課程介紹
試驗設計與統計分析是現代農業研究必備的工具。正確的使用試驗設計方法，是決定試驗能否達到預期效益的重要關鍵。本課程對於一般農業研究上常使用的試驗設計的方法作一完整介紹，並以案例結合試驗設計為範例，使學生能明瞭設計的概念，進而培養學生解決問題的能力。在進階課程中，展現對於先前所學知識的了解與整合。學生在面對農業研究問題時，能夠利用適合的試驗設計方法，輔以統計資料分析並推論結果。
教科書:
（1）Montgomery DC (2012) Design and Analysis of Experiments (8th Edition). John Wiley & Sons.
（2）Kuehl RO (2000) Design of experiments: statistical principles of research design and analysis (2nd edition). Brooks/Cole.
（2）Kuehl RO (2000) Design of experiments: statistical principles of research design and analysis (2nd edition). Brooks/Cole.
教學進度:
（1）Principles of research design: strategy and applications of experiment design; basic principles and guidelines for designing experiments.
（2）Basic statistical methods: sampling and sampling distributions; inferences about the differences in means and the variances of Normal distributions.
（3）Analysis of variance: ANOVA; fixed effects model; model adequacy checking; sample size; random effects model; regression approach.
（4）Experiments with blocking factors: randomized complete block design; Latin square design; Graeco-Latin square design.
（5）Factorial experiments: principles; 2-factor factorial design; the general factorial design; blocking in a factorial design.
（6）Two-level factorial design: the 2^{2} design; the 2^{3} design; the 2^{k} design; replication/unreplication in the 2^{k} design; optimal designs.
（7）Blocking & confounding for 2-level factorials: blocking a replicated 2^{k} design; confounding in the 2^{k} design; confounding the 2^{k} design in 2, 2^{2}, and 2^{p} blocks; partial confounding.
（8）Two-level fractional factorial designs: the one-half and one-quarter fractional of the 2^{k} design; the general 2^{k-p} fractional factorial design.
（9）Some other topics regarding factorial and fractional factorial designs: the 3^{k} factorial design; confounding in the 3^{k} design; fractional replication of the 3^{k} design.
（10）Regression modeling: linear regression models; hypothesis testing; estimation; model adequacy checking.
（11）Response surface methods and designs: introduction; analysis of a second-order model; experimental designs for fitting response surfaces; mixture experiments.
（12）Robust design: crossed array designs; combinded array designs and the response model approach; choice of designs.
（13）Random effects models: the 2-factor factorial with random factors; the 2-factor mixed model; sample size; expected mean squares; approximate F tests.
（14）Nested factors and hard-to-change factors: the 2-stage nested design; the general m-stage nested design; the split-spot design.
（2）Basic statistical methods: sampling and sampling distributions; inferences about the differences in means and the variances of Normal distributions.
（3）Analysis of variance: ANOVA; fixed effects model; model adequacy checking; sample size; random effects model; regression approach.
（4）Experiments with blocking factors: randomized complete block design; Latin square design; Graeco-Latin square design.
（5）Factorial experiments: principles; 2-factor factorial design; the general factorial design; blocking in a factorial design.
（6）Two-level factorial design: the 2^{2} design; the 2^{3} design; the 2^{k} design; replication/unreplication in the 2^{k} design; optimal designs.
（7）Blocking & confounding for 2-level factorials: blocking a replicated 2^{k} design; confounding in the 2^{k} design; confounding the 2^{k} design in 2, 2^{2}, and 2^{p} blocks; partial confounding.
（8）Two-level fractional factorial designs: the one-half and one-quarter fractional of the 2^{k} design; the general 2^{k-p} fractional factorial design.
（9）Some other topics regarding factorial and fractional factorial designs: the 3^{k} factorial design; confounding in the 3^{k} design; fractional replication of the 3^{k} design.
（10）Regression modeling: linear regression models; hypothesis testing; estimation; model adequacy checking.
（11）Response surface methods and designs: introduction; analysis of a second-order model; experimental designs for fitting response surfaces; mixture experiments.
（12）Robust design: crossed array designs; combinded array designs and the response model approach; choice of designs.
（13）Random effects models: the 2-factor factorial with random factors; the 2-factor mixed model; sample size; expected mean squares; approximate F tests.
（14）Nested factors and hard-to-change factors: the 2-stage nested design; the general m-stage nested design; the split-spot design.
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