教學大綱 Syllabus

科目名稱：統計學（一）

Course Name: Statistics(I)
  • 學年學期：115-1 Fall Semester, 2026
  • 科目代碼：000359011 Course No.000359011

修別：必

Type of Credit: Required

3.0

學分數

Credit(s)

140

預收人數

Number of Students

課程資料Course Details
  • 開課單位：統計系 Course Department:Statistics
  • 授課老師：吳漢銘 Instructor: WU HAN-MING
  • 先修科目：無Prerequisite(N/A)
  • 上課時間：二234 Session: tue09-12

課程簡介Course Description

本課程主要是介紹統計學的理論、計算方法及在商業的應用。所使用的教科書為"Anderson et al., 2019, Statistics for Business & Economics (14th Edition),Cengage Learning Ltd. (ISBN: 0357114485)."。另搭配一本中文書為參考書目:「陳正倉, 林惠玲, 現代統計學(二版), 2020/07/17, 雙葉書廊出版社. (ISBN: 9789579096805)」。上學期的主題包含: 敘述統計、統計圖表、(離散及連續型)機率分佈、抽樣分佈、(點及區間)估計、假設檢定等等。課堂上以教師講述(Lecture)教學法為主。除了教授統計學的理論之外，還會使用R軟體進行統計量及機率分佈的計算、統計圖表的繪製及檢定報表的解釋。(註: 每週課程進度與作業要求，會依實際授課狀況做調整)

核心能力分析圖 Core Competence Analysis Chart

能力項目說明

    課程目標與學習成效Course Objectives & Learning Outcomes

    本課程的教學目標，是讓學生修完這門課之後，應該能夠具備下列能力: (1) 了解並能解釋統計學的專有名詞。(2) 理解統計學中機率分佈及抽樣分佈的主要理論。(3) 理解假設檢定之原理，並能應用於實際的問題上，提出統計推論。(4) 會以統計軟體進行敘述統計、計算機率、執行假設檢定及繪製統計圖表。(5) 會解釋統計軟體所產生的報表及圖表的意涵。

    課程屬性Course Characteristics

    級分說明 Score Description:

    1關聯性最弱，2關聯性稍弱，3關聯性中等，4關聯性稍強，5關聯性最強。(1-5非百分比，而是Rank)

    1 indicates weakest relevance, 2 indicates slightly weaker relevance, 3 indicates moderate relevance, 4 indicates slightly stronger relevance, and 5 indicates strongest relevance. (1-5 does not represent a certain percentage but indicates the rank).

    課程屬性項目Course Characteristics Items 級分(1~5)Ranking 說明Description
    課程教學內容涉及學生人文關懷素養與態度之培養。 The course contents include the development of students' abilities and appreciation for humanistic care.
    課程教學包含不同學門、領域之內容。 The courses include contents from different disciplines and fields.
    課程教學內容涉及社會參與、服務等。 The course contents include social engagement and services.
    係統整與深化學生歷年所學，並運用於實務操作與討論，藉以檢視和評估學生的整體學習成效。 The courses are designed to integrate and intensify students' academic results from previous years and apply them in practice and discussions for the purpose of reviewing and evaluating the overall effectiveness of students' learning.
    課程主軸明確探討永續性之統整概念及/或聯合國永續發展目標及/或「生態」與「社會/文化/經濟」系統之間的相互依賴性。關聯度級分請點選連結說明。 A unit of instruction that has a primary and explicit focus on the integrated concept of sustainability, and/or UN Sustainable Development Goals, and/or the interdependence of ecological and social/cultural/economic systems. Please refer to the link of relevance score.
    課程內容旨在教授媒體、網路相關專業資訊之真偽辨別與判讀能力。 The course would provide skills to evaluate the quality of information found on the media and the Internet.
    將 AI 知能運用融入課程，引導師生與 AI 共學、協作，多元培養學生 AI 素養。 Integrate the application of AI knowledge into the curriculum, lead teachers and students to learn and collaborate with AI, and cultivate students' AI literacy.

    每周課程進度與作業要求 Course Schedule & Requirements

    週次

    Week

    課程主題

    Topic

    課程內容與指定閱讀

    Content and Reading Assignment

    教學活動與作業

    Teaching Activities and Homework

    學生學習投入時間

    Student workload expectation

    課堂講授

    In-class Hours

    課程前後

    Outside-of-class Hours

    1

    課程簡介、資料與統計

    Ch1: Data and Statistics.

    Lecture

    3

    3

    2

    敘述統計

    Ch2, Ch3: Descriptive Statistics

    Lecture
    HW(1)

    3

    5

    3

    機率介紹(I)

    Ch4: Introduction to Probability

    Lecture

    3

    5

    4

    機率介紹(II)

    Ch4: Introduction to Probability

    Lecture
    HW(2)

    3

    5

    5

    離散型機率分佈(I)

    Ch5: Discrete Probability Distributions

    Lecture
    HW(3)
    小考(1)

    3

    3

    6

    離散型機率分佈(II)

    Ch5: Discrete Probability Distributions

    Lecture

    3

    5

    7

    連續型機率分佈(I)

    Ch6: Continuous Probability Distributions

    Lecture

    3

    5
               

    8

    期中考

    Ch1-Ch5

    期中考

    3

    3

    9

    連續型機率分佈(II)

    Ch6: Continuous Probability Distributions

    Discussion

    HW(4)

    3

    5

    10

    抽樣分佈(I)

    Ch7: Sampling and Sampling Distributions.

    Lecture

    3

    5

    11

    抽樣分佈(II)

    Ch7: Sampling and Sampling Distributions

    Lecture
    HW(5)

    3

    3

    12

    區間估計

    Ch8: Interval Estimation

    HW(6)

    3

    5

    13

    假設檢定(I)

    Ch9: Hypothesis Tests

    Lecture
    小考(2)

    3

    5

    14

    假設檢定(II)

    Ch9: Hypothesis Tests

     

    3

    3

    15

    統計推論: 兩母體平均數差的假設檢定

    Ch10: Inference about Means and Proportions with Two Populations

    Lecture
    HW(7)

    3

    5
               

    16

    期末考

    Ch6-Ch10

    期末考

    3

    3

    授課方式Teaching Approach

    80%

    講述 Lecture

    10%

    討論 Discussion

    0%

    小組活動 Group activity

    10%

    數位學習 E-learning

    0%

    其他： Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    小考 (30%), 期中考 (30%), 期末考 (30%), 出席點名 (10%),
    作業 (0%)，實習課 (0%)，R程式上機考試加分(額外20%)

    指定/參考書目Textbook & References

    Textbook: Anderson et al., 2019, Statistics for Business & Economics (14th Edition), Cengage Learning Ltd. (ISBN: 0357114485).

    Reference: 陳正倉, 林惠玲, 現代統計學(二版), 2020/07/17, 雙葉書廊出版社. (ISBN: 9789579096805)

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

    維護智慧財產權，務必使用正版書籍。 Respect Copyright.

    課程AI能力關聯Course–AI Competency Relevance

    級分說明 Score Description:

    0：無關聯，課程內容、活動及評量均未涉及。
    1：極低關聯，僅於課堂中簡單提及，未安排學習活動。
    2：低度關聯，少數單元或活動涉及，但非主要教學內容。
    3：中度關聯，明確納入部分教學活動、作業或評量。
    4：高度關聯，為重要學習內容，融入多數教學活動或評量。
    5：核心關聯，為課程核心學習目標，且有系統性教學與評量。
    0：No relevance: The course content, learning activities, and assessments do not address this competency.
    1：Very low relevance: The competency is only briefly mentioned in class, with no related learning activities.
    2：Low relevance: The competency is addressed in a small number of units or activities but is not a major component of the course.
    3：Moderate relevance: The competency is explicitly incorporated into some learning activities, assignments, or assessments.
    4：High relevance: The competency is an important learning component and is integrated into most learning activities or assessments.
    5：Core relevance: The competency is a core learning objective of the course and is addressed through systematic instruction and assessment.
    課程AI能力項目Course–AI Competency Items 級分(0~5)Ranking 說明Description
    本課程培養學生依專業或學習需求，理解、選用及運用AI工具，進行資訊整理、資料產出、提問與自主學習的程度。 This course develops students'ability to understand, select, and use AI tools according to their disciplinary or learning needs for information organization, data or content generation, inquiry, and self-directed learning.
    本課程培養學生遵循學術誠信、智慧財產權、個人資料及相關倫理規範，並辨識AI偏誤、錯誤資訊與資安風險的程度。 This course develops students’ ability to comply with academic integrity, intellectual property, personal data protection, and relevant ethical requirements, as well as to identify AI bias, misinformation, and cybersecurity risks.
    本課程培養學生運用AI分析問題、整合專業知識、優化流程，並透過人機協作提出、驗證及修正解決方案的程度。 This course develops students’ ability to use AI to analyze problems, integrate disciplinary knowledge, optimize processes, and propose, validate, and refine solutions through human–AI collaboration.
    本課程培養學生自主判斷、承擔責任、溝通協作、同理關懷、多元思考及創意實踐等以人為本能力的程度。 This course develops students’ human-centered competencies, including independent judgment, accountability, communication and collaboration, empathy and care, diverse perspectives, and creative practice.

    本課程可否使用生成式AI工具Course Policies on the Use of Generative AI Tools

    有條件開放使用:考試時不可使用 Conditional Permitted to Use

    課程相關連結Course Related Links

    - 課程網站: https://hmwu.idv.tw 
- 注意: 每周課教學內容及進度會依實際教學狀況隨時修正調整。(不便之處尚請見諒!)

    課程附件Course Attachments

    課程進行中，使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    是 Yes

    列印