今日的科學已逐漸數量化，常常需要對從實驗室或調查得來的大量數據或測度作分析。在統計及數量方法的運算中，矩陣代數可明白地、簡潔地表示龐大的數量資料。收集、整理及表達資料常借助矩陣代數作為工具，故矩陣代數已成為現今在科學研究及實務運算上不可或缺的數學工具。因此，本課程的目的即在學習矩陣代數的理論基礎及在統計上的應用。

Today's science has been quantified gradually, and it is often necessary to analyze the large amount of data or measurements obtained from laboratory or surveys. In the calculation of statistical and quantitative methods, the matrix generation can clearly and simply represent large quantities data. Collecting, organizing and expressing data often uses matrix generation numbers as tools, so matrix generation numbers have become an indispensable mathematical tool in scientific research and practical calculations today. Therefore, the purpose of this course is to learn the theoretical basis of matrix generation and its application in statistics.

今日的科學已逐漸數量化，常常需要對從實驗室或調查得來的大量數據或測度作分析。在統計及數量方法的運算中，矩陣代數可明白地、簡潔地表示龐大的數量資料。收集、整理及表達資料常借助矩陣代數作為工具，故矩陣代數已成為現今在科學研究及實務運算上不可或缺的數學工具。因此，本課程的目的即在學習矩陣代數的理論基礎及在統計上的應用。
修習本課程所需具備的數學基礎訓練，只要求具有高中程度的代數及簡單的微分訓練即可。課程內容包含：矩陣的基本運算及性質、行列式、特殊矩陣的介紹、反矩陣、秩、廣義逆(反)矩陣、分割矩陣、線性方程式、特徵值及特徵向量、及統計上的應用。

Today's science has been quantified gradually, and it is often necessary to analyze the large amount of data or measurements obtained from laboratory or surveys. In the calculation of statistical and quantitative methods, the matrix generation can clearly and simply represent large quantities data. Collecting, organizing and expressing data often uses matrix generation numbers as tools, so matrix generation numbers have become an indispensable mathematical tool in scientific research and practical calculations today. Therefore, the purpose of this course is to learn the theoretical basis of matrix generation and its application in statistics.
The mathematical basic training required for this course is only required to have a high school degree and a simple differential training. The course content includes: basic calculations of the matrix and its properties, determinants, introduction of special matrix, inverse matrix, rank, inverse matrix, segmentation matrix, linear equation, characteristic values ​​and characteristic vectors, and statistical applications.

Matrix Algebra Useful for Statistics, by Shayle R. Searle

Matrix Algebra Useful for Statistics, by Shayle R. Searle