5.1 Key concepts and definitions

Multiple Regression

• Multiple regression: In multiple regression analysis, we are studying the relationship between one dependent variable and several independent variables (called predictors). The regression equation takes the form
• Y =b₀+ b₁x₁ + b₂x₂ …+ b_p+ e,
• where Y is the dependent variable, the b's are the regression coefficients for the corresponding x (independent) terms, b₀ is a constant or intercept, and e is the error term reflected in the residuals. The parameters of the regression equation are estimated using the ordinary least squares method (OLS).

• Ordinary least squares: This method derives its name from the criterion used to draw the best-fit regression line: a line such that the sum of the squared deviations of the distances of all the points to the line is minimized.
• Intercept: The intercept, b₀, is where the regression plane intersects with the Y-axis. It is equal to the estimated Y value when all the independents have a value of 0.
• Regression coefficient: Regression coefficients bi are the slopes of the regression plane in the direction of x_i. Each regression coefficient represents the net effect the i^th variable has on the dependent variable, holding the remaining x's in the equation constant.
• Beta weights are the regression coefficients for standardized data. Beta is the average amount by which the dependent variable increases when the independent variable increases one standard deviation and other independent variables are held constant. The ratio of the beta weights is the ratio of the predictive importance of the independent variables.
• Standardized means that for each datum the mean is subtracted and the result divided by the standard deviation. The result is that all variables have a mean of 0 and a standard deviation of 1.

• Residuals are the difference between the observed values and those predicted by the regression equation
• Dummy variables: Regression assumes interval data, but dichotomies may be considered a special case of intervalness. Nominal and ordinal categories can be transformed into sets of dichotomies, called dummy variables. To prevent perfect multicollinearity, one category must be left out.
• Interpretation of b for dummy variables. For b coefficients for dummy variables, which have been binary coded (the usual 1=present, 0=not present), b is relative to the reference category (the category left out).
• Multiple R: The correlation coefficient between the observed and predicted values. It ranges in value from 0 to 1. A small value indicates that there is little or no linear relationship between the dependent variable and the independent variables.
• Multiple R ² is the percent of the variance in the dependent variable, explained by the independent variables. It is also called the coefficient of multiple determination. Mathematically, R² = [ 1 - (SSE/SST) ] , where

· SSE = error sum of squares = S (Yi - Est Yi) ² where Yi is the actual value of Y for the i^th case and Est Yi is the regression prediction for the i^th case.

· SST = total sum of squares =S (Yi - MeanY) ²

· Adjusted R-Square: When there are a large number of independent variables, it is possible that R² may become artificially large, simply because some independent variables' chance variations "explain" small parts of the variance of the dependent variable. It is therefore essential to adjust the value of R² as the number of independent variables increases. In the case of a few independent variables, R² and adjusted R² will be close. In the case of a large number of independent variables, adjusted R² may be noticeably lower.

· Multicollinearity is the intercorrelation of the independent variables. The values of r²'s near 1 violate the assumption of no perfect collinearity, while high r²'s increase the standard error of the regression coefficients and make assessment of the unique role of each independent variable difficult or impossible. While simple correlations tell something about multicollinearity, the preferred method of assessing multicollinearity is to compute the determinant of the correlation matrix. Determinants near zero indicate that some or all independent variables are highly correlated.

· Partial correlation is the correlation of two variables while controlling for a third or more other variables. For example r_12.34 is the correlation of variables 1 and 2, controlling for variables 3 and 4. Partial correlation r_12.34 equal to uncontrolled correlation r₁₂ Þ No effect of control variables Partial correlation near 0 Þ Original correlation is spurious.

• Stepwise Regression: Stepwise regression is a sequential process for fitting the least squares model, where at each step a single predictor variable is either added to or removed from the model in the next fit.

Multiple Classification Analysis

• Multiple classification analysis: Multiple Classification Analysis (MCA) is a technique for examining the interrelationship between several predictor variables and one dependent variable in the context of an additive model Independent variables may be measured on nominal or ordinal scales and the dependent variable may be interval scale or a dichotomy.
• Additive model: Such a model assumes that the dependent variable can be predicted from an additive combination of the independent (or predictor) variables. In other words, they assume that the average score on the dependent variable for a given set of individuals (objects or cases) is predictable by adding the effects of several predictors.

• Eta: Eta indicates the ability of a predictor, using the given categories, to explain variation in the dependent variable.

• Eta square: Eta² is the correlation ratio and indicates the proportion of the total sum of squares, explained by the predictor.

• MCA Beta: This is directly analogous to Eta statistic, but is based on the adjusted means rather than the raw means. Beta is a measure of the ability of a predictor to explain variation in the dependent variable, after adjusting for the effects of all other predictors. Note that this is not in terms of percentage of variance explained.
• Multiple correlation coefficient squared: This coefficient indicates the proportion of variance explained in this run of the program.

• Adjustment for degrees of freedom: This is the factor used to correct for capitalizing on chance in fitting the model in the particular sample being analyzed.

• Multiple correlation coefficient squared (Adjusted): This coefficient estimates the proportion of variance in the dependent variable, explained by the predictor variables.