# Category Archive for: Correlation and Regression

Regression with dummy variables provides a framework for understanding analysis of variance and covariance. Although multiple regression with dummy variables provides a general procedure for the analysis of variance and covariance. we show only the equivalence of regression with dummy variables to one-way analysis of variance. In regression with dummy variables. the predicted Y for each category is the…

The Situation The West Michigan Whitecaps amin or league baseball team in Grand Rapids, wondered what they should do to develop fan loyalty. How could they best keep it, make it grow, and take advantage of it? General Manager Scott Lane got Message Factors  a Memphis, Tennessee-based research firm, to help them determine how to effectively maintain fan…

A major source of revenue for any professional sports team is through ticket sales, especially sales to season ticket subscribers. A study performed a regression analysis to determine what factors caused ticket prices to vary among teams in the same league within a given year. The regression equation was The research gathered data covering a span of…

The steps involved in conducting multiple regression analysis are similar to those for bivariate regression analysis. The discussion focuses on partial regression coefficients, strength of association, significance testing, and examination of residuals Partial Regression Coefficients To understand the meaning of a partial regression coefficient, let us consider a case in which there are two independent variables, so that:…

Standardization is the process by which the raw data are transformed into new variables that have a mean of 0 and a variance of I. When the data are standardized. the intercept assumes a value of. The term beta coefficient of beta weight is used to denote the standardized regression coefficient. In this case. the slope obtained by…