| Course title | |||||
| 農学実験計画法および統計解析演習Ⅰ [Exercise for Methods of Agricultural Experimental design and Statistical Analysis I] | |||||
| Course category | Requirement | Credit | 1 | ||
| Department | Year | 1～ | Semester | 1st | |
| Course type | 1st | Course code | 05ce0001c | ||
| Instructor(s) | |||||
| 奥田 武弘, 赤坂 宗光 [OKUDA Takehiro, AKASAKA Munemitsu] | |||||
| Facility affiliation | Graduate School of Agriculture | Office | Email address | ||
| Course description |
| Students obtain basic knowledge of statistical processing necessary for reading experiments related to agriculture and related fields. Students then improve ability for laboratory experiment, field survey, and reading papers. Especially, topics such as agricultural science, applied life science, ecology can be included. Planning and designing experiments and monitoring were also included in this class. Applications of specific statistical software will be demonstrated in this class. |
| Expected Learning |
|
Understand the basics of statistics as basic subjects necessary for conducting research in agriculture and related fields, understand the processing of data using interpretive statistical methods, and the interpretation method of results. The following three points are the reaching criteria. 1. Understand the basics of statistics 2. Methods that can statistically process phenomena related to agriculture and related fields 3. Evaluation of judgment and validity of contents obtained by analysis result Corresponding criteria in the Diploma policy: See the curriculum maps. https://www.tuat.ac.jp/campuslife_career/campuslife/policy/ |
| Course schedule |
|
Lesson 1: Guidance of course and understanding basic knowledge about statistics The outline of this course. To conduct appropriate statistical analyses, learn about basic statistics, statistical tests, test statistics, statistical models, probability distributions, etc. Lesson 2: “Statistics Done Wrong” Larn about inappropriate use of statistics. Lesson 3-4 Study planning, data handling, and generating figures Learn about: 1) Processes of planning observations and experiments to accomplish the study, and processed of considering appropriate statistical analyses to analyze obtained data sets; 2) Concept of “tidy data” to prepare data set for conducting statistical analysis; 3) How to generate virtual data by using statistical software R; 4) Processes of considering whether the planed analysis will be able to conduct appropriately based on characteristics of data sets; 5) Processes of revising methods of observations, experiments, and statistical analyses, through appropriate handling of obtained data sets and creating graphs providing overhead views of data sets; and 6) How to edit data and draw graphics by using statistical software R. Lesson 5-6 Statistical analysis with R -statistical modelling- Learn about: 1) Statistical tests and statistical modelling with R, and various types of statistics as results of statistical analyses; and 2) Processes of discussing whether obtained results will be appropriate to accomplish objects of the study, and advanced statistical analyses which would be candidate for revised analytical methods. This course focuses generalized linear model (GLM). Lesson 7 Statistical analysis with R -multivariate analyses- Learn about series of multivariate analyses. Lesson 8 How to indicate the analytical methods and results Learn about: 1) How to describe the analytical methods in scientific papers; 2) How to create appropriate graphs to show results of analyses by using R; and 3) How to present appropriate graphs and tables in scientific papers. The handout (e.g., PPT slides) will be distributed before each course work. It is desirable to prepare the summary of lessons. In each coursework, I will give some assignment to check learning contexts (deadline: two weeks after each lecture). After the ends of all coursework, I will request a report written in the form of a scientific paper (deadline: 20th July). |
| Prerequisites |
|
Related to subjects of the thesis, it is a required compulsory subject which is divided into special research A group. Subjects of special study group A open for April enrollment students, enrollees for October should take subjects of special study group B. Understanding mathematics provided at Japanese high school and basic knowledge about statistical software R. In completing this course, students will spend the standard amount of time as specified by the University. |
| Required Text(s) and Materials |
| Handout will be arbitrarily provided before each coursework. |
| References |
|
Paul Murrell (2011) R Graphics, Second Edition. Chapman & Hall. Michael J. Crawley (2013) The R Book, Second Edition. Wiley. |
| Assessment/Grading |
|
Assignment of each lesson: 60%, report: 40% 【2019】S:21%, A:42%, B:16.5%, C:4%, D:0%, E:16.5% 【2020】S:19%, A:44%, B:6%, C:12.5%, D:12.5%, E:6% 【2021】S:28%, A:48%, B:7%, C:3%, D:14%, E:0% |
| Message from instructor(s) |
|
One of the goals of this class is getting skills for conducting Generalized Linear Model (GLM) by using statistical software R. The coursework is going to held in Japanese. For non-native Japanese speaker, I will flexibly deliver the lecture, assignment of each lesson, and final report. Please, do not hesitate to ask me about language problems and contents of each lesson. |
| Course keywords |
| Statistical analysis, Data interpretation, Data handling, Statistical literacy, data literacy, Tidy data, programing, Statistical software R, statistical modelling |
| Office hours |
| Before and after of each “in-person” coursework. Open for any questions by e-mail. |
| Remarks 1 |
|
The coursework will be held on 4/8 (L1-2), 4/22 (L3-4), 5/13 (L5-6), and 6/10 (L7-8). |
| Remarks 2 |
| Related URL |
| Lecture Language |
| Japanese |
| Language Subject |
| Last update |
| 3/30/2022 4:46:16 PM |