predictor variable


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predictor variable

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Here it is found that out of eight predictor variables used in the analysis only belief in capital market and Surplus cash have the capacity to discriminate between those who are interested in the stock market and those who are not interested in investing in the stock markets.
Table (15) Path coefficients, t-statistics and the coefficient of determination (dependent variable: trust in the brand) Predictor Variable The coefficient Correlation of determination coefficient ([R.
05 indicating the coefficients of the predictor variables are significant.
For purposes of data coding and analysis, the set of demographic predictor variables in this study were operationalized as follows: age: age in years at the time of the study; gender: male, female; ethnicity: Caucasian, Non-Caucasian; marital status: married, not married; employment status: unemployed, employed; household income: less than $25,000, $25,000-$39,999, $40,000 -$59,999, $60,000-$79,000, $80,000-$99,000, over $100,000; health insurance coverage: yes, no; and educational attainment: some high school or high school graduate, some college or college graduate, post-graduate studies or professional training.
05 level of significance, cash plus cash equivalents was the first predictor variable selected for inclusion, with an F-to enter of 86.
A discriminatory power measure for assessing the importance of each predictor in identifying fraudulent claim files is defined by calculating the expected predictor variable score for the fraudulent and non-fraudulent claims.
Wilks Lambada output of discriminant analysis highlighted that the type of job as a predictor variable discriminated among the level of service quality of customers with p value < .
To examine research question 4 (Does the strength of expectations as a predictor variable vary according to the different categories of post-secondary enrollment?
Step 2 of the hierarchical regression analysis determined if each social cognitive predictor variable explains the unique variance in tobacco use and alcohol consumption behavior when controlling for the demographic variables.
If the Lowess fit is not linear, it may be possible to transform the data to linearize the relationship between the response and the predictor variable and improve the accuracy of the model.
Specific emphasis is given to the selection of the predictor variables (assessing model efficiency and accuracy) and cross-validation (assessing model stability).