為使修習者熟悉數據分析方法了解基礎統計推論之理論及技術再加上進階統計應用方法如迴歸分析與變異數分析之訓練使具備統計分析之概念與技術
The purpose of the course is to teach the students data analysis methods, techniques as well as theories, for applied real world problems.

To make practitioners familiar with data analysis methods, understand the theories and techniques of basic statistical recommendations, plus advanced statistical application methods such as reproductive analysis and variable number analysis training to provide the concepts and techniques of statistical analysis
The purpose of the course is to teach the students data analysis methods, techniques as well as theories, for applied real world problems.

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 disciplines has expanded to various fields of natural sciences, social sciences, engineering technology, management, economy, art and literature. Most people use stereotype information to make various decisions in their daily lives and formulate plans to come. In order to correctly understand and apply all effective messages, 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 software packages, students can easily handle the analysis and recommendation of some basic data, and the theoretical basis and implications behind the method are a slight understanding or even a mistake. Therefore, the main purpose of this course is to enable students to obtain correct statistical basic principles, concepts and cultivation. As much as possible, teach some simple, reasonable and logical concept descriptions to avoid deep content and too much mathematical guidance, so as not to cause learning obstacles and disconnection, and thus make students lose their learning interest.

This course basically includes:

1. Statistics: Description and sorting of data.

2. Opportunity theory: the basis of statistical recommendation.

3. Statistical recommendation: Methods for analyzing data.

Anderson, Sweeney, Williams, Camm, Cochran, Fry, and Ohlmann, 2020, Statistics for Business and Economics, 14e, Metric Version, Cengage Learning

Anderson, Sweeney, Williams, Camm, Cochran, Fry, and Ohlmann, 2020, Statistics for Business and Economics, 14e, Metric Version, Cengage Learning