chapter17.ppt 386KB Dec 31 1997 01:39:24 PM

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Chapter XVII

Correlation

and

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Chapter Outline

1) Overview

2) ProductMoment Correlation

3) Partial Correlation

4) Nonmetric Correlation

5) Regression Analysis

6) Bivariate Regression

7) Statistics Associated with Bivariate Regression Analysis

8) Conducting Bivariate Regression Analysis

i. Scatter Diagram

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iii. Estimation of Parameters

iv. Standardized Regression Coefficient

v. Significance Testing

vi. Strength and Significance of Association

vii. Prediction Accuracy

viii. Assumptions

9) Multiple Regression

10) Statistics Associated with Multiple Regression

11) Conducting Multiple Regression

i. Partial Regression Coefficients

ii. Strength of Association

iii. Significance Testing

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12) Stepwise Regression

13) Multicolinearity

14) Relative Importance of Predictors

15) Cross Validation

16) Regression with Dummy Variables

17) Analysis of Variance and Covariance with Regression

18) Internet and Computer Applications

19) Focus on Burke

20) Summary

21) Key Terms and Concepts

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[image:5.720.58.680.29.525.2]

Table 17.1

Respondent

No Toward theAttitude City

Duration of

Residence Attached toImportance Weather

1 6 10 3

2 9 12 11

3 8 12 4

4 3 4 1

5 10 12 11

6 4 6 1

7 5 8 7

8 2 2 4

9 11 18 8

10 9 9 10

11 10 17 8

12 2 2 5

Explaining Attitude Toward the

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A Nonlinear Relationship

for Which r = 0

[image:6.720.11.706.18.507.2]

for Which r = 0

Figure 17.1

1

2

0

1

2

3

4

3

1

2

0

5

Y

6

3

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Conducting Bivariate Regression Analysis

Fig. 17.2

Plot the Scatter Diagram

Formulate the General Model

Estimate the Parameters

Estimate Standardized Regression Coefficients

Test for Significance

Determine the Strength and Significance of Association

Check Prediction Accuracy

Examine the Residuals

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Plot of Attitude with Duration

[image:8.720.10.708.26.451.2]

Plot of Attitude with Duration

Figure 17.3

4.5

2.25

6.75

9

11.25

13.5

9

3

6

15.75 18

Duration of Residence

A

tt

itu

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Bivariate Regression

[image:9.720.21.695.20.443.2]

Bivariate Regression

Figure 17.4

X2

X1

X3

X4

X5

Y

YJJ eJ

eJ

Y

YJJ

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[image:10.720.0.719.72.484.2]

Table 17.2

Multiple R .93608

R2 _.87624

Adjusted R2 .86387

Standard Error 1.22329

ANALYSIS OF VARIANCE

df Sum of Squares Mean Square

Regression 1 105.95222 105.95222

Residual 10 14.96444 1.49644

F = 70.80266 Significance of F = .0000

VARIABLES IN THE EQUATION

Variable b SE_b Beta (ß) T Significance of T

Duration .58972 .07008 .93608 8.414 .0000 (Constant) 1.07932 .74335 1.452 .1772

Bivariate Regression

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[image:11.720.25.695.21.454.2]

Decomposition of the Total

Variation in Bivariate Regression

Figure 17.5

X2

X1

X3

X4

X5

Y Y X X Total Total Varia tion Varia tion SS SSyy

Residual Variation

SS

SS_res_res

Explained Variation

SS

SS_reg_reg

Y

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Multiple R .97210

R2 _.94498

Adjusted R2 .93276

Standard Error .85974

ANALYSIS OF VARIANCE

df Sum of Squares Mean Square

Regression 2 114.26425 57.13213 Residual 9 6.65241 .73916

F = 77.29364 Significance of F = .0000

VARIABLES IN THE EQUATION

Variable b SE b Beta (ß) T Significance of T

[image:12.720.17.709.85.476.2]

Importance .28865 .08608 .31382 3.353 .0085 Duration .48108 .05895 .76363 8.160 .0000 (Constant) .33732 .56736 .595 .5668

Table 17.3

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Residual Plot Indicating that Variance

is Not Constant

[image:13.720.11.705.31.461.2]

is Not Constant

Figure 17.6

Predicted Y Values

R

es

id

ua

ls

R

es

id

ua

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Residual Plot Indicating a Linear

Relationship Between Residuals and Time

[image:14.720.14.702.18.484.2]

Relationship Between Residuals and Time

Figure 17.7

Time

R

es

id

ua

ls

R

es

id

ua

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Plot of Residuals Indicating that a

Fitted Model is Appropriate

[image:15.720.19.699.20.463.2]

Fitted Model is Appropriate

Figure 17.8

Predicted Y Values

R

es

id

ua

ls

R

es

id

ua

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Airline Companies in Asia were facing uncertainty and tough competition from U.S. carriers for a long time. Asian Airlines, hit by global recession and preemptive competitive deals, awakened to the realization of banding together to increase air patronage. Secondary data revealed that among the important factors leading to airline selection by consumers were price, ontime schedules, destinations, deals available, kitchen and food service, on flight service, etc. Asian airlines offered these services at par if not better. In fact, research showed that inflight and kitchen services may have been even better. So, why were they feeling the competitive pressure? Qualitative research in the form of focus groups revealed that the frequent flier program was a critical factor for a broad segment in general and the business segment in particular. A survey of international passengers was conducted and multiple regression analyses was used to analyze the data. The likelihood of flying and other choice measures served as the dependent variable and the set of service factors, including the frequent flier program, were the independent variables. The results indicated that frequent flier program, indeed, had a significant effect on the choice of an airline. Based on these findings, Cathay Pacific, Singapore International Airlines, Thai Airways International, and Malaysian Airline systems introduced a cooperative frequent flier program called Asia Plus available to all travelers. The program was the first time the Asian carriers offered free travel in return for regular patronage. A multimillion dollar marketing and advertising campaign was started in 1993 to promote Asia Plus. Frequent fliers, thus, flew from the clouds to the clear and the Asian airlines experienced increased passenger traffic.

R.I.P. 17.1

Frequent Fliers: Fly from

the Clouds to the Clear

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Marketing research has been targeted as a major source of ethical problems within

the discipline of marketing. In particular, marketing research has been charged

with engaging in: deception, conflict of interest, violation of anonymity, invasion

of privacy, data falsifications, dissemination of faulty research findings, and the

use of research as a guise to sell merchandise. It has been speculated that when a

researcher chooses to participate in unethical activities, that decision may be

influenced by organizational factors. Therefore, a study using multiple regression

analysis was designed to examine organizational factors as determinants of the

incidence of unethical research practices. Six organizational variables were used

as the independent variables, namely: extent of ethical problems within the

organization, top management actions on ethics, code of ethics, organizational

rank, industry category, and organizational role. The respondent's evaluation of

the incidence of unethical research practices served as the dependent variable.

Regression analysis of the data suggested that four of the six organization variables

influenced the extent of unethical research practice: extent of ethical problems

within the organization, top management actions on ethics, organizational role, and

industry category.

R.I.P. 17.2

Reasons for Researchers

Regressing to Unethical Behavior