Multivariate Data Analysis 7th Edition Jr, Joseph F Hair;Black, William C;Babin, Barry J;Anderson, Rolph E - Solutions

+Illustrations Each chapter includes humorous pictures
+Introduces each subject in a general overview
+Depth Goes into great depth on each subject
+to their preferred textbook style for a class, and specify Factor Level
+1. Ask three of your classmates to evaluate choice combinations based on the following variables and levels relative
+ Recognize the limitations of traditional conjoint analysis and select the appropriate alternative methodology (e.g., choice-based or adaptive conjoint) when necessary.
+ Compare a main effects model and a model with interaction terms and show how to evaluate the validity of one model versus the other.
+ Apply a choice simulator to conjoint results for the prediction of consumer judgments of new attribute combinations.
+ Assess the relative importance of the predictor variables and each of their levels in affecting consumer judgments.
+ Explain the impact of choosing rank choice versus ratings as the measure of preference.
+ Understand how to create factorial designs.
+ Formulate the experimental plan for a conjoint analysis.
+ Know the guidelines for selecting the variables to be examined by conjoint analysis.
+ Explain the managerial uses of conjoint analysis.
+probability in a logistic regression procedure.
+5. Explain the concept of odds and why it is used in predicting
+4. What are the unique characteristics of interpretation in logistic regression?
+dependent and independent variables?
+3. How does logistic regression handle the relationship of the
+What are the advantages and disadvantages of this decision?
+2. When would you employ logistic regression rather than discriminant analysis?
+analysis, regression analysis, logistic regression analysis, and analysis of variance?
+1. How would you differentiate among multiple discriminant
+ Understand the strengths and weaknesses of logistic regression compared to discriminant analysis and multiple regression.
+comparisons to both multiple regression and discriminant analysis.
+ Interpret the results of a logistic regression analysis and assessing predictive accuracy, with
+ Describe the method used to transform binary measures into the likelihood and probability measures used in logistic regression.
+ Identify the types of variables used for dependent and independent variables in the application of logistic regression.
+ State the circumstances under which logistic regression should be used instead of multiple regression.
+8. How does discriminant analysis handle the relationship of the dependent and independent variables?
+7. Why should a researcher stretch the loadings and centroid data in plotting a discriminant analysis solution?
+6. How does a two-group discriminant analysis differ from a three-group analysis?
+5. How would you determine whether the classification accuracy of the discriminant function is sufficiently high relative to chance classification?
+4. How would you determine the optimum cutting score?
+into analysis and holdout groups? How would you change this procedure if your sample consisted of fewer than 100 individuals or objects?
+3. What procedure would you follow in dividing your sample
+2. What criteria could you use in deciding whether to stop a discriminant analysis after estimating the discriminant function(s)? After the interpretation stage?
+analysis, regression analysis, logistic regression analysis, and analysis of variance?
+1. How would you differentiate among multiple discriminant
+ Justify the use of a split-sample approach for validation
+ Tell how to identify independent variables with discriminatory power.
+ Explain what a classification matrix is and how to develop one, and describe the ways to evaluate the predictive accuracy of the discriminant function.
+ Describe the two computation approaches for discriminant analysis and the method for assessing overall model fit.
+ Understand the assumptions underlying discriminant analysis in assessing its appropriateness for a particular problem.
+ Identify the major issues relating to types of variables used and sample size required in the application of discriminant analysis.
+ State the circumstances under which linear discriminant analysis should be used instead of multiple regression.
+7. Are influential cases always omitted? Give examples of occasions when they should or should not be omitted.
+Do any of these differences affect your interpretation of the regression equation?
+6. What are the differences between interactive and correlated independent variables?
+5. What is the difference in interpretation between regression coefficients associated with interval-scale independent variables and dummy-coded (0, 1) independent variables?
+4. Could you find a regression equation that would be acceptable as statistically significant and yet offer no acceptable interpretational value to management?
+3. How can nonlinearity be corrected or accounted for in the regression equation?
+2. Why is it important to examine the assumption of linearity when using regression?
+1. How would you explain the relative importance of the independent variables used in a regression equation?
+ Apply the diagnostic procedures necessary to assess influential observations.
+ Interpret the results of regression.
+ Select an estimation technique and explain the difference between stepwise and simultaneous regression.
+ Be aware of the assumptions underlying regression analysis and how to assess them.
+ Use dummy variables with an understanding of their interpretation.
+ Understand how regression helps us make predictions using the least squares concept.
+ Determine when regression analysis is the appropriate statistical tool in analyzing a problem.
+of an orthogonal rotation? What are the basic differences between them?
+8. When would the researcher use an oblique rotation instead
+7. What is the difference between Q-type factor analysis and cluster analysis?
+6. What are the differences between factor scores and summated scales? When is each most appropriate?
+with other multivariate statistical techniques?
+5. How and when should you use factor scores in conjunction
+4. How do you use the factor-loading matrix to interpret the meaning of factors?
+3. What guidelines can you use to determine the number of factors to extract? Explain each briefly.
+results of other multivariate techniques?
+2. How can factor analysis help the researcher improve the
+1. What are the differences between the objectives of data summarization and data reduction?
+ State the major limitations of factor analytic techniques.
+ Explain the additional uses of factor analysis.
+ Describe how to name a factor.
+ Explain the concept of rotation of factors.
+ Describe how to determine the number of factors to extract.
+ Identify the differences between component analysis and common factor analysis models.
+ Distinguish between R and Q factor analysis.
+ Understand the seven stages of applying factor analysis.
+ Distinguish between exploratory and confirmatory uses of factor analytic techniques.
+Differentiate factor analysis techniques from other multivariate techniques.
+7. Discuss the following statement: Multivariate analyses can be run on any data set, as long as the sample size is adequate
+6. Evaluate the following statement: In order to run most multivariate analyses, it is not necessary to meet all the assumptions of normality, linearity, homoscedasticity, and independence.
+delete a case with missing data versus the conditions under which a researcher would use an imputation method.
+5. Describe the conditions under which a researcher would
+Explain how each type affects the analysis of missing data.
+4. Distinguish between data that are missing at random(MAR) and missing completely at random (MCAR).
+3. Discuss why outliers might be classified as beneficial and as problematic.
+2. List potential underlying causes of outliers. Be sure to include attributions to both the respondent and the researcher.
+1. Explain how graphical methods can complement the empirical measures when examining data.
+ Understand how to incorporate nonmetric variables as metric variables
+ Determine the best method of data transformation given a specific problem.
+ Test your data for the assumptions underlying most multivariate techniques.
+ Identify univariate, bivariate, and multivariate outliers.
+ Understand the different types of missing data processes.1===+ Explain the advantages and disadvantages of the approaches available for dealing with missing data.
+ Assess the type and potential impact of missing data.
+ Select the appropriate graphical method to examine the characteristics of the data or relationships of interest.
=+8. Detail the model-building approach to multivariate analysis, focusing on the major issues at each step
=+How can the power be improved if it is deemed too low?

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Multivariate Data Analysis 7th Edition Jr, Joseph F Hair;Black, William C;Babin, Barry J;Anderson, Rolph E - Solutions

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