Monday, July 22, 2019
Marketing Research Essay Example for Free
Marketing Research Essay 1. Explain the difference between testing for significant differences and testing for association. If a relationship present between two variables, it is important to know thw direction, the direction of a relationship can be either positive or negative. An understanding of the strength of association also is important. Researchers generally categorize the stenght os association as no relationship, weak relationship, moderate relationship, or strong relationship. If a consistent and systematic relationship is not present, then there is no relationship. 2. Explain the difference between association and causation. It depends if we have a linear relationship, which means the strength and nature of the relationship between them remains the same over the range of both variables, and can be best described using a straight line; or a curvilinear relationship, which means the strength and/or direction of the relationship changes over the range of both variables. 3. What is covariation? How does it differ from correlation? Covariation is the amount of change in one variable that is consistently related to the change in another variable of interest. 4. What are the differences between univariate and bivariate statistical techniques? Univariate focuses on one variable, and bivariate focuses on 2 5. What is regression analysis? When would you use it? Statistical technique that analyzes the linear relationship between two variables by estimating coefficients for an equation for a straight line. One variable is designated as dependent variable and the other is called an independent or predictor variable. 6. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? A situation in which several independent variables are highly correlated with each other. This characteristic can result in difficulty in estimating separate or independent regression coefficients for the correlated variables.
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