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Need help? Logistic regression Fred C. Donate this book to the Internet Archive library. If you own this book, you can mail it to our address below. Not in Library. All the questionnaires were self-administered. For this study, usable data from students were analyzed. The coding change was made to reflect the predicted target category, current frequent cigarette use. As suggested by Menard , preliminary analysis of the data was performed to check the assumptions of logistic regression with respect to the selected predictors of the study.
Multicollinearity among predictors in logistic regression creates problems for the validity of the model for the investigation. In particular, it affects the validity of the statistical tests of the regression coefficients by inflating their standard errors. Garson, The results of the analysis showed that the data did not violate the multicollinearity assumption. The tolerance value of each independent variable was greater than. Pallant, Lack of multicollinearity among the independent variables was also supported by the obtained variance inflation factor VIF values.
They were all well below the cut-off value of Field, After the preliminary analysis of the data, the binary logistic regression procedure in SPSS was used to perform the analysis to determine whether the likelihood of CFCU could be predicted from the independent variables.
Data from high school students were included in this analysis. The results of the data analysis showed that the proportion of students who have tried smoking increased with age. In contrast to the relatively high prevalence For example, the prevalence of ever smoked cigarettes daily was higher among White Wald statistics indicate that all the variables significantly predict current frequent cigarette use. The strongest predictor of CFCU was race.
In particular, white. The odds ratio for white was 5. Other predictors that made significant contribution to the model CFCU were frequency of cocaine use, physical inactive behavior, feeling sad or hopeless and initial cigarette smoking age. The older the youth before he smoked a whole cigarette for the first time, the more likely he would report that he is a current frequent cigarette user.
The predictor ICSA recorded an odds ratio of 1. Thus, the odds of smoking frequently compared to not smoking cigarettes frequently increase by a factor of 1. Warner, Cocaine use and cigarette smoking behavior of young people were strongly related. Frequent cocaine use FCU predicts smoking behavior.
For a unit increase in the number of times the youth uses any form of cocaine, including powder, crack, or freebase, the odds for smoking cigarettes frequently i.
Feeling sad or hopeless recorded an odds ratio of 1. This indicates that the odds of smoking cigarettes frequently were about 1. As shown in Table 1, physically inactive behavior is implicated in student smoking. It recorded an odds ratio of about 1. In other words, students who were physically inactive have higher odds of smoking frequently more than 1.
The strongest predictors of youth smoking behavior are race, frequency of cocaine use and physically inactive behavior. Predictor B S. Carrier Chain of Command Download Pdf. Civil Engineering Formulas Download Pdf. Decidable Theories: Vol. International Book Series Download Pdf. Download Apache Geronimo 2. Download Champion of Garathorm Ebook. Recognition, and Graphics Volume 0 Ebook.
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