Factor analysis is particularly suitable to extract few factors from the large number of related variables to a more manageable number, prior to using them in other analysis such as multiple regression or multivariate analysis of variance. It can be beneficial in developing of a questionnaire. Sometimes adding more statements in the questionnaire fail to give clear understanding of the variables. With the help of factor analysis, irrelevant questions can be removed from the final questionnaire. This study proposed a factor analysis to identify the factors underlying the variables of a questionnaire to measure tourist satisfaction. In this study, Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of Sphericity are used to assess the factorability of the data. Determinant score is calculated to examine the multicollinearity among the variables. To determine the number of factors to be extracted, Kaiser's Criterion and Scree test are examined. Varimax orthogonal factor rotation method is applied to minimize the number of variables that have high loadings on each factor. The internal consistency is confirmed by calculating Cronbach's alpha and composite reliability to test the instrument accuracy. The convergent validity is established when average variance extracted is greater than or equal to 0.5. The results have revealed that the factor analysis not only allows detecting irrelevant items but will also allow extracting the valuable factors from the data set of a questionnaire survey. The application of factor analysis for questionnaire evaluation provides very valuable inputs to the decision makers to focus on few important factors rather than a large number of parameters.

Multicollinearity occurs when the multiple linear regression analysis includes several variables that are significantly correlated not only with the dependent variable but also to each other. Multicollinearity makes some of the significant variables under study to be statistically insignificant. This paper discusses on the three primary techniques for detecting the multicollinearity using the questionnaire survey data on customer satisfaction. The first two techniques are the correlation coefficients and the variance inflation factor, while the third method is eigenvalue method. It is observed that the product attractiveness is more rational cause for the customer satisfaction than other predictors. Furthermore, advanced regression procedures such as principal components regression, weighted regression, and ridge regression method can be used to determine the presence of multicollinearity.

In this paper, the frequency of an earthquake occurrence and magnitude relationship has been modeled with generalized linear models for the set of earthquake data of Nepal. A goodness of fit of a statistical model is applied for generalized linear models and considering the model selection information criterion, Akaike information criterion and Bayesian information criterion, generalized Poisson regression model has been selected as a suitable model for the study. The objective of this study is to determine the parameters (a and b values), estimate the probability of an earthquake occurrence and its return period using a Poisson regression model and compared with the Gutenberg-Richter model. The study suggests that the probabilities of earthquake occurrences and return periods estimated by both the models are relatively close to each other. The return periods from the generalized Poisson regression model are comparatively smaller than the Gutenberg-Richter model.

This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.

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