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[统计--核心课程] 关于experimental design的困惑

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helenaqa 发表于 2016-5-24 06:50:56 | 显示全部楼层 |阅读模式

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各位统计大神们,请问我们为什么会有各种各样的experimental design呢? 我在学习的过程中一直在问,为什么不单单用regression直接上indicator variable解出来就好了么?

为什么会有这个design,那个design,这个block,那个factor,感觉linear model只是把内容复杂化了,还不如regression来的直接呢?

望指教!
demonhunter 发表于 2016-5-24 14:38:51 | 显示全部楼层
本帖最后由 demonhunter 于 2016-5-24 14:50 编辑

Sometimes people are only interested in the factors they want to study however the response is influenced by all factors so they need different design strategies to remove the influence of  these nuisance factors. 鏉ユ簮涓浜.涓夊垎鍦拌鍧.

Sometimes a simple linear regression is not able to fully explain the inner mechanism of your data(for example, to study repeated measurement you need mixed model) .
. from: 1point3acres.com/bbs
Performing an experiment and getting data is far more expensive and time consuming than you can expected, so you have to check the collection of designs carefully and adopt one wisely.. 鐗涗汉浜戦泦,涓浜╀笁鍒嗗湴
. visit 1point3acres.com for more.
____________________________ calalia mentioned Randomization. That means your cannot always have a simple CRD design(assigning treatments to the Experiment Units completely randomly), whose model equals to a linear regression with several indicator predictors. Sometimes you just cannot run you Experiment in this completely random way(like assigning treatment to the plants in some algriculture experiment) so you need a more complicated model(like the model for split-plot deisgn)
.1point3acres缃
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calalia 发表于 2016-5-24 13:17:21 | 显示全部楼层
为了Random
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 楼主| helenaqa 发表于 2016-5-25 10:54:36 | 显示全部楼层
demonhunter 发表于 2016-5-24 14:38
Sometimes people are only interested in the factors they want to study however the response is influ ...

Thank you demonhunter and thank you calalia!.鏈枃鍘熷垱鑷1point3acres璁哄潧

Thank you for your thoughtful and detailed discussion. I understand that the design of experiment is both an art and science. And once we have the design and the data, we can analyze the results using an appropriate linear model (CRD, BIBD, split-splot, etc), or with a linear regression model.

I am still puzzled whether there is a difference in the analysis stage, it appears to me the various linear model designs and linear regression do the same with parameters that differ only by a simple linear transformation.

Is that correct? Or is there more to it than that?

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demonhunter 发表于 2016-5-25 11:25:50 | 显示全部楼层
本帖最后由 demonhunter 于 2016-5-25 11:27 编辑
helenaqa 发表于 2016-5-25 10:54
Thank you demonhunter and thank you calalia!
. 鐣欏鐢宠璁哄潧-涓浜╀笁鍒嗗湴
Thank you for your thoughtful and detailed discuss ...

"it appears to me the various linear model designs and linear regression do the same with parameters that differ only by a simple linear transformation."

No, many design linear models estimate parameters in a different way. For example, in mixed model:

y = X\beta + Zu +\epsilon, if you want to estimate u, the Random effect you need to use either ML or REML approach, you have to use more complicated Optimization Algorithm than Ordinary Linear model does.
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