ABSTRACT
The purpose of this paper was to introduce the χ2 test for the data of four-fold tables collected from the cohort design， the hypothesis test and interval estimation of relative risk， and the implementation of SAS and R software. In view of the characteristics of cohort design， the concept of relative risk， hypothesis testing and interval estimation were emphasized. It was given that the interpretation of the results calculated by SAS and R software， as well as the statistical and professional conclusions.
ABSTRACT
The purpose of this article was to introduce the χ2 test for the data of four-fold tables collected from the cross-sectional design and the realization of SAS and R software. Specifically， three methods were introduced， namely " Pearson΄s χ2 test"， "corrected Pearson΄s χ2 test" and "likelihood ratio χ2 test". The contents involved the test hypotheses， calculation principles and formulas， prerequisites， implementation of SAS and R software， interpretation of results and statement of conclusions of the aforementioned three statistical analysis methods.
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The purpose of this paper was to introduce the McNemar΄s χ2 test and SAS and R software implementation of four-fold table data collected from the matched pairs design. Firstly， it was proposed that there were three situations for the data of four-fold table of the paired design， namely ①the data of paired design four-fold table with the special "gold standard" was worthy of statistical analysis； ②the data of four-fold table of the paired design without the special "gold standard" was not worthy of statistical analysis； ③the data of four-fold table collected from the matched pairs design with implicit "gold standard" was worthy of statistical analysis. Secondly， taking the "problems and data" in the first case as the object of statistical analysis， SAS and R software were used to analyze the differences， the calculation results were given and explained， and the statistical and professional conclusions were also made.
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The purpose of this article was to introduce the χ2 distribution and related contents， including χ2 distribution and non-central χ2 distribution. It focused on showing the definition of two χ2 distributions， the graph and the main properties of the probability density function. Among them， the two most important properties were： first， the limiting distribution of the χ2 distribution was the normal distribution； second， n-1s2σ2followed the χ2 distribution with n-1 degrees of freedom.In addition， it also explained the relationship between the χ2 distribution and the normal distribution， the relationship between χ2 test statistic and Z test statistic. Finally， it illustrated the computational approaches of the χ2 distribution based on the two SAS functions in SAS software.
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The purpose of this paper was to introduce the CMH χ2 test and SAS software implementation of the three kinds of R×C contingency table data. The first type was called “two-way unordered R×C contingency table data”. The CMH χ2 test corresponding to this type of data was essentially the Pearson’s χ2 test. The second type was called “R×C contingency table data with an ordinal outcome variable”. The CMH χ2 test corresponding to this kind of data was essentially a rank sum test. The third type was called “R×C contingency table data which was of two ordinal variables with different attributes”. The CMH χ2 test corresponding to the data was essentially Pearson’s correlation analysis or Spearman’s rank correlation analysis. When there were 1 or 2 “ordinal variables” in the R×C contingency table data， it was necessary to “assign or score” the ordinal variables before performing statistical analysis. In the FREQ procedure of SAS/STAT， there were four scoring methods. With different scoring approach， both the expression form and the calculation results of CMH χ2 test statistics could change accordingly.
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The purpose of this paper was to introduce the linear trend χ2 tests for the data of the two kinds of one-way ordinal two-dimensional contingency tables and the implementation of SAS and R software. The two one-way ordered two-dimensional contingency tables referred to the “R×2 contingency table” with R values ordinal variable as the cause variable and the “2×C contingency table”with C values ordinal variable as the outcome variable. Their linear trend tests required the use of the Cochran-Armitage’s linear trend χ2 test and the Lee’s linear trend χ2 test， respectively.
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The purpose of this article was to introduce the χ2 test and SAS and R software implementation of three kinds of two-way unordered two-dimensional contingency table data except the four-fold table data. The three kinds of the tables referred to the two-way unordered “R×2”“2×C” and “R×C” （both R and C were greater than 2） contingency tables. Generally speaking， the purpose of analyzing them was to test whether the two attribute variables in the contingency table were independent. When certain prerequisites were met， the simple statistical analysis method that could be used was the χ2 test， when the specific prerequisites were not met， the Fisher’s exact test， which required the large amount of calculation， should be selected.
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The purpose of this paper was to introduce the χ2 test for the data of four-fold tables collected from the case-control design， the hypothesis test and interval estimation of odds ratio， and the implementation of SAS and R software. In view of the characteristics of case-control design， the concept of odds ratio， hypothesis testing and interval estimation were emphasized. It was given that the interpretation of the results calculated by SAS and R software， as well as the statistical conclusion and professional.
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The purpose of this article was to introduce the types of a multiway table data and their corresponding statistical analysis methods. Based on the data types of the result variables， common high-dimensional table data could be divided into the following three categories， namely， the high-dimensional table data with binary variables， multi-valued nominal variables and multi-valued ordinal variables as the result variables. There were two main categories of the statistical analysis methods for the multiway table data. The first category was the generalized difference analysis， in which the contents included "weighted χ2 test""CMH χ2 test" and "Meta analysis"， the second category was the regression analysis， in which the contents included "log linear regression model analysis""Logistic regression model analysis" "probit regression model analysis" and "discrete choice model analysis".
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The purpose of this article was to introduce the methods for the independence test of a special high-dimensional table （ie g×2×2 table） and its SAS implementation. There were three approaches， in the SAS software and the statistical textbooks， which could be used to perform the independence test for the data of a multiway table. The three kinds of methods were the generalized CMH χ2 test （for short， approach-1）， the weighted χ2 test with the weighted coefficients in its formula （for short， approach-2）， and the weighted χ2 test without the weighted coefficients in its formula （for short， approach-3）， respectively. This article revealed that the “approach-2” and “approach-3” were the same weighted χ2 test essentially， but with different manifestations. It also revealed that the weighted χ2 test statistic estimation was approximately equal to the CMH χ2 test statistic estimation in the “approach-1”. Based on an example and the SAS software， the article introduced the concrete approaches for the independence test of the g×2×2 table data， explained the output results， and made the statistical and professional conclusions.
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The purpose of this article was to introduce the Wald΄s test and the SAS implementation. The specific contents involved the following nine aspects， namely the general Wald΄s test，the robust Wald΄s test，the constrained Wald΄s χ2 test，the generalized Wald΄s test，the generalized Wald΄s log-linear test，the Wald΄s F test，the Wald΄s adjusted F test，the Wald΄s log-linear F test and the adjusted Wald΄s log-linear F test. The paper used two examples and the SAS software to achieve most of the Wald΄s tests mentioned above.
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Background: In Bangladesh, smoking is one of the leading preventable causes of death. Despite possessing knowledge about the consequences of smoking and the resultant non-communicable diseases, individuals have become considerably habituated to it. The study aims to identify the factors associated with smoking cigarettes and as well as to examine the existing situation of this issue among adult males in Bangladesh.Methods: Total 480 adult males were surveyed from Bangladesh through personal interview and online questionnaire, (from June 2018 to June 2019). To measure the effect of the explanatory variables on cigarettes smoking, authors perform χ2 test of independence as bivariate analysis. After performing bivariate analysis, a logistic regression analysis has been performed to assess the effect of the explanatory variables.Results: Findings of the study revealed that educational level, household economic status, media exposure, division have significant contribution for smoking cigarettes among the adult male in Bangladesh. A comparison of religious affiliation showed smoking cigarettes to be higher among non-muslim counterparts. Respondents living in rural area are found to have smoking cigarettes comparing with urban area.Conclusions: From the study it can be concluded that education and socio-economic status of male make a significant contribution in cigarettes smoking.