Explore BrainMass

Explore BrainMass

    Statistical Analysis and Research

    This content was COPIED from BrainMass.com - View the original, and get the already-completed solution here!

    I have updated number 1.............

    1. What are differences (difference analysis) and describe the three ways that a researcher can investigate for differences?
    2. What is a relationship between two variables, and how does a relationship help a marketing manager? Give an example using a demographic variable and a consumer behavior variable, such as satisfaction with a brand.
    3. How does multiple regression differ from bivariate regression? How is it similar?
    4. What is ANOVA, and when is it used? Why is it termed efficient?
    5. Explain why the statistical significance of a correlation is important. That is, what must be assumed when the correlation is found to not be statistically significant?

    © BrainMass Inc. brainmass.com October 10, 2019, 8:11 am ad1c9bdddf

    Solution Preview

    1. In research, the difference analysis is also referred to as the difference between means or comparisons between two means. In many instances, researchers are looking for information about two groups within a population to make comparisons. The useful statistical tools to use for comparing two means would be confidence intervals and tests of significance. The three ways a researcher can investigate differences is 1) confidence interval for the difference between two means; 2) tests of significance for two unknown means and known standard deviations; 3) tests of significance for two unknown means and unknown standard deviations.

    2. The relationship between two variables describes the cause and effect between two variables. Variables are categorized into independent variables (IV) and ...

    Solution Summary

    The following explains the concept of the relationship between variables and why it's important. There are various statistical models to use when testing a hypothesis including ANOVA, multiple regression, and bivariate regression. Statistical significance is also important when testing the hypothesis.