This research was conducted to determine the important influential variables upon the deaths from road traffic accidents and effect of each of those upon the studied phenomenon through applying logistic regression model. The maximum likelihood method was used to estimate parameters to determine the explanatory variables effect. Wald test was used to determine the significance of the explanatory variables. The data set used in this research consists of a sample of (212) observations and was obtained from the records of the directorate traffic-Garmian. The accident victims is response variable in this study and it is a dichotomous variable with two categories. The study led to a number of conclusions, among them; logistic regression models fit such data, three explanatory variables were found most significantly associated to accident victims response variable namely; high speed, car type, and location.
The aim of this study is to estimate the survival function for the data of lung cancer patients, using parametric methods (Weibull, Gumbel, exponential and log-logistic).
Comparisons between the proposed estimation method have been performed using statistical indicator Akaike information Criterion, Akaike information criterion corrected and Bayesian information Criterion, concluding that the survival function for the lung cancer by using Gumbel distribution model is the best. The expected values of the survival function of all estimation methods that are proposed in this study have been decreasing gradually with increasing failure times for lung cancer patients, which means that there is an opposite relationship failure times and survival function.
Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network. The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model and artificial neural networks and choose the best and most efficient model for patients with hypertension in Kalar through the comparison between neural networks and Box- Jenkins models on a data set for predict. Comparisons between the models has been performed using Criterion indicator Akaike information Criterion, mean square of error, root mean square of error, and mean absolute percentage error, concluding that the prediction for patients with hypertension by using artificial neural networks model is the best.
Analysis of variance (ANOVA) is one of the most widely used methods in statistics to analyze the behavior of one variable compared to another. The data were collected from a sample size of 65 adult males who were nonsmokers, light smokers, or heavy smokers. The aim of this study is to analyze the effects of cigarette smoking on high-density lipoprotein cholesterol (HDL-C) level and determine whether smoking causes a reduction in this level, by using the completely randomized design (CRD) and Kruskal- Wallis method. The results showed that the assumptions of the one- way ANOVA are not satisfied, while, after transforming original data by using log transformation, they are satisfied. From the results, a significantly decreased level of HDL-C in smokers as compared to non-smokers is indicated.
The present focuses on detecting and treating the outlier values in the Latin square design. The box plot method has been used to detect the outlier values, their effects have been studied either by keeping them in the data or by considering them missing values. From the practical work it is concluded that the outlier values have apparent and important effect on value of mean squares error, and the results of analysis of variance have been better after treating the outlier values.
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