Question: What are the main uses of regression

> What is a classification matrix?

> Explain the meaning of standardised regression coefficients.

> What is the difference between ordinal and disordinal interaction?

> What is the relationship between exploratory, descriptive and causal research?

> What is the most common use of the covariate in ANCOVA?

> How is the total variation decomposed in n-way analysis of variance?

> How does n-way analysis of variance differ from the one-way procedure?

> What is the most powerful test for making a posteriori contrasts? Which test is the most conservative?

> What is an a priori contrast?

> What is meant by a suppressed association? How is it revealed?

> Define a spurious correlation.

> What measures of variability are commonly computed?

> Which non-parametric tests are the counterparts of the two-independent samples t test for parametric data?

> Which non-parametric tests are the counterparts of the paired samples t test for parametric data?

> What is a causal research design? What is its purpose?

> Describe the major sources of error related to survey fieldwork.

> What is the major difference between parametric and non-parametric tests?

> How can the researcher ensure that the generated confidence interval will be no larger than the desired interval when estimating a population proportion?

> What are the circumstances under which a researcher would use a mobile app rather than a mobile web browser?

> Why do researchers need to be careful when carrying out telephone research with mobile devices?

> How is the sample size affected when the degree of confidence with which a population mean is estimated increases from 95% to 99%?

> How is the sample size affected when the absolute precision with which a population mean is estimated is doubled?

> Why do researchers need to be careful when using mobile-based image or video functionality as part of a mystery shopping exercise?

> What are the advantages to the researcher of having an app for their MROC?

> Evaluate the factors that have led to the growth of social media research.

> Describe cohort analysis. Why is it of special interest?

> What do you see as the major challenges for researchers that emerge from the ESOMAR definition of marketing research?

> What are the advantages of a ratio scale over an interval scale? Are these advantages significant?

> What is the procedure for constructing a confidence interval around a mean?

> What is the standard error of the mean?

> What is Wilks’ λ? For what purpose is it used?

> How should the total sample be split for estimation and validation purposes?

> What are the steps involved in conducting discriminant analysis?

> Describe the relationship of discriminant analysis to regression and ANOVA.

> Describe four examples of the application of discriminant analysis.

> How does the stepwise discriminant procedure differ from the direct method?

> When the groups are of equal size, how is the accuracy of chance classification determined?

> Compare and contrast cross-sectional and longitudinal designs.

> Describe a common procedure for determining the validity of discriminant analysis.

> How is the statistical significance of discriminant analysis determined?

> Explain the concept of structure correlations.

> What are the objectives of discriminant analysis?

> State the null hypothesis in testing the significance of the overall multiple regression equation. How is this null hypothesis tested?

> Explain the meaning of a partial regression coefficient. Why is it called that?

> What is multiple regression? How is it different from bivariate regression?

> What is meant by prediction accuracy? What is the standard error of the estimate?

> How is the strength of association measured in bivariate regression? In multiple regression?

> What is the least squares procedure?

> Define research design in your own words.

> Demonstrate the equivalence of regression with dummy variables to one-way ANOVA.

> What are some of the measures used to assess the relative importance of predictors in multiple regression?

> Describe the cross-validation procedure. Describe double cross-validation.

> What is multicollinearity? What problems can arise because of multicollinearity?

> Explain the stepwise regression approach. What is its purpose?

> What is gained by an examination of residuals?

> What is the product moment correlation coefficient? Does a product moment correlation of zero between two variables imply that the variables are not related to each other?

> How is the relative importance of factors measured in a balanced design?

> What is the null hypothesis in one-way ANOVA? What basic statistic is used to test the null hypothesis in one-way ANOVA? How is this statistic computed?

> What interrelated events occur in the environmental context of a research problem?

> What is total variation? How is it decomposed in a one-way analysis of variance?

> What is the relationship between analysis of variance and the t test?

> What is multivariate analysis of variance? When is it appropriate?

> Describe two tests used for examining differences in central tendencies in non-metric ANOVA.

> What are the differences between metric and non-metric analyses of variance?

> What is meant by repeated measures ANOVA? Describe the decomposition of variation in repeated measures ANOVA.

> Discuss the similarities and differences between analysis of variance and analysis of covariance.

> What is the general rule for computing percentages in cross-tabulations?

> What is the major difference between cross-tabulation and frequency distribution?

> What is a skewed distribution? What does it mean?

> Describe some of the reasons why management are often not clear about the ‘real’ research problem that needs to be addressed.

> How is the relative flatness or peakedness of a distribution measured?

> What measures of location are commonly computed?

> Describe the general procedure for conducting a t test.

> Present a classification of hypothesis testing procedures.

> Discuss the reasons for the frequent use of cross-tabulations. What are some of the limitations?

> Describe the procedure for computing frequencies.

> What options are available for the treatment of missing data?

> What kinds of consistency checks are made in cleaning the data?

> What does transcribing the data involve?

> Describe the guidelines for the coding of unstructured questions.

> What is the significance of the ‘background’ section of a research brief and research proposal?

> What is the difference between pre-coding and post-coding?

> How are unsatisfactory responses that are discovered in editing treated?

> What is meant by editing a questionnaire?

> What activities are involved in the preliminary checking of questionnaires that have been returned from the field?

> What considerations are involved in selecting a data analysis strategy?

> Which scale transformation procedure is most commonly used? Briefly describe this procedure.

> Explain why scale transformations are made.

> What are dummy variables? Why are such variables created?

> Describe the weighting process. What are the reasons for weighting?

> What kinds of statistical adjustments are sometimes made to the data?

> How may a researcher be creative in interpreting a research brief and developing a research proposal?

> Describe the data integrity process. Why is this process needed?

> What are wearables? How could they be used in marketing research?

> What is passive data? Give some examples of types of passive data collection that can be carried out with mobile devices.

> What does ‘mobile first’ mean for research designs?

> What kind of research activity is SMS-based research appropriate for?

> What do we mean by ‘mobile devices’? Where does the boundary lie between mobile and non-mobile devices?

> Discuss the key challenges relating to accessing social media data for research.

> Why is analysing image data on social media important?