南台課程大綱
學年度 106學年第一學期 系所 商管專業學院
課程名稱 統計方法 班級 碩商管國際一甲
授課教師 顏慧 點 閱 次 數 22
選修
必修
課程概述
This course is the first part of a sequence of statistical data analysis courses: “Basic Statistical Data Analytic Methods”, 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 concept of every given technique and its use, and will help students learn to interpret the resulting output obtained from the widely used statistical packages: SPSS(Statistical Package for Social Sciences), and AMOS(Analysis of moment structures).
The application of each statistical method in solving managerial problem will be demonstrated by examples. Exercises are then given for practices in class; students will be asked to interpret analysis results in group discussions.
課程目標
課程大綱
Course Objectives:
After finishing this course, students are expected to be able to
1.understand the classification of types of statistical methods and their usages in data analysis,
2.identify the appropriate statistical method when encountering a managerial problem, and learn to use it correctly,
3.recognize the importance of applying statistical methods correctly and rigorously,
4.transfer the data into information and to develop managerial decisions,
5.analyze managerial data in practice using statistical methods,
6.apply SPSS on statistical data analysis and to interpret its output properly,
7.Present data analysis results in a way that decision makers who don’t know statistics can also understand.
英文大綱
1. Introduction
(1) Types of Measurement Scales
(2) Types of Data
(3) Classification of Data Analytic Methods
2. Hypothesis Testing
3. Assumptions Checking
(1) Homogeneity
(2) Normality
4. Analysis of Variance (ANOVA)
5. Correlation Analysis
6. Regression Analysis
7. Chi-Square Tests of Independence
8. Analysis of Data for Multiple Choices
9. Exploratory Factor Analysis (EFA)
10. Reliability and Validity Analysis
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