This study examined the differences in knowledge of mathematical modeling between a group of elementary preservice teachers and a group of elementary inservice teachers. Mathematical modeling has recently come to the forefront of elementary mathematics classrooms because of the call to add mathematical modeling tasks in mathematics classes through…

Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics--a high-enrollment, high-impact, and core course that many engineering…

Ridge regression is an approach to the problem of large standard errors of regression estimates of intercorrelated regressors. The effect of ridge regression on the estimated squared multiple correlation coefficient is discussed and illustrated. (JKS)

The proposed method has the advantage of being a rational procedure which reduces the larger set of variables'' down to a desired subset of predictor variables. The selected subset, then, can be coded for a full regression run if it contains multiple level category variables among those selected. (Author)

Following Conger's revised definition of suppressor variables, the universe relationships among two predictors and a criterion is analyzed. A simple mapping of relationships, based on the correlation between two predictors and the ratio of their validities, is provided. The relation between suppressor and part correlation is also discussed.…

The Automatic Interaction Detector (AID) is discussed as to its usefulness in multiple regression analysis. The algorithm of AID-4 is a reversal of the model building process; it starts with the ultimate restricted model, namely, the whole group as a unit. By a unique splitting process maximizing the between sum of squares for the categories of…

The use of interaction and non-linear terms in multiple regression poses problems for determining parsimonious models. Several computer programs for using these terms are discussed. (JKS)

Common applications of the part correlation coefficient are in causal regression models and estimation of suppressor variable effects. However, there is no statistical test of the significance of the difference between a zero-order correlation and a part correlation, nor between a pair of part correlations. Hotelling's t is used for contrasting:…

A definition of a suppressor variable is presented which is based on the relation of the semipartial correlation to the zero order correlation. Advantages of the definition are given. (Author/JKS)

The relationship between quantitative predictor variables and the probability of occurrence of one or more levels of a qualitative criterion variable can be analyzed by quantal response techniques. This paper presents and discusses two quantal response models, comparing them to multiple linear regression and discriminant analysis. (Author/JKS)

Major objective in this study was the structuring of a predictive model that would assess combinations of variables that most effectively and parsimoniously measure and forecast college success. (Author)

The implications of the report are that a knowledge of quantitative areas is becoming increasingly more important in the logistics career fields. Therefore the study has emphasized predicting academic performance in quantitative courses in graduate logistics. It was expected that a useful model of this type, in conjunction with the other models,…

The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for…

The uses and restrictions of commonality analysis are described. Commonality analysis has been increasingly used as a method to examine the relative importance of independent variables, through the partitioning of variance among the variables of the regression equation into unique and common components. The effects of all other independent…

Purpose of this investigation was to determine whether the inclusion of quadratic and/or interaction terms in a regression model would improve the prediction of student nurses' grade point averages. (Authors)