The objective of this course is to provide students with knowledge in
basic probability theory, statistical inferences. This course will cover the following topics:
1. Basic probability theory. 2. Introduction of discrete random variables,
including binomial , hypergeometric and Poisson random variables.
3. Introduction of histogram, basic calculus, probability density function.
4. Introduction of continuous random variables, including uniform,
exponential, normal, Student, F and Chi-squared random variables.
4. Basic statistical inferences, including definitions of expectation, variance,
covariance, marginal distribution and joint distribution.
5. Estimation, including point estimation and interval estimation.
6. Central limit theory and its application.
7. Testing hypothesis (one-population).The objective of this course is to provide students with knowledge in
basic probability theory, statistical inferences. This course will cover the following topics:
1. Basic probability theory. 2. Introduction of discrete random variables,
including binomial, hypergeometric and Poisson random variables.
3. Introduction of histogram, basic calculation, probability density function.
4. Introduction of continuous random variables, including uniform,
exponential, normal, Student, F and Chi-squared random variables.
4. Basic statistical inferences, including definitions of expectation, variance,
covariance, marginal distribution and joint distribution.
5. Estimation, including point estimation and interval estimation.
6. Central limit theory and its application.
7. Testing hypothesis (one-population).
今天，對統計學的理解、研究和應用，已經擴展到自然科學、社會科學、工程技術、管理、經濟、藝術和文學的各個領域。一般人利用統計訊息在日常生活中作出各種抉擇，制定將來的計畫。為了能對所有有效的訊息正確的理解和應用，人們有必要掌握一定的統計知識。
統計方法是現今計量科學研究中最常用的方法之一，但是，隨著科技的進步、套裝軟體的發達，同學可以很容易的處理一些基本資料的分析及推論，而方法背後的理論基礎及涵意卻是一知半解，甚至錯誤的離譜。因此，本課程的主要目的是讓同學能獲得正確的統計基本原理、概念及素養。盡量教授一些看起來簡單、合理、合乎邏輯的概念禪述，避免艱深的內容和太多的數學推導，以免造成學習障礙與疏離，進而使同學失去學習興趣。
本課程基本上包含：
1. 敘述統計：關於數據資料的描述及整理。
2. 機率論：統計推論的基礎。
3. 統計推論：分析數據資料的方法。
Today, the understanding, research and application of statistics have expanded to various fields of natural sciences, social sciences, engineering technology, management, economics, art and literature. Ordinary people use statistical information to make various decisions in their daily lives and to make plans for the future. In order to correctly understand and apply all valid information, it is necessary for people to master certain statistical knowledge.
Statistical methods are one of the most commonly used methods in today's quantitative scientific research. However, with the advancement of technology and the development of packaged software, students can easily handle the analysis and inference of some basic data, and the theoretical basis and connotation behind the method But the meaning is half-understood, and even wrong. Therefore, the main purpose of this course is to enable students to acquire correct basic principles, concepts and literacy of statistics. Try to teach concepts that seem simple, reasonable, and logical, and avoid difficult content and too much mathematical derivation, so as not to cause learning obstacles and alienation, and thus make students lose interest in learning.
This course basically covers:
1. Narrative statistics: description and organization of data.
2. Probability theory: the basis of statistical inference.
3. Statistical inference: method of analyzing data.
1. Statistics for Business and Economics by
Anderson, Sweeney, Williams, Camm and Cochran
Metric Version. 14th Edition
1. statistics for business and economics by
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| midtermmidterm midterm |
50 | |
| finalfinal final |
50 |
