Chap 2-1 Introduction to Linear Regression. Chap 2-2 Scatter Plots and Correlation A scatter plot...

Chap 2-1

Introduction to Linear Regression

Chap 2-2

Scatter Plots and Correlation

A scatter plot (or scatter diagram) is used to show the relationship between two variables

Correlation analysis is used to measure strength of the association (linear relationship) between two variables

Only concerned with strength of the relationship

No causal effect is implied

Chap 2-3

Scatter Plot Examples

y

x

y

x

y

y

x

x

Linear relationships Curvilinear relationships

Chap 2-4


y

x

y

x

y

y

x

x

Strong relationships Weak relationships

(continued)

Chap 2-5


y

x

y

x

No relationship

(continued)

Chap 2-6

Correlation Coefficient

The population correlation coefficient ρ (rho) measures the strength of the association between the variables

The sample correlation coefficient r is an estimate of ρ and is used to measure the strength of the linear relationship in the sample observations

(continued)

Chap 2-7

Features of ρand r

Unit free Range between -1 and 1 The closer to -1, the stronger the negative

linear relationship The closer to 1, the stronger the positive

linear relationship The closer to 0, the weaker the linear

relationship

Chap 2-8r = +.3 r = +1

Examples of Approximate r Values

y

x

y

x

y

x

y

x

y

x

r = -1 r = -.6 r = 0

Chap 2-9

Calculating the Correlation Coefficient

])yy(][)xx([

)yy)(xx(r

22

where:r = Sample correlation coefficientn = Sample sizex = Value of the independent variabley = Value of the dependent variable

])y()y(n][)x()x(n[

yxxynr

2222

Sample correlation coefficient:

or the algebraic equivalent:

Chap 2-10

Calculation Example

Tree Height

Trunk Diameter

y x xy y2 x2

35 8 280 1225 64

49 9 441 2401 81

27 7 189 729 49

33 6 198 1089 36

60 13 780 3600 169

21 7 147 441 49

45 11 495 2025 121

51 12 612 2601 144

=321 =73 =3142 =14111 =713

Chap 2-11

0

10

20

30

40

50

60

70

0 2 4 6 8 10 12 14

0.886

](321)][8(14111)(73)[8(713)

(73)(321)8(3142)

]y)()y][n(x)()x[n(

yxxynr

22

2222

Trunk Diameter, x

TreeHeight, y

Calculation Example(continued)

r = 0.886 → relatively strong positive linear association between x and y

Chap 2-12

Significance Test for Correlation

Hypotheses

H0: ρ = 0 (no correlation)

HA: ρ ≠ 0 (correlation exists)

Test statistic

(with n – 2 degrees of freedom)

2nr1

rt

2

Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 2-13

Example: Produce Stores

Is there evidence of a linear relationship between tree height and trunk diameter at the .05 level of significance?

H0: ρ = 0 (No correlation)

H1: ρ ≠ 0 (correlation exists)

=.05 , df = 8 - 2 = 6

4.68

28.8861

.886

2nr1

rt

22

Chap 2-14

4.68

28.8861

.886

2nr1

rt

22

Example: Test Solution

Conclusion:There is evidence of a linear relationship at the 5% level of significance

Decision:Reject H0

Reject H0Reject H0

/2=.025

-tα/2

Do not reject H0

0 tα/2

/2=.025

-2.4469 2.44694.68

d.f. = 8-2 = 6

Chap 2-15

Introduction to Regression Analysis

Regression analysis is used to: Predict the value of a dependent variable based on

the value of at least one independent variable Explain the impact of changes in an independent

variable on the dependent variable

Dependent variable: the variable we wish to explain

Independent variable: the variable used to explain the dependent variable

Chap 2-16

Simple Linear Regression Model

Only one independent variable, x

Relationship between x and y is described by a linear function

Changes in y are assumed to be caused by changes in x

Chap 2-17

Types of Regression Models

Positive Linear Relationship

Negative Linear Relationship

Relationship NOT Linear

No Relationship

Chap 2-18

εxββy 10 Linear component

Population Linear Regression

The population regression model:

Population y intercept

Population SlopeCoefficient

Random Error term, or residualDependent

Variable

Independent Variable

Random Error component

Chap 2-19

Linear Regression Assumptions

Error values (ε) are statistically independent Error values are normally distributed for any

given value of x The probability distribution of the errors is

normal The probability distribution of the errors has

constant variance The underlying relationship between the x

variable and the y variable is linear

Chap 2-20

Population Linear Regression(continued)

Random Error for this x value

y

x

Observed Value of y for xi

Predicted Value of y for xi

εxββy 10

xi

Slope = β1

Intercept = β0

εi

Chap 2-21

i 0 1ˆ ˆy x

The sample regression line provides an estimate of the population regression line

Estimated Regression Model

Estimate of the regression

intercept

Estimate of the regression slope

Estimated (or predicted) y value

Independent variable

The individual random error terms ei have a mean of zero

Chap 2-22

Least Squares Criterion

and are obtained by finding the values of and that minimize the sum of the squared residuals

2 2

20 1

ˆe (y y)

ˆ ˆ(y ( x))

01

10

Chap 2-23

The Least Squares Equation

The formulas for and are:

algebraic equivalent:

1 22

ˆ( )

xy

xx

x yxy Sn

x Sx

n

1 2

( )( )ˆ( )

xy

xx

Sx x y y

x x S

0 1ˆ ˆy x

and

01

Chap 2-24

is the estimated average value of y when the value of x is zero

is the estimated change in the average value of y as a result of a one-unit change in x

Interpretation of the Slope and the Intercept

0

1

Chap 2-25

Finding the Least Squares Equation

The coefficients and will usually be found using computer software, such as Minitab

Other regression measures will also be computed as part of computer-based regression analysis

01

Chap 2-26

Simple Linear Regression Example

A real estate agent wishes to examine the relationship between the selling price of a home and its size (measured in square feet)

A random sample of 10 houses is selected Dependent variable (y) = house price in $1000s Independent variable (x) = square feet

Chap 2-27

Sample Data for House Price Model

House Price in $1000s(y)

Square Feet (x)

245 1400

312 1600

279 1700

308 1875

199 1100

219 1550

405 2350

324 2450

319 1425

255 1700

Chap 2-28

Regression Using MINATAB

Stat / Regression/ RegressionRegression Analysis: House price

versus Square Feet

The regression equation is House price = 98.2 + 0.110 Square Feet

Predictor Coef SE Coef T P

Constant 98.25 58.03 1.69 0.129

Square Feet 0.110 0.03297 3.33 0.010

S = 41.3303 R-Sq = 58.1% R-Sq(adj) = 52.8%

MINITAB OUTPUT

Analysis of Variance

Source DF SS MS F P

Regression 1 18935 18935 11.08 0.010

Residual Error 8 13666 1708

Total 9 32600

Chap 2-29

Chap 2-30

Graphical Presentation

Stat /Regression/ Fitted Line Plot

Chap 2-31

Interpretation of the Intercept,

is the estimated average value of Y when the value of X is zero (if x = 0 is in the range of observed x values) Here, no houses had 0 square feet, so = 98.25

just indicates that, for houses within the range of sizes observed, $98,250 is the portion of the house price not explained by square feet

0

House price = 98.25 + 0.110 Square Feet

0

0

Chap 2-32

Interpretation of the Slope Coefficient,

measures the estimated change in the average value of Y as a result of a one-unit change in X Here, = .110 tells us that the average value of a

house increases by 0.110 ($1000) = $110, on average, for each additional one square foot of size

1

1


1

Chap 2-33

Least Squares Regression Properties

The sum of the residuals from the least squares regression line is 0 ( )

The sum of the squared residuals is a minimum (minimized )

The simple regression line always passes through the mean of the y variable and the mean of the x variable

The least squares coefficients are unbiased estimates of β0 and β1

0)ˆ( yy

2)ˆ( yy

Chap 2-34

Explained and Unexplained Variation

Total variation is made up of two parts:

Re RSS SS SST s Total sum of

SquaresSum of Squares

RegressionSum of Squares Error (Residual)

2( )TSS y y 2Re ˆ( )sSS y y 2ˆ( )RSS y y

where:

= Average value of the dependent variable

y = Observed values of the dependent variable

= Estimated value of y for the given x valuey

y

Chap 2-35

= total sum of squares

Measures the variation of the yi values around their mean y

= error (residual) sum of squares

Variation attributable to factors other than the relationship between x and y

= regression sum of squares

Explained variation attributable to the relationship between x and y

(continued)


SST

TSS

Re sSS

RSS

Chap 2-36

(continued)

Xi

y

x

yi

= (yi - y)2

= (yi - yi )2

= (yi - y)2

_

_

_


y

y

y_y

TSS

Re sSS

RSS

Chap 2-37

The coefficient of determination is the portion of the total variation in the dependent variable that is explained by variation in the independent variable

The coefficient of determination is also called R-squared and is denoted as R2

Coefficient of Determination, R2

2 R

T

SSR

SS 1R0 2 where

Chap 2-38

Coefficient of determination

Coefficient of Determination, R2

2 of squares explained by regression

sum of squaresR

T

SS sumR

SS total

(continued)

Note: In the single independent variable case, the coefficient of determination is

where:R2 = Coefficient of determination

r = Simple correlation coefficient

22 rR

Chap 2-39

R2 = +1

Examples of Approximate R2 Values

y

x

y

x

R2 = 1

R2 = 1

Perfect linear relationship between x and y:

100% of the variation in y is explained by variation in x

Chap 2-40


y

x

y

x

0 < R2 < 1

Weaker linear relationship between x and y:

Some but not all of the variation in y is explained by variation in x

Chap 2-41


R2 = 0

No linear relationship between x and y:

The value of Y does not depend on x. (None of the variation in y is explained by variation in x)

y

xR2 = 0

Chap 2-42

Standard Error Estimation



Constant 98.25 58.03 1.69 0.129

Square Feet 0.110 0.03297 3.33 0.010

S = 41.3303 R-Sq = 58.1% R-Sq(adj) = 52.8%

Source DF SS MS F P

Regression 1 18935 18935 11.08 0.010

Residual Error 8 13666 1708

Total 9 32600

Chap 2-43

Standard Error of Estimate

The standard deviation of the variation of observations around the regression line is estimated by

ReRe 1

ss

SSMS

n k

Where

= Sum of squares error (residual) n = Sample size

k = number of independent variables in the model

Re sSS

Chap 2-44

The Standard Deviation of the Regression Slope

The standard error of the regression slope coefficient ( ) is estimated by

1 2 22

ˆSe( )(x x) ( x)

xn

Res ResMS MS

where:

= Estimate of the standard error of the least squares slope

= Sample standard error of the estimate

1( )Se

ReRe

SS

n 2s

sMS

1

Chap 2-45

Comparing Standard Errors

y

y y

x

x

x

y

x

1small ( )Se

1large ( )Se

small ResMS

Relarge MS s

Variation of observed y values from the regression line

Variation in the slope of regression lines from different possible samples

Chap 2-46

Inference about the Slope: t Test

t test for a population slope Is there a linear relationship between x and y?

Null and alternative hypotheses H0: β1 = 0 (no linear relationship) H1: β1 0 (linear relationship does exist)

Test statistic

1 1

1

ˆ βt

ˆ( )Se

2nd.f.

where:

= Sample regression slope coefficient

β1 = Hypothesized slope

= Estimator of the standard error of the slope

1

1( )Se

Chap 2-47

House Price in $1000s

(y)

Square Feet (x)

245 1400

312 1600

279 1700

308 1875

199 1100

219 1550

405 2350

324 2450

319 1425

255 1700

Estimated Regression Equation:

The slope of this model is 0.110

Does square footage of the house affect its sales price?

Inference about the Slope: t Test

(continued)


Chap 2-48

Inferences about the Slope: t Test Example

H0: β1 = 0

HA: β1 0

Test Statistic: t = 3.33

There is sufficient evidence that square footage affects house price

From MINITAB output:

Reject H0

Decision:

Conclusion:

Reject H0Reject H0

/2=.025

-tα/2

Do not reject H0

0 tα/2

/2=.025

-2.3060 2.3060 3.329

d.f. = 10-2 = 8


Constant 98.25 58.03 1.69 0.129

Square Feet 0.110 0.03297 3.33 0.010

Chap 2-49

Regression Analysis for Description

Confidence Interval Estimate of the Slope:

At 95% level of confidence, the confidence interval for the slope is (0.0337, 0.1858)

1 /2 1ˆ ˆ( )t Se d.f. = n - 2

This 95% confidence interval does not include 0.

Conclusion: There is a significant relationship between house price and square feet at the .05 level of significance

Chap 2-50

Confidence Interval for the Average y, Given x

Confidence interval estimate for the mean of y given a particular

Size of interval varies according to distance away from mean, x

20

/2 Re 2

(x x)1ˆ MS [ ]

n (x x)sy t

0x

Chap 2-51

Confidence Interval for an Individual y, Given x

Confidence interval estimate for an Individual value of y given a particular

20

/2 Re 2

(x x)1ˆ MS [1 ]

n (x x)sy t

This extra term adds to the interval width to reflect the added uncertainty for an individual case

0x

Chap 2-52

Interval Estimates for Different Values of x

y

x

Prediction Interval for an individual y, given

xp

y = + x

x

Confidence Interval for the mean of y, given

10

0x

0x

Chap 2-53

House Price in $1000s

(y)

Square Feet (x)

245 1400

312 1600

279 1700

308 1875

199 1100

219 1550

405 2350

324 2450

319 1425

255 1700

Estimated Regression Equation:

Example: House Prices

Predict the price for a house with 2000 square feet


Chap 2-54

house price 98.25 0.110 (sq.ft.)

98.25 0.110(2000)

318.25

Example: House Prices

Predict the price for a house with 2000 square feet:

The predicted price for a house with 2000 square feet is 318.25($1,000s) = $318,250

(continued)

Chap 2-55

Estimation of Mean Values: Example

Find the 95% confidence interval for the average price of 2,000 square-foot houses

Predicted Price Yi = 318.25 ($1,000s)

Confidence Interval Estimate for E(y)|

20

α/2 2

(x x)1y t [ ] 318.25 37.12

n (x x)ResMS

The confidence interval endpoints are 281.13 -- 355.37, or from $281,13 -- $355,37

0x

Chap 2-56

Estimation of Individual Values: Example

Find the 95% confidence interval for an individual house with 2,000 square feet

Predicted Price Yi = 317.85 ($1,000s)

Prediction Interval Estimate for y|

20

α/2 Re 2

(x x)1y t MS [1 ] 318.25 102.28

n (x x)s

The prediction interval endpoints are 215.97 -- 420.53, or from $215,97 -- $420,53

0x

Chap 2-57

Finding Confidence and Prediction Intervals MINITAB

Stat\Regression\Regression-option

Predicted Values for New Observations

New Obs Fit SE Fit 95% CI 95% PI

1 317.8 16.1 (280.7, 354.9) (215.5, 420.1)

Values of Predictors for New Observations

Square Feet

New Obs

1 2000

Chap 2-58

Finding Confidence and Prediction Intervals MINITAB

Stat\Regression\fitted line plot

Chap 2-1 Introduction to Linear Regression. Chap 2-2 Scatter Plots and Correlation A scatter plot...

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