選修

This course is the second part of a sequence of statistical data analysis courses: “Statistical Data Analytic Methods (I)”, “Statistical Data Analytic Methods (II)”, and “Advanced Statistical Data Analytic Methods”. Materials covered in the sequence consist of univariate statistical methods, and multivariate statistical methods. These courses are to help students, primarily from business and social sciences, learn the application of the basic and advanced data analytic techniques. We will emphasize the concepts of every given technique and its use, and will help students learn how to interpret the resulting output obtained from the widely used statistical package SPSS(Statistical Package for Social Sciences) or SAS(Statistical Analysis System).

At the end of this course, students will be able to learn the basics of statistical data analysis, and will be aware of the importance of statistical analysis for conducting a scientific research. A wrongly doing statistical analysis which results in a misleading conclusion is worse than no analysis. Students will be introduced to the essentials of having an appropriate statistical analysis.

英文全程教學，無須中文課程大綱。

1. Tests of Population Means (Continue)
(1) Two-way ANOVA
(2) Three-way ANOVA
(3) Multi-way ANOVA
2. Studying the Relationships among Two or More Quantitative Variables
(1) Correlation Analysis
(2) Regression Analysis
3. Tests of Population Proportions
(1) Tests for Two Population Proportions：Independent Samples
(Binomial Test，Z-test (Normal Approximation)，Chi-Square Tests for Independence：Contingency Table Analysis)
(2) Tests for Two Population Proportions：Paired Samples
(McNemar Test)
(3) Tests for More Than Two Population Proportions：Independent Samples
(Chi-Square Tests of Independence：Contingency Table Analysis)
(4) Tests for More Than Two Population Proportions：Dependent Samples
(Cochran’s Q Test)
4. Analysis of Data for Multiple Choices
5. Nonparametric Methods
(1) Spearman Rank Correlation Coefficient
(2) Chi-Square Test
(3) McNemar Test
(4) Cochran’s Q Test
6. Analysis of Covariance
7. Multivariate Data Analysis (I)
(1) Principal Components Analysis
(2) Exploratory Factor Analysis
(3) Reliability and Validity Analysis
(4) Canonical Correlation Analysis
(5) Cluster Analysis