| 課程大綱Course Description： |
| This course emphasizes both practice and theory. The theoretical part will explain the basic and advanced methods of statistical analysis and its commonly used algorithms in the field of business management, including discrete random variables (binomial distribution, hypergeometric distribution, and Boisson distribution). , Normal distribution (application of data standardization, normal distribution to find approximation of binomial distribution), sampling distribution (estimation and error, law of large numbers and central limit theorem, distribution of sample variance and chi-square distribution, t test, F test), Hypothesis test and confidence interval, test of two groups of samples, inference of proportion problem, chi-square test, analysis of variance (ANOVA, ANCOVA, MANOVA), linear regression (least squares method, logistic regression) complex correlation coefficient and complex regression Analysis (regression model evaluation and residual analysis, polynomial regression, hierarchical regression, mediation and interference mixed model), time series (weighted average method, exponential smoothing method, component decomposition method), no-matrix method verification, etc. Practical cases will use SPSS and Statistica statistical analysis software to operate on the computer, and the theoretical concepts will be manipulated through actual data, so that students can better understand the concepts and teach in a practical problem-oriented way. |
| English Outline： |
| This course emphasizes both practice and theory. The theoretical part will explain the basic and advanced methods of statistical analysis and its commonly used algorithms in the field of business management, including discrete random variables (binomial distribution, hypergeometric distribution, and Boisson distribution). , Normal distribution (application of data standardization, normal distribution to find approximation of binomial distribution), sampling distribution (estimation and error, law of large numbers and central limit theorem, distribution of sample variance and chi-square distribution, t test, F test), Hypothesis test and confidence interval, test of two groups of samples, inference of proportion problem, chi-square test, analysis of variance (ANOVA, ANCOVA, MANOVA), linear regression (least squares method, logistic regression) complex correlation coefficient and complex regression Analysis (regression model evaluation and residual analysis, polynomial regression, hierarchical regression, mediation and interference mixed model), time series (weighted average method, exponential smoothing method, component decomposition method), no-matrix method verification, etc. Practical cases will use SPSS and Statistica statistical analysis software to operate on the computer, and the theoretical concepts will be manipulated through actual data, so that students can better understand the concepts and teach in a practical problem-oriented way. |
| 本科目教學目標Course Objectives： |
| Through this course, students can understand the statistical methods and practical applications of business data analysis, and through the establishment of cross-industry data analysis processes and models, effective statistical methods can be evaluated. This course emphasizes both practice and theory. The academic part will explain the basis of statistical analysis. and advanced methods and their commonly used algorithms in various fields, including discrete random variables, normal distribution, sampling distribution, hypothesis testing and confidence intervals, testing of two groups of samples, inference of proportion problems, chi-square test, variance Mathematical analysis, simple linear regression, multiple correlation and multiple regression analysis. Practical cases will use SPSS and STATISTICA statistical analysis software to practice on the computer, and the theoretical concepts will be manipulated through actual data, so that students can better understand the concepts and teach in a practical problem-oriented way. |
