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In this manuscript first a brief introduction to the Skew-t and Weighted exponential distributions is considered and some of their important properties will be studied. Then we will show that the Skew-t distribution is prefered to the Weighted exponential distribution in fitting by using the real data. Finally we will prove our claim by using the simulation method.

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Abstract One of the main goal in the mixture distributions is to determine the number of components. There are different methods for determination the number of components, for example, Greedy-EM algorithm which is based on adding a new component to the model until satisfied the best number of components. The second method is based on maximum entropy and finally the third method is based on nonparametric. In this manuscript it is considered the mixture distributions with Skew-t-Normal components.

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In this paper‎, ‎we ‎propose ‎Hurdle regression models for analysing count responses with extra zeros‎. A method of estimating maximum likelihood is used to estimate model parameters. The application of the proposed model is presented in insurance dataset‎. In this example‎, there are many numbers of claims equal to zero is considered that clarify the application of the model with a zero-inflated count response‎. ‎Different count regression models are introduced in this paper to model such data sets. Including Hurdle Poisson and Hurdle Negative Binomial regression models‎.

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Nonrespose is a source of error in the survey results and National statistical organizations are always looking for ways to
control and reduce it. Predicting nonrespons sampling units in the survey before conducting the survey is one of the solutions
that can help a lot in reducing and treating the survey nonresponse. Recent advances in technology and the facilitation of
complex calculations have made it possible to apply machine learning methods, such as regression and classification trees
or support vector machines, to many issues, including predicting the nonresponse of sampling units in statistics. . In this
article, while reviewing the above methods, we will predict the nonresponse sampling units in a establishment survey using
them and we will show that the combination of the above methods is more accurate in predicting the correct nonresponse
than any of the methods.

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The occurrence of lifetime data is a problem which is commonly encountered in various researches, including surveys, clinical trials and epidemioligical studies. Recently there has been extensive methodological resarech on analyzing lifetime data. Howerver, because usually little information from data is available to corretly estimate, the inferences might be sensitive to untestable assumptions which this calls for a sensitivity analysis to be performed.
In this paper, we describe how to evaluate the  effect  that  perturbations to the  Log-Beta Weibull Regression  Responses. Also, we review and extend the application and  interpretation of influence analysis methods using censored data analysis. A full likelihood-based approach that allows yielding maximum likelihood estimates of the model parameters is used. Some simulation studies are conducted to evalute the performance of the proposed indices in ddetecting sensitivity of key model parameters. We illustrate the methods expressed by analyzing the  cancer data.

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The purpose of this research is to investigate the statistical thinking of undergraduate student teachers in the field of mathematics education in the topic of diagrammatic literacy based on the framework of Wilde and Pfannkuch. For this purpose, a questionnaire including 9 diagram literacy questions (box diagram) was designed. The questions were classified based on the components of the Wilde and Pfannkuch framework. Questionnaire was completed by 50 student teachers of math education of Farhangian University of Education and Training of the director of Shahid Darjaei (boys and girls). The responses were leveled based on Watson's framework, which is a modification of Solow's model. The significance of the gender differences of student teachers was not confirmed statistically. The findings showed that most of the student teachers' answers in all statistical components are at the relational level, but the results showed the average performance of the student teachers in the components of statistical thinking in the topic of graph literacy.

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The importance of statistics and its education in today’s world, which is full of information and data, is not hidden from
anyone. Statistical thinking is the main core of correct understanding of statistical concepts, data analysis and interpretation
of phenomena. With the aim of achieving a comprehensive definition of statistical thinking and determining its elements,
the present research has studied the researches of the last thirty years. This descriptive research has been carried out in a
qualitative metacomposite
method to provide an insight into the totality of existing studies. Based on the entry criteria,
123 researches were identified between 1990 and 2022, and finally, after screening, 22 researches were selected for detailed
review and analysis. According to the present metacomposite findings, the elements of statistical thinking are: 1) Being dataoriented:
paying attention to data, identifying the need for data, collecting and considering data, different representations of
data, and methods of converting them to each other. 2) Variability: Considering permanent changes in all phenomena. 3)
Statistical inference: paying attention to the types of sampling, reasoning and inference using statistical models, including the
use of statistical charts and generalizing the results from the sample to the population. [4) Analysis of the statistical context:
combining the statistical problem with the context.

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Given the limited energy resources globally, energy optimization is crucial. A significant portion of this energy is consumed
by buildings. Therefore, the aim of this research is to explore the simultaneous factors affecting the heating and cooling of
buildings. In the current research, 768 different residential buildings simulated with Ecotect software have been investigated.
Joint regression model and exploratory data analysis methods were used to identify the influencing factors of the heating and
cooling of buildings. Based on variables such as relative compactness, overall height, surface area, and roof of the buildings,
a new variable called ”type” (building model) was introduced and shown to be one of the strongest factors affecting the
heating and cooling of buildings. This variable is related to the shape of the building. In the joint regression model, it is
assumed that the responses follow a multivariate normal distribution. Then this model is compared with separate regression
models (without assuming responses correlation) and using Akaike’s information criterion and deviance information criterion,
pointing to the superiority of the joint regression model. Additionally, the model parameters are estimated using the maximum
likelihood estimation method and the amount of Akaike model compared to the separate model is a decrease of 0.0072%,
which shows the superiority of the joint regression model. The deviance information criterion is equal to 0.001736%, and in
comparison with the chi distribution, the null hypothesis is rejected to test the superiority of the models, which is regressed
to the superiority of the joint model.