河津北京网站建设,网站开发一个支付功能要好多钱,wordpress产品的分类标签属性区别,河北邢台手机网站建设本文是实验设计与分析#xff08;第6版#xff0c;Montgomery著#xff0c;傅珏生译) 第5章析因设计引导5.7节思考题5.7 R语言解题。主要涉及方差分析#xff0c;正态假设检验#xff0c;残差分析#xff0c;交互作用图#xff0c;等值线图。 dataframe -data.frame…本文是实验设计与分析第6版Montgomery著傅珏生译) 第5章析因设计引导5.7节思考题5.7 R语言解题。主要涉及方差分析正态假设检验残差分析交互作用图等值线图。 dataframe -data.frame(
forcec(2.70,2.78,2.83,2.86,2.45,2.49,2.85,2.80,2.60,2.72,2.86,2.87,2.75,2.86,2.94,2.88),
feedgl(4,4,16),
speedgl(2,2,16))
summary (dataframe)
dataframe.aov2 - aov(force~feed*speed,datadataframe)
summary (dataframe.aov2) summary (dataframe.aov2) Df Sum Sq Mean Sq F value Pr(F)
feed 3 0.09250 0.03083 11.859 0.00258 **
speed 1 0.14822 0.14822 57.010 6.61e-05 ***
feed:speed 3 0.04187 0.01396 5.369 0.02557 *
Residuals 8 0.02080 0.00260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 with(dataframe,interaction.plot(feed,speed,force,typeb,pch19,fixedT,xlabfeed,ylabforce)) plot.design(force~feed*speed,datadataframe) fit -lm(force~feed*speed,datadataframe)
anova(fit) anova(fit)
Analysis of Variance Table Response: force Df Sum Sq Mean Sq F value Pr(F)
feed 3 0.092500 0.030833 11.8590 0.002582 **
speed 1 0.148225 0.148225 57.0096 6.605e-05 ***
feed:speed 3 0.041875 0.013958 5.3686 0.025567 *
Residuals 8 0.020800 0.002600
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 summary(fit) summary(fit) Call:
lm(formula force ~ feed * speed, data dataframe) Residuals: Min 1Q Median 3Q Max
-0.06000 -0.02625 0.00000 0.02625 0.06000 Coefficients: Estimate Std. Error t value Pr(|t|)
(Intercept) 2.740e00 3.606e-02 75.994 1e-12 ***
feed2 -2.700e-01 5.099e-02 -5.295 0.000733 ***
feed3 -8.000e-02 5.099e-02 -1.569 0.155303
feed4 6.500e-02 5.099e-02 1.275 0.238172
speed2 1.050e-01 5.099e-02 2.059 0.073449 .
feed2:speed2 2.500e-01 7.211e-02 3.467 0.008482 **
feed3:speed2 1.000e-01 7.211e-02 1.387 0.202934
feed4:speed2 -5.912e-16 7.211e-02 0.000 1.000000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05099 on 8 degrees of freedom
Multiple R-squared: 0.9314, Adjusted R-squared: 0.8715
F-statistic: 15.53 on 7 and 8 DF, p-value: 0.0004502 par(mfrowc(2,2))
plot(fit) par(mfrowc(2,2))
plot(as.numeric(dataframe$feed), fit$residuals, xlabfeed, ylabResiduals, typep, pch16)
plot(as.numeric(dataframe$speed), fit$residuals, xlabspeed, ylabResiduals, pch16) dataframe-data.frame(
forcec(2.70,2.78,2.83,2.86,2.45,2.49,2.85,2.80,2.60,2.72,2.86,2.87,2.75,2.86,2.94,2.88),
feedc(0.015,0.015,0.015,0.015,0.030,0.030,0.030,0.030,0.045,0.045,0.045,0.045,0.060,0.060,0.060,0.060),
speedc(125,125,200,200,125,125,200,200,125,125,200,200,125,125,200,200)) fit -lm(force~feed*speedfeed*I(speed^2)I(feed^2)*speedI(feed^2)I(speed^2),datadataframe) tmp.speed - seq(125,200,by.5)
tmp.feed - seq(0.015,0.060,by.005)
tmp - list(feedtmp.feed,speedtmp.speed)
new - expand.grid(tmp)
new$fit - c(predict(fit,new))
require(lattice)
contourplot (fit~feed*speed ,datanew, cuts8,regionT,col.regionsgray(7:16/16))
