MATHEMATICS C1 (STATISTICAL DATA ANALYSIS)

理工学部 - 理工学部共通科目(英語コース)

SCT6690E

コース情報

担当教員: 木村 晃敏

単位数: 2

年度: 2024

学期: 春学期

曜限: 水3

形式: 対面授業

レベル: 200

アクティブラーニング: なし

他学部履修: 不可

評価方法

出席状況

20%

リアクションペーパー

30%

レポート

50%

詳細情報

概要

When you make some experiments or surveys to complete academic papers, visualizing and summarizing data will help you a lot. Now it has been normal to use software to do such works. Through this course, you can learn the first step of statistical data analysis by making use of the freeware “R”, which is in line with the Faculty's curriculum policy: "acquire basic knowledge to solve scientific and technical problems in a wide perspective." “R” is now becoming the worldwide standard software for data analysis. You can download “R” and its option packages free of charge wherever Internet connection is available. Hence this course can be called “The introductory course of statistical data analysis with survival technique”

目標

Visualizing data is very simple by using software, and it helps us understand the outline of data intuitively. This first step is necessary before rigorous analyses are considered. Moreover, when some advanced techniques are applied, data visualization enables us to check whether or not the conditions to be satisfied are fulfilled. By making use of the freeware “R”, this course provides an opportunity to learn how to visualize data, understand the data through visualization, and use basic numerical summaries such as means, medians, standard deviations, and interquartile ranges. Obtaining such skills agree to the Faculty's diploma policy: "acquire basic knowledge to solve scientific and technical problems in a wide perspective."

授業外の学習

Students are required to review the exercises and review the lectures (190 minutes for each class). It is strongly recommended to acquire knowledge and technique in this course because they are bases of data visualization and handling.

所要時間: At least 190 minutes

スケジュール

  1. Guidance and introduction *The schedule is subject to change.
  2. “R” and “RStudio”
  3. Data Format
  4. Data Visualization
  5. Random Variables
  6. Limit Theorems
  7. Probability Distributions
  8. Descriptive Statistics
  9. Estimation 1
  10. Estimation 2
  11. Statistical Hypothesis Testing 1
  12. Statistical Hypothesis Testing 2
  13. Analysis of Variance
  14. Linear Regression Analysis

教科書

Distribute teaching materials for this course every week

    参考書

    書籍情報はありません。

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