Predicting Presidential Elections with Equally Weighted Regressors
... Republican (21); DURATION, a weighted index of the number of consecutive terms the incumbents have controlled the White House; and WAR. Fair adjusts two of his three economic variables, INFLATION and GOODNEWS, assigning a value of 0 in three ‘‘war’’ years (1920, 1944, and 1948). Fair’s expectations ...
... Republican (21); DURATION, a weighted index of the number of consecutive terms the incumbents have controlled the White House; and WAR. Fair adjusts two of his three economic variables, INFLATION and GOODNEWS, assigning a value of 0 in three ‘‘war’’ years (1920, 1944, and 1948). Fair’s expectations ...
Simple Linear Regression
... • To determine which of (often) several factors explain variability in the key response of interest; • To summarise the relationship(s); • For predictive purposes, e.g. predicting y for given x’s, or identifying x’s that optimise y in some way; Note: Presence of an association between variables does ...
... • To determine which of (often) several factors explain variability in the key response of interest; • To summarise the relationship(s); • For predictive purposes, e.g. predicting y for given x’s, or identifying x’s that optimise y in some way; Note: Presence of an association between variables does ...
Component-based Structural Equation Modelling
... It is often mentioned that PLS is to covariance-based SEM as PCA is to factor analysis. But the situation has seriously changed when Roderick McDonald showed in his 1996 seminal paper that he could easily carry out a PCA with a covariance-based SEM software by using the ULS criterion and constrainin ...
... It is often mentioned that PLS is to covariance-based SEM as PCA is to factor analysis. But the situation has seriously changed when Roderick McDonald showed in his 1996 seminal paper that he could easily carry out a PCA with a covariance-based SEM software by using the ULS criterion and constrainin ...
Analysis of Dosage-Response Data in Agricultural Research
... Logistic option of the Probit procedure (d=Logistic option in the Model statement using SAS) which fits linear logistic regression models, "i + $’X, to binary or ordinal response data by the method of maximum likelihood (" represents intercept parameters, $ is the vector of slope parameters, and X i ...
... Logistic option of the Probit procedure (d=Logistic option in the Model statement using SAS) which fits linear logistic regression models, "i + $’X, to binary or ordinal response data by the method of maximum likelihood (" represents intercept parameters, $ is the vector of slope parameters, and X i ...
IBM SPSS Regression 22
... of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous. Logistic regression coefficients can be used to estimate odds ratios for each of the independent variables i ...
... of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous. Logistic regression coefficients can be used to estimate odds ratios for each of the independent variables i ...
photic depth - KSU Web Home
... Equations (8) and (10) are based on empiricism; and therefore, the best relationships should be adapted for use. An F test of the standard errors of estimates for the equations indicates that these parameters are equal. Thus, it is concluded that Equations (8) and (10) are equally valid at this leve ...
... Equations (8) and (10) are based on empiricism; and therefore, the best relationships should be adapted for use. An F test of the standard errors of estimates for the equations indicates that these parameters are equal. Thus, it is concluded that Equations (8) and (10) are equally valid at this leve ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.