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課程名稱 |
應用生物統計學
Applied Biostatistics |
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開課學期 |
112-2 |
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授課對象 |
生物資源暨農學院 植物病理與微生物學系 |
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授課教師 |
張皓巽 |
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課號 |
PPM5084 |
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課程識別碼 |
633 U1500 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期五6,7,8(13:20~16:20) |
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上課地點 |
學新820 |
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備註 |
本課程中文授課,使用英文教科書。教室：學新館820
總人數上限：8人 |
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課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
Statistics is once named as “The Grammar of Science” by Karl Pearson in 1891. This is not only a compliment, but more an emphasis to show the importance of statistics in knowing the probability and difference for research in all fields of science. In other words, any claim from observational difference to experimental significance relies on a solid and robust statistical analysis. As the field of statistics contains a broad spectrum from theoretical statistics to applied statistics, and from frequentist’s statistics to Bayesian’s statistics, this course “Applied Biostatistics” would focus on the classic and applied statistics, with an aim to equip students with the ability of using R language to perform statistical analyses, programing, and plotting. |
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課程目標 |
The goals of “Applied Biostatistics” includes the instructions of the concepts and principles of classic statistics, with expectation that students who completed this course to have the ability of using standard statistical terminologies in scientific discussion, and the ability of applying the skills of R programing and plotting in personal research. |
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課程要求 |
1. 本課程將學習以R程式進行統計分析。學生將以手算結果與R分析結果相互驗證。本課程將學習基礎R語言，歡迎沒有使用過R語言的同學。
2. 每位學生需自備筆電與科學計算機以利作業與考試進行。
1. This is a statistic course based on R programming. Students will need to hand-calculate and learn how to use R to match the results. No R background is needed.
2. Students need to prepare a personal scientific calculator/laptop for exams |
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預期每週課後學習時數 |
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Office Hours |
每週二 13:30~14:00 |
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指定閱讀 |
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參考書目 |
#For each midterm and final, you are allowed to bring a single A4 page cheat-sheet. You may write down any formula, note, and even draw tables and figures to assist you pass the exam. You are not allowed to write down any exact examples in the cheat-sheet. If you bring a cheat-sheet to the exam, you will need to turn in the cheat-sheet together with your exam papers. |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
Midterm I |
20% |
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2. |
Midterm II |
20% |
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3. |
Midterm III |
20% |
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4. |
Final |
13% |
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5. |
Homeworks |
27% |
9 HWs. Each accounts for 3% |
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週次 |
日期 |
單元主題 |
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第1週 |
2/23 |
Preface. Syllabus
Chapter 1. What is Statistics?
1. Statistics is the grammar of science
2. Milk tea and Ronald Fisher
Chapter 2. Descriptive Statistics
1. Parameters and statistics
2. Data visualization
3. The philosophy of statistics
4. Pearson’s and Spearman’s correlation |
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第2週 |
3/1 |
Chapter 3. Normal distribution
1. Normal distribution (Gaussian distribution)
2. Z-distribution (standardized normal distribution)
3. Bivariate normal distribution
Chapter 4. Inference Statistics, Z test and Z distribution
1. Inference statistics
2. Estimation of confidence interval
3. Z test
4. Type I error and Type II error
(#HW1) |
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第3週 |
3/8 |
Chapter 5. Student’s t-test and t distribution
1. Central limit theorem
2. Unbiased variance
3. Student’s t-test and t distribution
4. Degree of freedom
5. Unpaired t-test, Welch’s t-test and paired t-test
(#HW2) |
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第4週 |
3/15 |
Chapter 6. Chi-square test and F test for Variance Inference
1. One sample chi-square test
2. Two sample F test
3. Multi-sample F test
(#HW3) |
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第5週 |
3/22 |
Midterm I. (Chapter 1-5) |
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第6週 |
3/29 |
Chapter 7. Analysis of Variance (ANOVA)
1. One-way ANOVA
2. Multiple comparisons
3. Bonferroni correction and false discovery rate (FDR)
4. Fisher least significant difference (LSD)
5. Tukey’s honest significant test (TukeyHSD)
6. Dunnett’s test
7. Scheffe’s test
(#HW4) |
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第7週 |
4/5 |
No Class |
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第8週 |
4/12 |
Chapter 8. Linear Regression
1. Simple linear regression
2. Coefficient of determination (R2)
3. Multiple linear regression
4. Analysis of covariate (ANCOVA)
5. Polynomial regression
(#HW5) |
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第9週 |
4/19 |
Chapter 8. Linear Regression
1. Multicollinearity
2. Variance inflation factor (VIF)
3. Model selection – likelihood, Akaike information criterion (AIC), Bayesian information criterion (BIC)
Chapter 9. Assumption Diagnosis and Data transformation
1. Normality
2. Equal variance (homoscedasticity)
3. Data transformation
4. Leverage, outlier and influential points
(#HW6) |
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第10週 |
4/26 |
Midterm II. (Chapter 6-7) |
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第11週 |
5/3 |
Chapter 10. Experimental Design
1. Fisher’s basic principles of experimental design
2. The meaning of replicate and repeat
3. Complete randomized design (CRD)
4. Complete randomized block design (RCBD)
5. Latin square
(#HW7) |
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第12週 |
5/10 |
Chapter 11. Mixed Model and Variance Partition
1. Variance partition
2. Fixed effect
3. Random effect
(#HW8) |
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第13週 |
5/17 |
Midterm III (Chapter 8-10) |
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第14週 |
5/24 |
Chapter 12. Nonparametric Statistics
1. Contingency table – Pearson’s chi square test and Fisher’s exact test
2. Nonparametric t test – Wilcoxon rank sum test (Mann-Whitney U test)
3. Nonparametric paired t test – Wilcoxon signed rank test
4. Nonparametric one-way ANOVA– Kruskal-Wallis test
5. Nonparametric two-way ANOVA – Friedman test
(#HW9) |
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第15週 |
5/31 |
Closing Remarks |
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第16週 |
6/7 |
Final (All Chapters) |
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