R语言 Sleuth2包 case1302()函数中文帮助文档(中英文对照)

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case1302(Sleuth2)
case1302()所属R语言包：Sleuth2

                                        Pygmalion Effect
                                         皮格马利翁效应

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

One company of soldiers in each of 10 platoons was assigned to a Pygmalion treatment group, with remaining companies in the platoon assigned to a control group. Leaders of the Pygmalion platoons were told their soldiers had done particularly well on a battery of tests which were, in fact, non-existent.  In this randomised block experiment, platoons are experimental units, companies are blocks, and average Practical Specialty test score for soldiers in a platoon is the response. The researchers wished to see if the platoon response was affected by the artificially-induced expectations of the platoon leader.
公司10排的士兵在被分配到一个皮格马利翁处理组，与其余的公司中排分配到对照组。皮格马利翁排的领导告诉他们的士兵进行了特别的电池的测试，其实是不存在的。在此随机区组试验中，排的试点单位，公司块，实用的专业测试成绩平均在一个排的士兵的回应。研究人员希望看到如果排响应排长由人工诱导的期望的影响。


用法----------Usage----------


case1302



格式----------Format----------

A data frame with 29 observations on the following 3 variables.   
29以下3个变量的观察与数据框。

Companya factor indicating company identification, with levels "C1", "C2", ..., "C10"
Company的一个因素，指出企业标识，同级别"C1"，"C2"，...，"C10"

Treata factor indicating treatment with two levels, "Pygmalion" and "Control"
Treat的一个因素两个层面处理，"Pygmalion"和"Control"

Scoreaverage score on practical specialty test of all soldiers in the platoon   
Score实用的专业测试所有士兵的平均得分排


源----------Source----------

Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed), Duxbury.
拉姆齐，F.L.和Schafer，D.W. （2002年）。的统计的猎犬：A课程方法的数据分析（第二版），达克斯伯里。


参考文献----------References----------

Eden, D. (1990). Pygmalion Without Interpersonal Contrast Effects: Whole Groups Gain from Raising Manager Expectations, Journal of Applied Psychology 75(4): 395–398.

实例----------Examples----------


str(case1302)

# two-way model with interactions[双向交互模型]
fitfull <- aov(Score ~ Company*Treat, case1302)
# No problems are indicated by residual plot[没有问题表示残差图]
plot(fitfull)
# Interaction terms are not statistically significant[互动方面无统计学意义]
anova(fitfull)  
# Additive model, with "treatment contrast" for treatment:[加性模型，与“治疗”治疗对比：]
fitadditive <- aov(Score ~ Company + Treat, case1302)
# Interpret treatment effect as coefficient of Treat[解释治疗效果系数TREAT]
anova(fitadditive)  

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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