGWEIGHT (1,000
LBS.)124.58203.18134.66232.68154.02242.65182.53331.70193.
09361.95193.11421.92
32. © 2009 Prentice-Hall, Inc. 4 – *
Colonel Motors
Figure 4.6A
Linear model
| | | | |
1.00 2.00 3.00 4.00 5.00
MPG
45 –
40 –
35 –
30 –
25 –
20 –
15 –
10 –
5 –
0 –
Weight (1,000 lb.)
33. © 2009 Prentice-Hall, Inc. 4 – *
Colonel Motors
Program 4.4A useful model with a small F-test for significance
and a good r2 value
© 2009 Prentice-Hall, Inc. 4 – *
Colonel Motors
Figure 4.6B
Nonlinear model
| | | | |
1.00 2.00 3.00 4.00 5.00
MPG
45 –
40 –
35 –
30 –
25 –
20 –
15 –
10 –
34. 5 –
0 –
Weight (1,000 lb.)
© 2009 Prentice-Hall, Inc. 4 – *
Colonel MotorsThe nonlinear model is a quadratic modelThe
easiest way to work with this model is to develop a new
variableThis gives us a model that can be solved with linear
regression software
© 2009 Prentice-Hall, Inc. 4 – *
Colonel Motors
Program 4.5A better model with a smaller F-test for
significance and a larger adjusted r2 value
© 2009 Prentice-Hall, Inc. 4 – *
Cautions and PitfallsIf the assumptions are not met, the
35. statistical test may not be validCorrelation does not necessarily
mean causationMulticollinearity makes interpreting coefficients
problematic, but the model may still be goodUsing a regression
model beyond the range of X is questionable, the relationship
may not hold outside the sample data
© 2009 Prentice-Hall, Inc. 4 – *
Cautions and Pitfallst-tests for the intercept (b0) may be
ignored as this point is often outside the range of the modelA
linear relationship may not be the best relationship, even if the
F-test returns an acceptable valueA nonlinear relationship can
exist even if a linear relationship does notJust because a
relationship is statistically significant doesn't mean it has any
practical value
SST
SSE
SST
SSR
r
-
=
=
1
2
å
å
-
=
=
2
2
)
ˆ
(
51. ˆ
+
-
=
a
<
value
-
if
Reject
p
1. Discuss what is, and what is not, included in calculating
GDP.
2. Why does investment spending not equal saving in the
circular flow?
3. Why does the consumer price index exaggerate the inflation
rate?
4. Discuss the limitations of national income accounting.
5. Why do total leakages and total injections have to be equal?
1. What is the difference between anticipated and unanticipated
inflation? How do they differ in their effects on economic
agents? Does inflation have no effects on the economy if it is
anticipated? Explain
2. What is meant by "quality of labor”? How does improvement
in the quality of labor affect economic growth?
3. Explain how the law of diminishing marginal returns is
related to the per-worker production function.
4. What is meant by industrial policy? Discuss the strengths and
weaknesses of an industrial policy.
5. Describe the four types of unemployment. How do the four
types differ in their effects on the economy and on the
unemployed?
52. Instructions: I need each question (10 questions) answered in 75
words or greater. I need the book that is being used to be quoted
within and I need the reference for the book in APA format. The
book being used is ECON MACRO 3 by William A. McEachern.
Assignment due date is Tuesday (3/12) at midnight but I need
these back Tuesday (3/12) by 4:00pm so that I have time to read
over and submit them. 4:00 EST. This does not have to be
written in any certain form. Just type the answer underneath the
question and send it that way. Thanks so much!
cleaned dataFiscal
YearFiscal
QuarterCompany
NameAssetsCost of Goods SoldLong-Term
DebtInventoriesGross fixed assets (including Property Plant and
Equipment)SalesMarket ValueAccount PayableInventory
turnoverGross Margincapital intensityTrade Credit
Ratio20101AAR
CORP1532.733334.392331.099520.001355.412404.393609.2237
130.1120.657666153717.31%40.60%32.17%20102AAR
CORP1557.227379.795330.637524.548369.348454.858974.4917
129.7890.727194224516.50%41.32%28.53%20103AAR
CORP1655.991379.242330.206523.464420.67458.0351084.5592
200.6560.723735987817.20%44.56%43.81%20104AAR
CORP1703.727399.272329.802507.274417.764487.8261049.820
6185.0960.774730338818.15%45.16%37.94%20101ACETO
CORP203.31858.181054.3334.18470.609164.03722.631.070143
008217.60%7.15%32.05%20102ACETO
CORP215.04558.656069.7764.08670.91130.212634.4320.94523
3625317.28%5.53%48.56%20103ACETO
CORP226.39482.812069.4087.05599.347153.059636.0911.1899
64363716.64%9.23%36.33%20104ACETO
CORP231.85189.1720.5574.8576.913105.765145.62839.971.23
6225002615.69%8.45%37.79%20101AMPAL-AMERICAN
ISRAEL
56. CO565.27197.94765.903161.41381.332222.996299.518988.452
1.191113598311.23%33.51%39.67%20102CASTLE (A M) &
CO569.074208.69167.062168.53578.871240.132318.858896.25
71.264993271713.09%31.88%40.09%20103CASTLE (A M) &
CO569.89212.83468.437161.7877.616244.938304.140589.2561.
288672933413.11%32.42%36.44%20104CASTLE (A M) &
CO529.352204.761.127130.91776.715235.64423.172371.7641.3
98716078413.13%36.95%30.45%20101CATO CORP -CL
A491.989149.860106.71101.469261.963702.71586.2671.330090
796942.79%48.74%32.93%20102CATO CORP -CL
A493.573141.404095.72100.869234.701686.294479.8021.39706
5652339.75%51.31%34.00%20103CATO CORP -CL
A504.796127.1370120.557100.367200.975780.380887.221.1756
86735136.74%45.43%43.40%20104CATO CORP -CL
A522.092144.8610132.0299.773227.046721.0289105.5261.1470
64063636.20%43.04%46.48%20101COAST DISTRIBUTION
SYSTEMS51.47818.9912.80425.2472.06924.10218.64654.9910.
787444020621.21%7.57%20.71%20102COAST
DISTRIBUTION
SYSTEMS54.927.72813.27529.7761.97634.64717.9166.8051.00
7869436419.97%6.22%19.64%20103COAST DISTRIBUTION
SYSTEMS49.54926.1939.44127.1731.85232.24516.58714.4180.
919875678218.77%6.38%13.70%20104COAST
DISTRIBUTION
SYSTEMS47.78215.32110.11325.9121.70717.60618.03423.375
0.577225204912.98%6.18%19.17%20101CCOM GROUP
INC24.96311.462.19511.9871.23515.8981.39657.1790.9722163
30927.92%9.34%45.16%20102CCOM GROUP
INC26.39515.782.26311.1751.23721.5011.02416.7481.3625766
34126.61%9.97%31.38%20103CCOM GROUP
INC26.42315.9791.19411.7631.15422.0031.29787.7211.393233
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INC24.31814.8331.88610.7811.12720.6541.95516.5141.315915
542928.18%9.46%31.54%20101COMMERCIAL
METALS3619.7821250.81177.227663.2081381.0631402.25817
92.8204325.6041.864432170210.80%67.56%23.22%20102COM
58. 030.551955505914.29%22.85%16.50%20101DILLARDS INC -
CL
A4647.043914.261969.1321453.5512717.9771475.9911924.884
793.3930.663895657338.06%65.16%53.75%20102DILLARDS
INC -CL
A4413.68930.436956.3531328.2282697.6911414.2541529.0912
701.6950.668950337234.21%67.01%49.62%20103DILLARDS
INC -CL
A4712.091857.474909.6251708.5042649.7181367.3561585.038
31049.2310.564734721437.29%60.80%76.73%20104DILLARD
S INC -CL
A4374.1661273.892908.6291290.1472595.5141958.062382.286
4491.5360.849643389634.94%66.80%25.10%20101DOLLAR
GENERAL
CORP8977.0762048.3063399.8871604.7541360.8683111.31497
42.2272789.2741.311196121334.17%45.89%25.37%20102DOL
LAR GENERAL
CORP9179.4742115.2723350.8071738.4391377.633214.155995
1.1679941.7421.265420213634.19%44.21%29.30%20103DOLL
AR GENERAL
CORP9349.5742149.1763286.9071885.7531414.73223.4279615.
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R GENERAL
CORP9546.2222290.7633287.071765.4331524.5753486.104949
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NA RETAIL GROUP
INC1145.044240.29225.708181.136278.803404.0891099.33531
12.971.281164442940.53%60.62%27.96%20102ASCENA
RETAIL GROUP
INC1638.386361.61725.351242.458482.175594.121726.635512
6.2931.707375458639.13%66.54%21.26%20103ASCENA
RETAIL GROUP
INC1623.776373.87424.985263.985479.636665.4972210.48141
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RETAIL GROUP
INC1654.119419.48424.617320.345478.086710.8651939.91331
61. INC3731.9281000.558412.711867.303934.6551783.6967056.97
2347.5051.163558596743.91%51.87%19.48%20103GRAINGER
(W W)
INC3789.2371072.366427.495935.219942.3541899.4128225.85
57402.5681.189850664843.54%50.19%21.19%20104GRAINGE
R (W W)
INC3904.3771023.109420.446991.577963.6721826.6969581.79
56344.2951.061979576543.99%49.29%18.85%20101HAVERTY
FURNITURE364.4270.4666.71893.651172.62156.25353.290316
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FURNITURE367.70166.5776.60989.002170.608145.259268.532
418.0070.728999797454.17%65.72%12.40%20103HAVERTY
FURNITURE374.1672.3988.72788.184172.547157.305238.7869
21.1790.81719774753.98%66.18%13.46%20104HAVERTY
FURNITURE370.23975.2648.57491.938175.511162.234283.401
818.0880.835700247653.61%65.62%11.15%20101HAWKINS
INC161.64554.494025.84448.48874.665246.892215.1082.31048
7375727.02%65.23%20.23%20102HAWKINS
INC170.98350.955024.77148.94370.399363.373814.1672.01343
4752527.62%66.40%20.12%20103HAWKINS
INC171.73455.213024.41449.68270.62455.499614.5132.245115
380721.82%67.05%20.55%20104HAWKINS
INC185.00567.629029.21762.39581.957423.411623.352.522011
523217.48%68.11%28.49%20101HOME DEPOT
INC4361911069767611479254041686359397.7870511.0217381
27134.36%68.88%41.81%20102HOME DEPOT
INC4253512828772710759251901941047497.6659191.1537008
72433.91%70.07%30.49%20103HOME DEPOT
INC4174110913875210993250501659850737.857141.00340198
634.25%69.50%34.43%20104HOME DEPOT
INC401259883870710625250601512659677.7147170.91433065
0434.66%70.23%31.18%20101HUTTIG BUILDING
PRODUCTS
INC146.983.44251.920.2103.519.53335.91.719587628919.42%2
8.02%34.69%20102HUTTIG BUILDING PRODUCTS
INC164.1108.252.561.519.7133.931.214743.51.908289241619.1
62. 9%24.26%32.49%20103HUTTIG BUILDING PRODUCTS
INC147.9103.351.153.719.2127.220.563232.91.793402777818.7
9%26.34%25.86%20104HUTTIG BUILDING PRODUCTS
INC126.18441.946.217.9103.121.671326.11.681681681718.53%
27.93%25.32%20101ANIXTER INTL
INC2678.4974.3705943.185.21272.61588.1213602.21.04656533
6523.44%8.29%47.32%20102ANIXTER INTL
INC2778.4975.2686.6957.684.61270.51447.9314663.61.026148
261223.24%8.12%52.23%20103ANIXTER INTL
INC2827.11027.3650.7981.285.41344.91841.7069675.11.05972
7666623.62%8.01%50.20%20104ANIXTER INTL
INC2933.31056.3688.81002.784.61386.52050.1128648.71.0648
72221423.82%7.78%46.79%20101SEARS HOLDINGS
CORP2541772161391931675911004613897.15537340.8008434
60428.17%44.90%37.17%20102SEARS HOLDINGS
CORP248337635137894307485104587859.736730.8145737757
26.99%44.25%35.12%20103SEARS HOLDINGS
CORP260457121257011226744896787917.846160.6894848954
26.42%39.88%47.70%20104SEARS HOLDINGS
CORP242689476266391237365131448215.3331010.931347977
827.91%44.67%23.59%20101KAMAN
CORP784.41199.53981.75301.67281.223276.772647.583983.64
0.679935597627.90%21.21%30.22%20102KAMAN
CORP836.319228.84895.7306.76583.481317.087573.814995.31
0.752248794927.83%21.39%30.06%20103KAMAN
CORP860.111257.972100.55308.01686.917359.545680.9358101
.250.839232181928.25%22.01%28.16%20104KAMAN
CORP895.757251.863140.443316.89989.719365.109756.575895
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CO228811915569974772139762473814227.239633.9600992351
22.57%74.55%16.02%20102KROGER
CO232761455172774650140031876013428.1238513.088728507
722.44%75.07%20.53%20103KROGER
CO23755145507261525614106186671390441792.93761356752
2.05%72.85%22.39%20104KROGER
CO23505155487304496614147198851326842273.04206613192
63. 1.81%74.02%21.26%20101LIMITED BRANDS
INC65921140.2782523109816771931.5368707.7224851.068176
112440.97%60.43%25.11%20102LIMITED BRANDS
INC67221368.0232532108316412242.518281.725591.25449151
7739.00%60.24%24.93%20103LIMITED BRANDS
INC68911172.082519145616331983.3719434.196870.92326112
6440.90%52.87%34.64%20104LIMITED BRANDS
INC64511908.1082507103216103455.869386.045451.53384887
4644.79%60.94%15.77%20101LOWE'S COMPANIES
INC37414803055319899223791238839134.1670620.884945999
635.18%69.33%57.01%20102LOWE'S COMPANIES
INC34633935455338653222741436129513.0248881.008408796
934.87%72.02%34.04%20103LOWE'S COMPANIES
INC34341752555398543221801158629747.9649590.875203535
735.05%72.19%42.80%20104LOWE'S COMPANIES
INC33699675465378321220891048033579.243510.8009962049
35.55%72.64%41.52%20101MCKESSON
CORP2739925989227894298642745017528.76132962.7545310
0165.32%8.39%48.44%20102MCKESSON
CORP2679225978227987638602753415630.34128342.8559806
5085.65%8.94%46.61%20103MCKESSON
CORP3039626672230595479342824717876.52135812.9133806
6635.58%8.91%48.08%20104MCKESSON
CORP3088626958358792259912885319920.6140902.87215001
076.57%9.70%48.83%20101CVS CAREMARK
CORP612841865684541027580442376050123.7640431.809680
861421.48%43.91%17.02%20102CVS CAREMARK
CORP615241834694541038982482371839757.9238671.775648
470822.65%44.26%16.30%20103CVS CAREMARK
CORP617131832686531058583562371142736.2641621.747496
900922.71%44.12%17.55%20104CVS CAREMARK
CORP621691876286521069583222458947391.5140261.763345
864723.70%43.76%16.37%20101ENVIROSTAR
INC10.372.75803.0650.2053.6287.31540.9720.909030982223.9
8%6.27%26.79%20102ENVIROSTAR
INC9.5334.58502.3630.1896.1228.22910.6611.689388356725.1
66. CO12952255430994267528541897374.0717660.658501998239.
03%55.33%42.16%20104PENNEY (J C)
CO13042356030993213523157037590.96911330.951871657837
.58%61.95%19.87%20101PEP BOYS-MANNY MOE &
JACK1529.641354.224305.931561.351699.439510.033657.4366
218.4720.632278090730.55%55.48%42.83%20102PEP BOYS-
MANNY MOE &
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196.6760.62825553930.29%55.40%38.96%20103PEP BOYS-
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MANNY MOE &
JACK1556.672334.098295.122564.402700.981477.389733.0349
210.440.589761287430.02%55.40%44.08%20101PIER 1
IMPORTS
INC/DE653.055191.8629.5303.19356.518306.259929.855974.96
90.622232600237.35%15.71%24.48%20102PIER 1 IMPORTS
INC/DE668.286195.4189.5352.03657.024309.869714.633385.38
0.596487640236.94%13.94%27.55%20103PIER 1 IMPORTS
INC/DE684.376209.699.5338.43759.171353.7591144.506461.44
50.607380737640.73%14.88%17.37%20104PIER 1 IMPORTS
INC/DE743.577244.1139.5311.7764.773426.5831184.238757.42
10.750877797442.77%17.20%13.46%20101AGILYSYS
INC338.76927.8290.48225.85726.54946.787153.943687.791.38
0989008340.52%50.66%187.64%20102AGILYSYS
INC359.7232.1790.50522.55527.13449.941149.5715103.7251.3
29381145235.57%54.61%207.70%20103AGILYSYS
INC418.35439.8560.8922.52826.43158.997129.7828149.1241.7
6811658532.44%53.99%252.77%20104AGILYSYS
INC312.39827.1781.46120.63226.54346.956132.15293.4861.25
9406858242.12%56.26%199.09%20101PUBLIX SUPER
MARKETS
INC9817.7654560.77997.9421345.4844372.166548.66519790.2
51148.973.340303805930.36%76.47%17.55%20102PUBLIX
SUPER MARKETS
69. INC13758664463482270660486601829.5621072.721277902923.
28%74.42%24.33%20101TRINITY PLACE HOLDINGS
INC274.38767.7029.81691.617127.594121.445103.447753.6840
.77885085544.25%58.21%44.20%20102TRINITY PLACE
HOLDINGS
INC290.06464.9525.95199.429128.443102.073119.918459.3820
.679940956636.37%56.37%58.18%20103TRINITY PLACE
HOLDINGS
INC304.02969.39442.977103.093129.489120.739101.858450.43
90.685298387342.53%55.67%41.78%20104TRINITY PLACE
HOLDINGS
INC270.77469.29530.19276.595126.334100.87696.223741.7010
.771281332131.31%62.26%41.34%20101SYSCO
CORP10429.7057240.1612468.7831747.7733014.3419081.4261
4694.15291960.3544.26087447820.28%63.30%21.59%20102SY
SCO
CORP10276.9667078.092468.691790.3273072.7218868.499165
42.54761906.7454.0010683720.19%63.19%21.50%20103SYSC
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CORP10468.2277166.9362468.5171751.2393176.228945.09317
443.4092038.9224.047325956919.88%64.46%22.79%20104SYS
CO
CORP10313.7018260.8462472.6621771.5393203.82310348.477
16810.75941953.0924.689961161320.17%64.39%18.87%20101
RADIOSHACK
CORP2356.5523.2630.7688.7271.7991.72837.3494185.60.7698
07989447.24%28.29%18.72%20102RADIOSHACK
CORP2395.7503.2324.1646.2263.4962.32446.3199185.40.7539
14150947.71%28.96%19.27%20103RADIOSHACK
CORP2242.5547.1327.9759.1264.410022427.9086278.20.77862
3781445.40%25.83%27.76%20104RADIOSHACK
CORP2175.4770.9331.8723.7274.31309.81955.7428272.41.039
789587341.14%27.48%20.80%20101TRANSCAT
INC34.12814.7742.9317.0464.01320.62854.6757.5592.2813465
10228.38%36.29%36.64%20102TRANSCAT
INC35.22915.4330.0197.2974.09520.9254.09488.0772.1519905
71. A329.913222.21632.59535.582161.988302.784400.867.3286.36
2207429726.61%81.99%22.24%20102VILLAGE SUPER
MARKET -CL
A347.893229.15332.38935.694165.818315.309347.7580.7376.4
30018519627.32%82.29%25.61%20103VILLAGE SUPER
MARKET -CL
A339.473218.57932.30236.296166.77300.992360.378770.1876.
072482289227.38%82.13%23.32%20104VILLAGE SUPER
MARKET -CL
A357.129248.95241.83136.256175.286342.74366.465459.1766.
862719153227.36%82.86%17.27%20101WAL-MART STORES
INC17437172754357803550310292899811200399.04313722.11
9161702827.11%74.35%31.43%20102WAL-MART STORES
INC176944755543870234793103814103726187099.45339532.1
49595994127.16%74.90%32.73%20103WAL-MART STORES
INC186890739324389941059106542101952194091.11362081.9
49375098927.48%72.18%35.51%20104WAL-MART STORES
INC180663850654384236318107878116360197142.12335572.1
98715380526.89%74.81%28.84%20101WALGREEN
CO265481154123667474108651636438313.416950431.6183131
17929.47%59.25%30.82%20102WALGREEN
CO263341183623477200108911698734479.83846111.61319340
3330.32%60.20%27.14%20103WALGREEN
CO267071218323597110111541719931180.623145841.7027253
66929.16%61.07%26.65%20104WALGREEN
CO262751181424767378111841687025229.702445851.6308669
24429.97%60.25%27.18%20101WATSCO
INC1210.92384.54947.65458.23431.133509.7551844.1174176.6
050.885739227424.56%6.36%34.65%20102WATSCO
INC1417.568661.1920.574522.54531.361864.8051877.476344.8
421.348295589523.54%5.66%39.88%20103WATSCO
INC1316.924614.72422.664456.76830.999812.7871802.306823
5.6231.255418849724.37%6.36%28.99%20104WATSCO
INC1237.227500.1210.016391.92531.221657.2482051.3377182.
1851.178565158423.91%7.38%27.72%20101WEIS MARKETS
INC924.008472.9110216.51510.264664.256978.0113119.2762.1
72. 51918548428.81%70.21%17.96%20102WEIS MARKETS
INC940.871457.2610219.822506.153653.676885.2132114.4442.
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INC963.196450.0650230.053503.32639.9671052.5579123.3512.
000844679129.67%68.63%19.27%20104WEIS MARKETS
INC992.081477.9490231.021525.062662.4791084.8367134.278
2.073198662327.85%69.45%20.27%20101WEYCO GROUP
INC207.6636.926031.96926.60161.039266.76385.9391.0210142
12239.50%45.42%9.73%20102WEYCO GROUP
INC205.75829.384037.26626.01148.723258.62136.5910.848819
238839.69%41.11%13.53%20103WEYCO GROUP
INC216.0634.305047.8426.02557.136273.63767.5140.80617112
7839.96%35.23%13.15%20104WEYCO GROUP
INC223.43535.619056.11125.67562.333278.132910.360.685303
652742.86%31.39%16.62%20101WILLIAMS-SONOMA
INC1996.76408.0778.642502.387800.963717.6373108.4128177.
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SONOMA
INC1995.493453.9477.197518.623771.635775.5542844.160918
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SONOMA
INC2035.172469.6127.165586.256750.239815.5163412.510120
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SONOMA
INC2131.762654.0337.13513.381730.5561195.4513377.393622
7.9631.189543458445.29%58.73%19.07%20101FOOT LOCKER
INC2960888137114637812812396.42673590.81355932230.68%
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INC2913791137121937610962115.40583450.668921775927.83
%23.57%31.48%20103FOOT LOCKER
INC2912892137120238712802467.04722860.736885584530.31
%24.35%22.34%20104FOOT LOCKER
INC2896962137105938613922761.51322230.850950906730.89
%26.71%16.02%20101ZALE
CORP1382.158169.377465.5902.344229.32329.21151.5492358.
4620.206230240948.55%20.26%108.89%20102ZALE
77. 190.6330.50371047242.60%52.06%28.94%20104SAKS
INC2143.1538.73359.25671.383890.364866.3311909.176388.37
80.717344946237.81%57.01%10.20%20101CASUAL MALE
RETAIL GRP
INC189.8351.4161.48498.69339.37594.984195.060327.8190.54
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GRP
INC187.08752.1420.26594.24139.02397.251165.076627.4970.5
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GRP
INC204.4148.8020.133109.59140.0889.936210.612831.8350.47
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GRP
INC182.61360.855092.88939.051111.471200.692817.5520.6010
96404645.41%29.60%15.75%20101HANCOCK FABRICS
INC150.13834.87530.78693.00341.763.10342.819423.2710.378
052878644.73%30.96%36.88%20102HANCOCK FABRICS
INC162.30931.03842.091104.59841.94460.45535.940623.530.3
14148207848.66%28.62%38.92%20103HANCOCK FABRICS
INC163.82340.71538.142106.32842.04773.45426.468628.4620.
386059565944.57%28.34%38.75%20104HANCOCK FABRICS
INC140.92345.47531.85687.80439.33578.45328.697217.8420.4
68495662742.04%30.94%22.74%20101AMERICA'S CAR-
MART
INC261.97946.4336.59320.69223.40182.602256.61545.212.261
769648643.79%53.07%6.31%20102AMERICA'S CAR-MART
INC269.20347.2696.36222.88823.98882.611289.47625.6192.16
929784342.78%51.17%6.80%20103AMERICA'S CAR-MART
INC276.89548.154025.67924.91582.775265.63596.3671.982992
56741.83%49.24%7.69%20104AMERICA'S CAR-MART
INC276.40954.12947.53923.59525.53293.871256.96667.7422.1
97061330542.34%51.97%8.25%20101TIFFANY &
CO3418.707233.445464.171473.73673.786633.5866167.043816
4.6650.160908606863.15%31.38%25.99%20102TIFFANY &
CO3446.561243.518467.8551553.117661.387668.765321.35021
65.7570.16090539163.59%29.87%24.79%20103TIFFANY &
82. INC23.4253.1179.3113.17117.5995.6193.7535.3721.173569277
144.53%84.73%95.60%20104CALLOWAY'S NURSERY
INC22.7944.83711.7562.09717.4499.4153.7535.1951.83637053
9148.62%89.27%55.18%20101VALUEVISION MEDIA INC -
CL
A180.53679.24042.6927.339124.977100.67643.2511.826500858
636.60%39.04%34.61%20102VALUEVISION MEDIA INC -CL
A181.35379.021047.15627.443126.17759.561350.6951.7590321
21637.37%36.79%40.18%20103VALUEVISION MEDIA INC -
CL
A186.85585.234051.99726.651132.28376.742651.6181.7192419
79635.57%33.89%39.02%20104VALUEVISION MEDIA INC -
CL
A238.359119.2492539.825.775178.836233.870658.312.5981023
34533.32%39.31%32.61%20101ADDVANTAGE
TECHNOLOGIES
GP49.7446.88913.52731.9257.46510.2219.94572.5340.2116696
36832.59%18.95%24.79%20102ADDVANTAGE
TECHNOLOGIES
GP50.968.43613.06131.4277.39912.05523.22983.1490.2663215
05230.02%19.06%26.12%20103ADDVANTAGE
TECHNOLOGIES
GP51.1439.08912.51228.5467.30513.29828.60612.1780.303103
06331.65%20.38%16.38%20104ADDVANTAGE
TECHNOLOGIES
GP52.268.43812.05727.4117.22411.73429.92482.7520.3015887
19928.09%20.86%23.45%20101EZCORP INC -CL
A526.80462.5722.563.51552.378184.751838.190439.6920.9813
67044166.13%45.20%21.48%20102EZCORP INC -CL
A546.27862.1622056.40354.044176.5841012.757838.5921.0367
41773564.80%48.93%21.85%20103EZCORP INC -CL
A571.38358.98517.561.02759.045173.542912.993944.1941.004
598484266.01%49.17%25.47%20104EZCORP INC -CL
A606.41267.4051571.50262.293198.168986.4899.1351.0172113
27365.99%46.56%4.61%20101BON-TON STORES
INC1695.327414.341021.688676.085736.762675.211325.57118
84. 5.134596537131.77%93.23%5.32%20102GLACIER WATER
SERVICES70.30216.414116.9362.95941.52625.44981.571.2765
.474070368535.50%93.35%5.01%20103GLACIER WATER
SERVICES71.82617.977115.3582.89842.46628.26677.24681.86
46.138637527736.40%93.61%6.59%20104GLACIER WATER
SERVICES73.84515.596118.7823.44345.26923.30666.642.6514
.919097934133.08%92.93%11.37%20101CHRISTOPHER &
BANKS
CORP264.94772.857034.54392.884126.235328.20287.4431.995
016361142.28%72.89%5.90%20102CHRISTOPHER & BANKS
CORP260.81165.536040.10988.466101.339230.835411.8411.75
5773455535.33%68.80%11.68%20103CHRISTOPHER &
BANKS
CORP245.50377.548045.97383.955120.947189.99857.8031.801
723937635.88%64.62%6.45%20104CHRISTOPHER & BANKS
CORP234.16376.772039.21176.64799.609217.766515.1491.802
498121722.93%66.16%15.21%20101STEIN MART
INC418.825209.1590237.52473.139300.998412.562882.7830.91
8070707930.51%23.54%27.50%20102STEIN MART
INC408.499202.5820218.37275.074275.955335.480374.9970.88
8720234426.59%25.58%27.18%20103STEIN MART
INC479.015195.530280.98677.313267.887408.0894137.5730.78
3125533227.01%21.58%51.35%20104STEIN MART
INC436.444244.6030232.29579.964336.67347.850595.5450.953
095867627.35%25.61%28.38%20101BUCKLE
INC508.594114.856084.741158.533214.7971690.546733.3071.3
28367875646.53%65.17%15.51%20102BUCKLE
INC509.177106.3790108.68168.048188.6391289.092138.6141.0
99973632643.61%60.73%20.47%20103BUCKLE
INC543.578129.7990111.235171.335243.3461355.652235.8911.
180446990946.66%60.63%14.75%20104BUCKLE
INC494.844149.894088.593169.234303.0561684.82633.4891.50
023019850.54%65.64%11.05%20101KOHL'S
CORP134662498175430177109403516936.9214120.841077441
138.09%70.21%34.99%20102KOHL'S
CORP137062450176629307310410014688.5213450.823944846
85. 140.24%71.39%32.80%20103KOHL'S
CORP147342595168140307274421815826.9821250.745689655
238.48%64.35%50.38%20104KOHL'S
CORP147793816351230368692603814776.9811381.080101896
436.80%74.11%18.85%20101BED BATH & BEYOND
INC5361.0261148.01501846.141103.3671923.05111840.564867
8.6850.636752626240.30%37.41%35.29%20102BED BATH &
BEYOND
INC5440.0121261.81201903.0961105.2972136.739320.2227771
.7520.673103533640.95%36.74%36.12%20103BED BATH &
BEYOND
INC5560.1471297.24702171.7831124.7042193.75511141.97778
56.6460.6367045540.87%34.12%39.05%20104BED BATH &
BEYOND
INC5646.1931428.501968.9071116.2972504.96712117.7179709
.550.689981621442.97%36.18%28.33%20101FINISH LINE INC
-CL
A616.821181.9970197.75132.041282.398893.222666.5330.9365
74345735.55%40.04%23.56%20102FINISH LINE INC -CL
A647.183194.4770217.04128.712301.07693.871297.1590.93771
3059635.40%37.23%32.27%20103FINISH LINE INC -CL
A676.092172.5320262.16130.091260.935939.3027114.9180.720
083472533.88%33.17%44.04%20104FINISH LINE INC -CL
A664.845239.1080193.505126.51384.599914.694572.781.04949
0305437.83%39.53%18.92%20101JEWETT-CAMERON
TRADING
CO19.5955.29606.8051.8747.37515.90020.2430.769767441928.
19%21.59%3.29%20102JEWETT-CAMERON TRADING
CO19.8425.91405.3821.9567.67515.06310.140.970542381222.9
4%26.66%1.82%20103JEWETT-CAMERON TRADING
CO21.6259.61305.8871.93612.48816.7370.7571.706096370623.
02%24.75%6.06%20104JEWETT-CAMERON TRADING
CO21.65911.21406.2661.92614.03415.83720.4651.8454702543
20.09%23.51%3.31%20101CENTRAL GARDEN & PET
CO1121.977174.236404.007327.403162.336269.236654.374411
7.6350.569178275735.29%33.15%43.69%20102CENTRAL
86. GARDEN & PET
CO1195.705273.515400.171330.57162.296441.936602.211147.
6980.831386698238.11%32.93%33.42%20103CENTRAL
GARDEN & PET
CO1183.707295.466400.138306.118162.352465.486578.408711
9.8690.928134345236.53%34.66%25.75%20104CENTRAL
GARDEN & PET
CO1130.884236.396400.106285.964165.281346.99638.0859112
.6110.798524528731.87%36.63%32.45%20101PATTERSON
COMPANIES
INC2447.952563.178525336.382178.721849.7873308.32180.47
21.801861121433.73%34.70%21.24%20102PATTERSON
COMPANIES
INC2550.047571.769525306.675182.83857.4143395.42164.342
1.778284040133.31%37.35%19.17%20103PATTERSON
COMPANIES
INC2503.585537.86525323.27185.572824.654063.074172.2271.
707641143334.78%36.47%20.88%20104PATTERSON
COMPANIES
INC2564.968574.025525336.094189.583883.8194203.381210.0
331.7411475335.05%36.06%23.76%20101BOOKS-A-MILLION
INC281.96481.7946.36210.25253.331116.968117.008891.6420.
397287753630.07%20.23%78.35%20102BOOKS-A-MILLION
INC279.45380.3730203.14353.948115.668101.347295.3950.388
843599930.51%20.98%82.47%20103BOOKS-A-MILLION
INC304.8773.8070230.47553.85102.69498.6409114.4150.34042
4059928.13%18.94%111.41%20104BOOKS-A-MILLION
INC274.802104.2260196.81454.71150.79588.024391.6170.4878
47803230.88%21.75%60.76%20101SPORT CHALET
INC138.3557.147099.39632.45779.68729.053825.2350.5811283
53228.29%24.62%31.67%20102SPORT CHALET
INC131.61563.755093.70829.98988.76328.165527.0710.660317
756228.17%24.24%30.50%20103SPORT CHALET
INC156.08669.4640108.07429.08695.82841.398645.4950.68850
5416727.51%21.21%47.48%20104SPORT CHALET
INC127.1269.765093.58826.8398.20528.995521.6060.69190030
90. STEEL INDS -CL
A1350.158582.89399.371291.273438.354703.541390.261390.97
22.355899013217.15%60.08%12.93%20104SCHNITZER
STEEL INDS -CL
A1343.418557.27899.24268.103460.81639.091213.10591.8791.
992498784412.80%63.22%14.38%20101WEST MARINE
INC341.00480.5580242.80955.051109.559243.148551.7680.366
639359226.47%18.48%47.25%20102WEST MARINE
INC332.89147.1090240.12955.223233.39244.840.6650.6092252
00836.97%18.70%17.42%20103WEST MARINE
INC329.43119.2510208.93854.665172.544229.311232.930.5311
05603430.89%20.74%19.08%20104WEST MARINE
INC308.88685.3170201.58856.483107.309239.372529.4030.415
647242820.49%21.89%27.40%20101NAVARRE
CORP165.22484.2141.16924.8211.29298.79279.284465.6143.2
96563062714.76%31.27%66.42%20102NAVARRE
CORP181.957103.0980.05527.69410.548120.47694.595874.512
3.926495791614.42%27.58%61.85%20103NAVARRE
CORP199.015129.3720.04228.4699.758147.32578.238492.644.
607018855812.19%25.53%62.88%20104NAVARRE
CORP173.866108.5460.05524.9139.299124.30469.498280.3794
.066764077812.68%27.18%64.66%20101TRACTOR SUPPLY
CO1340.543479.9861.324755.617365.838710.9172107.4472394
.9550.707492117932.48%32.62%55.56%20102TRACTOR
SUPPLY
CO1363.243705.5271.238702.405372.5421065.6562213.759728
9.5380.967786494333.79%34.66%27.17%20103TRACTOR
SUPPLY
CO1428.058553.4261.151758.683385.223829.1142887.1687348
.610.757553275433.25%33.68%42.05%20104TRACTOR
SUPPLY
CO1463.474695.7321.316736.52395.7891032.6493528.9082247
.3880.930618785532.63%34.95%23.96%20101OLYMPIC
STEEL
INC381.799132.53623.42129.274112.556167.901355.3371.141.
100171414121.06%46.54%42.37%20102OLYMPIC STEEL
92. INC951.672376.753195.662213.211102.401549.6581503.82191
28.0571.661820555731.46%32.45%23.30%20101EURO GROUP
OF COMPANIES
INC0.5331.1932.0970.0920.0710.0414.77791.0123.5611940299
-2882.50%43.56%2530.00%20102EURO GROUP OF
COMPANIES
INC0.5170.1422.1440.0920.0540.0195.76311.0121.5434782609
-647.37%36.99%5326.32%20103EURO GROUP OF
COMPANIES
INC0.5460.1792.2770.080.0360.09711.52611.0082.0813953488
-84.54%31.03%1039.18%20104EURO GROUP OF
COMPANIES
INC0.5410.0843.3860.0780.0180.021.26771.0091.0632911392-
320.00%18.75%5045.00%20101RELIANCE STEEL &
ALUMINUM
CO4571.7011083.19931.428845.275982.5051454.0753648.1399
299.2941.384100332925.51%53.75%20.58%20102RELIANCE
STEEL & ALUMINUM
CO4618.521211.038923.446896.66982.8881620.5852684.46292
91.7041.390451423325.27%52.29%18.00%20103RELIANCE
STEEL & ALUMINUM
CO4817.5231264.917944.231921.225985.361653.7983094.0265
326.3591.391635884623.51%51.68%19.73%20104RELIANCE
STEEL & ALUMINUM
CO4668.8931198.302857.789860.2151025.3051584.3373814.05
29244.9881.345318394124.37%54.38%15.46%20101TESSCO
TECHNOLOGIES
INC166.175108.5893.19452.8920.906141.953123.496576.9172.
218773625423.50%28.33%54.18%20102TESSCO
TECHNOLOGIES
INC184.718127.1713.09557.57721.394165.026113.657990.0252
.302425158622.94%27.09%54.55%20103TESSCO
TECHNOLOGIES
INC179.308132.9960.249.84921.761167.94120.454482.1312.47
6048628820.81%30.39%48.90%20104TESSCO
TECHNOLOGIES
98. PROPANE PRTNRS -
LP970.26160.937347.95361.047350.42168.0291922.358739.886
2.77788901354.22%85.16%23.74%20101RUSH ENTERPRISES
INC1007.31241.225174.154294.269355.636299.288481.986130.
1420.855068199919.40%54.72%10.07%20102RUSH
ENTERPRISES
INC1102.533260.66208.583308.891410.098329.839480.270138.
6860.864314609720.97%57.04%11.73%20103RUSH
ENTERPRISES
INC1144.724327.644223.329335.995414.754405.841556.05293
7.5331.016129982719.27%55.25%9.25%20104RUSH
ENTERPRISES
INC1167.933383.508224.081321.933445.919462.959741.12063
7.9331.165805376917.16%58.07%8.19%20101BIOSCRIP
INC714.563294.666316.6960.40622.514335.068423.581983.718
5.277820565612.06%27.15%24.99%20102BIOSCRIP
INC718.706336.183315.92854.08822.982412.03279.658874.193
5.87249986918.41%29.82%18.01%20103BIOSCRIP
INC743.881363.366314.75266.32222.723441.153277.030189.38
6.035478780817.63%25.52%20.26%20104BIOSCRIP
INC663.986375.435225.11766.50923.919450.372283.042483.85
15.652822006916.64%26.45%18.62%20101ABERCROMBIE &
FITCH -CL
A2738.596199.65170.603316.4471209.345687.8043856.854814
8.4390.636751864270.97%79.26%21.58%20102ABERCROMBI
E & FITCH -CL
A2856.863204.78475.967480.1281204.349745.7983259.807220
5.0250.514161252972.54%71.50%27.49%20103ABERCROMBI
E & FITCH -CL
A2922.777265.99581.67511.8211220.103885.7793763.5795202.
0440.536307814269.97%70.45%22.81%20104ABERCROMBIE
& FITCH -CL
A2947.902357.01368.566385.8571149.5831149.3964398.07091
37.2350.795414391468.94%74.87%11.94%20101EMPIRE
RESOURCES
INC163.19111.8251.735108.3674.167120.12615.110123.1581.0
99. 0096672846.91%3.70%19.28%20102EMPIRE RESOURCES
INC171.004107.0211.698105.0744.137114.82229.874321.481.0
0281576646.79%3.79%18.71%20103EMPIRE RESOURCES
INC174.149116.3571.66117.1474.106124.95233.766221.3461.0
4721875976.88%3.39%17.08%20104EMPIRE RESOURCES
INC190.12499.2081.621132.1964.078105.11348.146831.4820.7
9575524485.62%2.99%29.95%20101PENSKE AUTOMOTIVE
GROUP
INC3916.1752024.135862.7851384.231720.5752424.1871328.6
876213.5411.504506342616.50%34.23%8.81%20102PENSKE
AUTOMOTIVE GROUP
INC3838.6632188.655844.2921364.718707.8322603.811046.73
31209.5351.592357661115.94%34.15%8.05%20103PENSKE
AUTOMOTIVE GROUP
INC3975.5972218.104837.9761437.939731.8132630.0841215.5
88210.9991.582857980815.66%33.73%8.02%20104PENSKE
AUTOMOTIVE GROUP
INC4069.8322253.376769.2851524.226739.8472670.3041604.3
82261.9861.521438542415.61%32.68%9.81%20101STAGE
STORES
INC823.132235.8334.844349.195335.382340.042590.1903128.9
30.719481965730.65%48.99%37.92%20102STAGE STORES
INC812.166226.33931.491338.899327.87345.019420.926115.01
20.657872325634.40%49.17%33.33%20103STAGE STORES
INC854.132240.75428.449420.078326.056331.85487.9047184.1
710.634417116727.45%43.70%55.50%20104STAGE STORES
INC796.084288.42625.002325.501317.954453.679564.78995.36
50.773696684136.43%49.41%21.02%20101INGRAM MICRO
INC7721.9117638.528251.9642579.494221.2258095.9542902.6
6473832.6473.00765623585.65%7.90%47.34%20102INGRAM
MICRO
INC7692.4687702.805254.3172645.951223.5348156.3282380.0
7553997.9432.94819101535.56%7.79%49.02%20103INGRAM
MICRO
INC8478.8337984.718535.8662875.714228.5618453.8352643.6
6494133.2822.89214141025.55%7.36%48.89%20104INGRAM