bivariate linear regression

Bivariate Linear Regression

Linear Function

•Y = a + bX +e

Sources of Error

• Error in the measurement of X and or Y or in the manipulation of X.

• The influence upon Y of variables other than X (extraneous variables), including variables that interact with X.

• Any nonlinear influence of X upon Y.

The Regression Line

• r2 < 1 Predicted Y regresses towards mean Y

• Least Squares Criterion:

bXaY ˆxy rZZ ˆ

2ˆ YY

Our Beer and Burger DataSubject X Y Y )ˆ( YY 2)ˆ( YY 2)ˆ( YY

1 5 8 9.2 -1.2 1.44 10.24 2 4 10 7.6 2.4 5.76 2.56 3 3 4 6.0 -2.0 4.00 0.00 4 2 6 4.4 1.6 2.56 2.56 5 1 2 2.8 -0.8 0.64 10.24

Sum 15 30 30 0.0 14.40 25.60 Mean 3 6 St. Dev. 1.581 3.162

x

y

x

xy

x s

sr

s

COV

SS

SSCPb

26.1

581.1

162.38.

b

1.2 1.6(3)- 6 XbYa

40)162.3(4 2 YSS

Pearson r is a Standardized Slope

• Pearson r is the number of standard deviations that predicted Y changes for each one standard deviation change in X.

x

y

s

srb

Error Variance

• What is SSE if r = 0?

• If r2 > 0, we can do better than just predicting that Yi is mean Y.

8.43

4.14

2

n

SSEMSE

Y Y ySSYYSSE 2

4.14)40)(64.1())(1()ˆ( 22 ySSrYYSSE

Standard Error of Estimate

• Get back to the original units of measurement.

2

11 2

ˆ

n

nrsMSEs yyy

191.28.4ˆ yys

Regression Variance

• Variance in Y “due to” X

• p is number of predictors.• p = 1 for bivariate regression.

25.6 14.4 - 40ˆ 22 yyregr SSrSSESSYYSS

p

SSMS regr

regr

Coefficient of Determination

• The proportion of variance in Y explained by the linear model.

y

regr

SS

SSr 2

Coefficient of Alienation

• The proportion of variance in Y that is not explained by the linear model.

y

error

SS

SSr 21

Testing Hypotheses

• H: b = 0

• F = t2

• One-tailed p from F = two-tailed p from t

2 , ndfMSE

MSt regr

1 , , pnpdfMSE

MSF regr

Source Table

• MStotal is nothing more than the sample variance of the dependent

variable Y. It is usually omitted from the table.

Source SS df MS F p Regression 25.6 1 25.6 5.33 .104 Error 14.4 3 4.8 Total 40.0 4 10.0

Power Analysis

• Using Steiger & Fouladi’s R2.exe

Power = 13%

G*Power = 15%

Summary Statement

The linear regression between my friends’ burger consumption and their beer consumption fell short of statistical significance, r = .8,beers = 1.2 + 1.6 burgers, F(1, 3) = 5.33, p = .10. The most likely explanation of the nonsiginficant result is that it represents a Type II error. Given our small sample size (N = 5), power was only 13% even for a large effect (ρ = .5).

The Regression Line is Similar to a Mean

Subject X Y Y )ˆ( YY 2)ˆ( YY 2)ˆ( YY 1 5 8 9.2 -1.2 1.44 10.24 2 4 10 7.6 2.4 5.76 2.56 3 3 4 6.0 -2.0 4.00 0.00 4 2 6 4.4 1.6 2.56 2.56 5 1 2 2.8 -0.8 0.64 10.24

Sum 15 30 30 0.0 14.40 25.60 Mean 3 6 St. Dev. 1.581 3.162

0ˆ YY errorSSYY 2ˆ

regressionSSYY 2ˆ

Increase n to 10

• Same value of F• r2 = SSregression SStotal, = 25.6/64.0 = .4 (down

from .64).

Source SS df MS F p Regression 25.6 1 25.6 5.33 .05 Error 38.4 8 4.8 Total 64.0 9 7.1

Power Analysis

Power = 33%

G*Power = 36%

Increase n to 10

A .05 criterion of statistical significant was employed for all tests. An a priori power analysis indicated that my sample size (N = 10) would yield power of only 33% even for a large effect (ρ = .5). Despite this low power, the analysis yielded a statistically significant result. Among my friends, beer consumption increased significantly with burger consumption, r = .632,beers = 1.2 + 1.6 burgers, F(1, 8) = 5.33,p = .05.

Testing Directional Hypotheses

• H: b 0 H1: b > 0

• For F, one-tailed p = .05• half-tailed p = .025.• P(AB) = P(A)P(B) = .5(.05) = .025

025. tailed-one , 31.240.1

8632.

pt

Assumptions

• To test H: b = 0 or construct a CI• Homoscedasticity across Y|X• Normality of Y|X• Normality of Y ignoring X• No assumptions about X• No assumptions for descriptive statistics

(not using t or F)

Placing Confidence Limits on Predicted Values of Mean Y|X

• To predict the mean value of Y for all subjects who have some particular score on X:

•

x

yy SS

XX

nstYCI

2

ˆ

1ˆ

Placing Confidence Limits on Predicted Values of Individual Y|X

x

yy SS

XX

nstYCI

2

ˆ

11ˆ

Bowed Confidence Intervals

Testing Other Hypotheses

• Is the correlation between X and Y the same in one population as in another?

• Is the slope for predicting Y from X the same in one population as in another?

• Is the intercept for predicting Y from X the same in one population as in another.

Can differ on r but not slope or slope but not r.