Reading – Linear Regression Le (Chapter 8 through 8.1.6) C &S (Chapter 5:F,G,H)

Reading – Linear Regression

• Le (Chapter 8 through 8.1.6)• C &S (Chapter 5:F,G,H)

Issues with hypothesis testing

• Significance does not imply causality– Need a proper prospective experiment

• Significance does not imply practical importance– Trivial but significant differences

• Run lots of tests, will find significant difference by chance– With α = 0.05, expect 1 in 20 results to be sig. by chance

Issues with hypothesis testing

• Large p-values because sample size is small– Effect could exist but we may not have a large enough

sample size

• Outliers may cause problems especially in small samples.

Issues With Hypothesis Testing

What is the population of inference?

Example: A statistics class of n=15 women and n=5 men yield the following exam scores:

Women: mean = 90% SD = 10%Men: mean = 85% SD = 11%

Test the hypothesis that women did better on the exam then men.

Hypothesis tests and Confidence Intervals

bapba

nnstxx

11)( *

Two sampletest statistic:

bap

ba

nns

xxt

11

CI for differencein means:

If 95% CI excludes 0 then the p-value will be <0.05.

Linear Regression

• Investigate the relationship between two variables– Does blood pressure relate to age?– Does weight loss relate to blood pressure loss– Does income relate to education?– Do sales relate to years of experience?

• Dependent variable – The variable that is being predicted or explained

• Independent variable – The variable that is doing the predicting or explaining

• Think of data in pairs (xi, yi)

Linear Regression - Purpose

• Is there an association between the two variables– Does weight change relate to BP change?

• Estimation of impact– How much BP change occurs per pound of weight change

• Prediction – If a person loses 10 pounds how much of a drop in blood

pressure can be expected

Regression History

• Sir Francis Galton (1822-1911) studied the relationship between a father’s height and the son’s height.

• He found that although there was a relationship between father and son’s height the relationship was not perfect.

• If the father was above average in height so was the son (typically) but not as much above average. This was called regression to the mean

Example of Regression Equation

We know systolic BP increases with age. How much does it increase per year and is the increase constant over time?

SBP = 90 + 0.8*AGE

Interpretation: For each year of age SBP increases by 0.8 mmHg.

At age 50: SBP = 90 + 0.8*50 = 130 mmHg

At age 60: SBP = 90 + 0.8*60 = 138 mmHg

Y or Dependent Variable

X or Dependent Variable

Simple Linear Regression EquationSimple Linear Regression Equation

The The simple linear regression equationsimple linear regression equation is: is:

yy = = 00 + + 11xx

• Graph of the regression equation is a Graph of the regression equation is a straight line.straight line.

• 00 is the is the yy intercept of the regression line. intercept of the regression line.

• 11 is the slope of the regression line. is the slope of the regression line.

• yy is the mean value of is the mean value of yy for a given for a given xx value.value.

Simple Linear Regression ModelSimple Linear Regression Model

The equation that describes how y is related to x and an The equation that describes how y is related to x and an error term is called the error term is called the regression modelregression model..

The The simple linear regression modelsimple linear regression model is: is:

yy = = 00 + + 11xx + +

• 00 and and 11 are called are called parameters of the modelparameters of the model..

• is a random variable called theis a random variable called the error term error term..


Positive Linear RelationshipPositive Linear Relationship

EE((yy))

xx

Slope Slope 11

is positiveis positive

Regression lineRegression line

InterceptIntercept00


Negative Linear RelationshipNegative Linear Relationship

EE((yy))

xx

Slope Slope 11

is negativeis negative

Regression lineRegression lineInterceptIntercept00


No RelationshipNo Relationship

EE((yy))

xx

Slope Slope 11

is 0is 0

Regression lineRegression lineInterceptIntercept

00

Estimated Simple Linear Regression Estimated Simple Linear Regression EquationEquation

The The estimated simple linear regression estimated simple linear regression equationequation is: is:

• The graph is called the estimated The graph is called the estimated regression line.regression line.

• bb00 is the is the yy intercept of the line. intercept of the line.

• bb11 is the slope of the line. is the slope of the line.

• is the estimated value of is the estimated value of yy for a given for a given xx value.value.

0 1y b b x 0 1y b b x

yy

Estimation ProcessEstimation Process

Regression ModelRegression Modelyy = = 00 + + 11xx + +

Regression EquationRegression Equationyy = = 00 + + 11xx

Unknown ParametersUnknown Parameters00, , 11

Sample Data:Sample Data:x yx y

xx11 y y11

. .. . . .. . xxnn yynn

EstimatedEstimatedRegression EquationRegression Equation

Sample StatisticsSample Statistics

bb00, , bb11

bb00 and and bb11

provide estimates ofprovide estimates of00 and and 11

0 1y b b x 0 1y b b x

Least Squares MethodLeast Squares Method

Least Squares Criterion: Choose Least Squares Criterion: Choose and and to minimizeto minimize

where:where:

yyii = = observedobserved value of the dependent variable value of the dependent variable

for the for the iith observationth observation

S = Yi – 01

Estimation

Slope: Slope:

The Least Squares EstimatesThe Least Squares Estimates

21 )(

))((

xx

yyxxb

i

ii

21 )(

))((

xx

yyxxb

i

ii

0 1b y b x 0 1b y b x Intercept:Intercept:

Example Restaurant Student Population

(Thousands)Quarterly Sales

1 2 58

2 6 105

3 8 88

4 8 118

5 12 117

6 16 137

7 20 157

8 20 169

9 22 149

10 26 202

X-Y PLOT OF DATA

CalculationsObs Xi Yi Xi-XBAR Yi-YBAR (Xi – XBAR)*

(Yi – YBAR)

(Xi – XBAR)2

1 2 58 -12 -72 864 144

2 6 105 -8 -25 200 64

3 8 88 -6 -42 252 36

4 8 118 -6 -12 72 36

5 12 117 -2 -13 26 4

6 16 137 2 7 14 4

7 20 157 6 27 162 36

8 20 169 6 39 234 36

9 22 149 8 19 152 64

10 26 202 12 72 864 144

Tot 140 1300 2840 568

Estimates for Dataset

b1 = 2840/568 = 5 b0 = 130 – 5*14 = 60

Y = Sales; X = # thousands of students

Equation:

Y = 60 + 5* X

21 )(

))((

xx

yyxxb

i

ii

21 )(

))((

xx

yyxxb

i

ii0 1b y b x 0 1b y b x

DATA sales;INFILE DATALINES;INPUT restaurant studentpop quarsales;DATALINES;1 2 582 6 1053 8 884 8 1185 12 1176 16 1377 20 1578 20 1699 22 14910 26 202;

PROC PRINT DATA=sales;PROC MEANS DATA=sales;

PROC REG DATA=sales SIMPLE; MODEL quarsales = studentpop; PLOT quarsales * studentpop ;RUN;

OUTPUT FROM PROC REG

The REG Procedure

Descriptive Statistics

Uncorrected StandardVariable Sum Mean SS Variance Deviation

Intercept 10.00000 1.00000 10.00000 0 0studentpop 140.00000 14.00000 2528.00000 63.11111 7.94425quarsales 1300.00000 130.00000 184730 1747.77778 41.80643

Parameter Estimates

Parameter StandardVariable DF Estimate Error t Value Pr > |t|

Intercept 1 60.00000 9.22603 6.50 0.0002

studentpop 1 5.00000 0.58027 8.62 <.0001

REGRESSION EQUATION:

Y = 60.0 + 5.0*X

QUARSALES = 60 + 5*STUDENTPOP

The Coefficient of DeterminationThe Coefficient of Determination

Relationship Among SST, SSR, SSERelationship Among SST, SSR, SSE

SST = SSR + SSESST = 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

( ) ( ) ( )y y y y y yi i i i 2 2 2( ) ( ) ( )y y y y y yi i i i 2 2 2^^

The The coefficient of determinationcoefficient of determination is: is:

rr22 = SSR/SST = SSR/SST

where:where:

SST = total sum of squaresSST = total sum of squares

SSR = sum of squares due to SSR = sum of squares due to regressionregression

The Coefficient of DeterminationThe Coefficient of Determination

OUTPUT FROM PROC REG

Dependent Variable: quarsales

Analysis of Variance

Sum of MeanSource DF Squares Square F Value Pr > F

Model 1 SSR 14200 14200 74.25 <.0001

Error 8 SSE 1530 191.25000

Corrected Total 9 SST 15730

Root MSE 13.82932 R-Square 0.9027Dependent Mean 130.00000 Coeff Var 10.63794

Coefficient of Determination

42 130

46 115

42 148

71 100

80 156

74 162

70 151

80 156

85 162

72 158

64 155

81 160

41 125

61 150

75 165

First value is age

Second value is SBP

Find the regression equation

SBP = b0 + b1*age

Your TURN

Documents

Reading – Linear Regression Le (Chapter 8 through 8.1.6) C &S (Chapter 5:F,G,H)