From 95982d415418e4764edf9c15378e22db9bc50fe2 Mon Sep 17 00:00:00 2001 From: gehan7 Date: Tue, 17 Dec 2019 23:39:46 -0500 Subject: [PATCH] assignment 8 team 13 --- .DS_Store | Bin 0 -> 8196 bytes Link for the presentation | 2 +- R code for assignment 8.Rmd | 173 ++++++++++++++ Untitled.html | 440 ++++++++++++++++++++++++++++++++++++ Untitled.pdf | Bin 0 -> 207230 bytes assignment 8 video link.R | 4 + cwj1.csv | 311 +++++++++++++++++++++++++ cwj2.csv | 220 ++++++++++++++++++ eric - after.csv | 229 +++++++++++++++++++ eric before.csv | 275 ++++++++++++++++++++++ gh 1- before.csv | 251 ++++++++++++++++++++ gh 1- before.numbers | Bin 0 -> 252407 bytes gh2-after.csv | 234 +++++++++++++++++++ ych - after.csv | 220 ++++++++++++++++++ ych - before.csv | 245 ++++++++++++++++++++ 15 files changed, 2603 insertions(+), 1 deletion(-) create mode 100644 .DS_Store create mode 100644 R code for assignment 8.Rmd create mode 100644 Untitled.html create mode 100644 Untitled.pdf create mode 100644 assignment 8 video link.R create mode 100644 cwj1.csv create mode 100644 cwj2.csv create mode 100644 eric - after.csv create mode 100644 eric before.csv create mode 100644 gh 1- before.csv create mode 100755 gh 1- before.numbers create mode 100644 gh2-after.csv create mode 100644 ych - after.csv create mode 100644 ych - before.csv diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..e6de0fa632290639b79c936f1301722baaf011b3 GIT binary patch literal 8196 zcmeHMO>Yx15PgOof@qbXB2mP}2M%0_l)?dt%TlF6NN{PyIpm`uDVwB9(gL@=@JEn9 z;`ea}-q@zBH@mdz0aapL*>B^WHy%IF?#eCzvv)FV0XqO{tb&c3Y)XpUm%3K2g`RDq zAs={)8KyX7yIQl>4pl%EPz6*0RX`QE3JUO>Eu~iR-S>C3RRvUm|55>YKUh}5Bw*&z zy*k*qEdX(Y-LCMO`vAwW0h554M@OYkzk4u&YQh%7cslP}F()PgGmoAQ=XMM{QLBRbX8K*}LCD4@>?{qW$|5G;x4qEHKB>i*!Edr}I9oI4Wy|a$c7wW% zIcm#|eIY)+=4N~QFZ>zRo1zy%Q=1k literal 0 HcmV?d00001 diff --git a/Link for the presentation b/Link for the presentation index 0dd0856..3eb50c9 100644 --- a/Link for the presentation +++ b/Link for the presentation @@ -1 +1 @@ -https://drive.google.com/open?id=1VyMeCv4LojqMLf1YIj3LbU3E3wNar5WC +https://youtu.be/WgXDINp60vE \ No newline at end of file diff --git a/R code for assignment 8.Rmd b/R code for assignment 8.Rmd new file mode 100644 index 0000000..abac9c5 --- /dev/null +++ b/R code for assignment 8.Rmd @@ -0,0 +1,173 @@ +--- +title: "assignment 8" +output: + html_document: default + pdf_document: default +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r} +library(dplyr) +library(tidyverse) +library(tidyr) +library(GGally) +``` + +```{r} +library(ggplot2) +library(dplyr) +#The measured X and Y linear accelerations determine the position of the ball along the X and Y axes. The Z acceleration determines the height of the ball, as indicated by the ball radius and the square bracket (marked Z). +GH1 <- read.csv("~/Desktop/master fall/assignment8/gh 1- before.csv") +GH2 <- read.csv("~/Desktop/master fall/assignment8/gh2-after.csv") +ggplot(GH1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="han ge x-axis"))+geom_line(aes(y=lac_Y_value,color ="han ge y-axis"))+geom_line(aes(y=lac_Z_value,color="han ge z-axis")) + ggplot(GH2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="han ge x-axis"))+geom_line(aes(y=lac_Y_value,color ="han ge y-axis"))+geom_line(aes(y=lac_Z_value,color="han ge z-axis")) + +``` + +```{r} +CWJ1 <- read.csv("~/Desktop/master fall/assignment8/cwj1.csv") +CWJ2 <- read.csv("~/Desktop/master fall/assignment8/cwj2.csv") +ggplot(CWJ1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="CWJ x-axis"))+geom_line(aes(y=lac_Y_value,color ="CWJ y-axis"))+geom_line(aes(y=lac_Z_value,color="CWJ z-axis")) +ggplot(CWJ2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="CWJ x-axis"))+geom_line(aes(y=lac_Y_value,color ="CWJ y-axis"))+geom_line(aes(y=lac_Z_value,color="CWJ z-axis")) +``` + +```{r} +YCH1 <- read.csv("~/Desktop/master fall/assignment8/ych - before.csv") +YCH2 <- read.csv("~/Desktop/master fall/assignment8/ych - after.csv") +ggplot(YCH1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="YCH x-axis"))+geom_line(aes(y=lac_Y_value,color ="YCH y-axis"))+geom_line(aes(y=lac_Z_value,color="YCH z-axis")) +ggplot(YCH2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="YCH x-axis"))+geom_line(aes(y=lac_Y_value,color ="YCH y-axis"))+geom_line(aes(y=lac_Z_value,color="YCH z-axis")) +``` + + +```{r} +ERIC1 <- read.csv("~/Desktop/master fall/assignment8/eric before.csv") +ERIC2 <- read.csv("~/Desktop/master fall/assignment8/eric - after.csv") +ggplot(ERIC1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="ERIC x-axis"))+geom_line(aes(y=lac_Y_value,color ="ERIC y-axis"))+geom_line(aes(y=lac_Z_value,color="ERIC z-axis")) +ggplot(ERIC2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="ERIC x-axis"))+geom_line(aes(y=lac_Y_value,color ="ERIC y-axis"))+geom_line(aes(y=lac_Z_value,color="ERIC z-axis")) +``` + +```{R} +GH1 <- abs(GH1) +GH2 <- abs(GH2) +mean.gh1 <- c(mean(GH1$lac_X_value),mean(GH1$lac_Y_value),mean(GH1$lac_Z_value)) +var.gh1 <- c(var(GH1$lac_X_value),var(GH1$lac_Y_value),var(GH1$lac_Z_value)) +sd.gh1 <- sqrt(var.gh1) +mean.gh1 +var.gh1 +sd.gh1 +mean.gh2 <- c(mean(GH2$lac_X_value),mean(GH2$lac_Y_value),mean(GH2$lac_Z_value)) +var.gh2 <- c(var(GH2$lac_X_value),var(GH2$lac_Y_value),var(GH2$lac_Z_value)) +sd.gh2 <- sqrt(var.gh2) +mean.gh2 +var.gh2 +sd.gh2 +``` + +```{R} +CWJ1 <- abs(CWJ1) +CWJ2 <- abs(CWJ2) +mean.CWJ1 <- c(mean(CWJ1$lac_X_value),mean(CWJ1$lac_Y_value),mean(CWJ1$lac_Z_value)) +var.CWJ1 <- c(var(CWJ1$lac_X_value),var(CWJ1$lac_Y_value),var(CWJ1$lac_Z_value)) +sd.CWJ1 <- sqrt(var.CWJ1) +mean.CWJ1 +var.CWJ1 +sd.CWJ1 +mean.CWJ2 <- c(mean(CWJ2$lac_X_value),mean(CWJ2$lac_Y_value),mean(CWJ2$lac_Z_value)) +var.CWJ2 <- c(var(CWJ2$lac_X_value),var(CWJ2$lac_Y_value),var(CWJ2$lac_Z_value)) +sd.CWJ2 <- sqrt(var.CWJ2) +mean.CWJ2 +var.CWJ2 +sd.CWJ2 +``` + +```{R} +YCH1 <- abs(YCH1) +YCH2 <- abs(YCH2) +mean.YCH1 <- c(mean(YCH1$lac_X_value),mean(YCH1$lac_Y_value),mean(YCH1$lac_Z_value)) +var.YCH1 <- c(var(YCH1$lac_X_value),var(YCH1$lac_Y_value),var(YCH1$lac_Z_value)) +sd.YCH1 <- sqrt(var.YCH1) +mean.YCH1 +var.YCH1 +sd.YCH1 +mean.YCH2 <- c(mean(YCH2$lac_X_value),mean(YCH2$lac_Y_value),mean(YCH2$lac_Z_value)) +var.YCH2 <- c(var(YCH2$lac_X_value),var(YCH2$lac_Y_value),var(YCH2$lac_Z_value)) +sd.YCH2 <- sqrt(var.YCH2) +mean.YCH2 +var.YCH2 +sd.YCH2 +``` + +```{R} +ERIC1 <- abs(ERIC1) +ERIC2 <- abs(ERIC2) +mean.ERIC1 <- c(mean(ERIC1$lac_X_value),mean(ERIC1$lac_Y_value),mean(ERIC1$lac_Z_value)) +var.ERIC1 <- c(var(ERIC1$lac_X_value),var(ERIC1$lac_Y_value),var(ERIC1$lac_Z_value)) +sd.ERIC1 <- sqrt(var.ERIC1) +mean.ERIC1 +var.ERIC1 +sd.ERIC1 +mean.ERIC2 <- c(mean(ERIC2$lac_X_value),mean(ERIC2$lac_Y_value),mean(ERIC2$lac_Z_value)) +var.ERIC2 <- c(var(ERIC2$lac_X_value),var(ERIC2$lac_Y_value),var(ERIC2$lac_Z_value)) +sd.ERIC2 <- sqrt(var.ERIC2) +mean.ERIC2 +var.ERIC2 +sd.ERIC2 +``` + +```{r} +CB1 <- rbind(GH1,CWJ1,YCH1,ERIC1) +CB2 <- rbind(GH2,CWJ2,YCH2,ERIC2) +group <- c(1) +CB1 <- cbind(CB1,group) +group <- c(2) +CB2 <- cbind(CB2,group) +CB <- rbind(CB1,CB2) +str(CB) + +res1 <- t.test(CB1$lac_X_value,CB2$lac_X_value) +res1 +res2 <- t.test(CB1$lac_Y_value,CB2$lac_Y_value) +res2 +res3 <- t.test(CB1$lac_Z_value,CB2$lac_Z_value) +res3 +#z-axis has no significant difference + +library(lattice) +CB1 <- data.frame(CB1) +CB2 <- data.frame(CB2) +cloud(CB1$lac_Z_value~CB1$lac_X_value*CB1$lac_Y_value,data = CB1,color = "red") +cloud(CB2$lac_Z_value~CB2$lac_X_value*CB2$lac_Y_value,data = CB2) +``` +```{r} +time1<-c(10.754328-1.659422,10.10797-2.003494,12.410064-2.118693,8.82169-0.515078) +time2<-c(8.452365-0.890054,10.01876-2.752813,14.771162-7.462382,11.44068-3.715343) +time1 +time2 +height <- c(183,180,183,173) +weight<- c(95,80,80,48) +gender <-c('male','male','male','female') +group <- c(1) +D1 <- cbind(time1,height,weight,gender,group) +group <- c(2) +D2 <- cbind(time2,height,weight,gender,group) +D3 <- rbind(D1,D2) +D3 <- as.data.frame(D3) +D3$time1 <- as.character(D3$time1) +D3$height <-as.numeric(D3$height) +D3$weight <- as.numeric(D3$weight) +D3$group <- as.numeric(D3$group) +D3$group[which(D3$group == 1)] <- "before" +D3$group[which(D3$group == 2)] <- "after" +lm1 <- lm(formula =time1~height+weight+gender+group,data = D3) +summary(lm1) +options(contrasts = c("contr.sum", "contr.poly")) +lm2 <- lm(formula =time1~group,data = D3) +summary(lm2) +lm3 <- lm(formula =time1~1,data = D3) +summary(lm3) +anova(lm2,lm3) +``` + diff --git a/Untitled.html b/Untitled.html new file mode 100644 index 0000000..b75e3ea --- /dev/null +++ b/Untitled.html @@ -0,0 +1,440 @@ + + + + + + + + + + + + + +assignment 8 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + + +
library(dplyr)
+
## Warning: package 'dplyr' was built under R version 3.5.2
+
## 
+## Attaching package: 'dplyr'
+
## The following objects are masked from 'package:stats':
+## 
+##     filter, lag
+
## The following objects are masked from 'package:base':
+## 
+##     intersect, setdiff, setequal, union
+
library(tidyverse)
+
## -- Attaching packages --------------------------------------- tidyverse 1.2.1 --
+
## <U+221A> ggplot2 3.2.1     <U+221A> readr   1.3.1
+## <U+221A> tibble  2.1.3     <U+221A> purrr   0.2.5
+## <U+221A> tidyr   1.0.0     <U+221A> stringr 1.3.1
+## <U+221A> ggplot2 3.2.1     <U+221A> forcats 0.3.0
+
## Warning: package 'ggplot2' was built under R version 3.5.2
+
## Warning: package 'tibble' was built under R version 3.5.2
+
## Warning: package 'tidyr' was built under R version 3.5.2
+
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
+## x dplyr::filter() masks stats::filter()
+## x dplyr::lag()    masks stats::lag()
+
library(tidyr)
+library(GGally)
+
## 
+## Attaching package: 'GGally'
+
## The following object is masked from 'package:dplyr':
+## 
+##     nasa
+
library(ggplot2)
+library(dplyr)
+#The measured X and Y linear accelerations determine the position of the ball along the X and Y axes. The Z acceleration determines the height of the ball, as indicated by the ball radius and the square bracket (marked Z).
+GH1 <- read.csv("~/Desktop/master fall/assignment8/gh 1- before.csv")
+GH2 <- read.csv("~/Desktop/master fall/assignment8/gh2-after.csv")
+ggplot(GH1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="han ge x-axis"))+geom_line(aes(y=lac_Y_value,color ="han ge y-axis"))+geom_line(aes(y=lac_Z_value,color="han ge z-axis"))
+

+
 ggplot(GH2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="han ge x-axis"))+geom_line(aes(y=lac_Y_value,color ="han ge y-axis"))+geom_line(aes(y=lac_Z_value,color="han ge z-axis"))
+

+
CWJ1 <- read.csv("~/Desktop/master fall/assignment8/cwj1.csv")
+CWJ2 <- read.csv("~/Desktop/master fall/assignment8/cwj2.csv")
+ggplot(CWJ1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="CWJ x-axis"))+geom_line(aes(y=lac_Y_value,color ="CWJ y-axis"))+geom_line(aes(y=lac_Z_value,color="CWJ z-axis"))
+

+
ggplot(CWJ2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="CWJ x-axis"))+geom_line(aes(y=lac_Y_value,color ="CWJ y-axis"))+geom_line(aes(y=lac_Z_value,color="CWJ z-axis"))
+

+
YCH1 <- read.csv("~/Desktop/master fall/assignment8/ych - before.csv")
+YCH2 <- read.csv("~/Desktop/master fall/assignment8/ych - after.csv")
+ggplot(YCH1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="YCH x-axis"))+geom_line(aes(y=lac_Y_value,color ="YCH y-axis"))+geom_line(aes(y=lac_Z_value,color="YCH z-axis"))
+

+
ggplot(YCH2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="YCH x-axis"))+geom_line(aes(y=lac_Y_value,color ="YCH y-axis"))+geom_line(aes(y=lac_Z_value,color="YCH z-axis"))
+

+
ERIC1 <- read.csv("~/Desktop/master fall/assignment8/eric before.csv")
+ERIC2 <- read.csv("~/Desktop/master fall/assignment8/eric - after.csv")
+ggplot(ERIC1, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="ERIC x-axis"))+geom_line(aes(y=lac_Y_value,color ="ERIC y-axis"))+geom_line(aes(y=lac_Z_value,color="ERIC z-axis"))
+

+
ggplot(ERIC2, aes(x=time_tick)) + geom_line(aes(y=lac_X_value,color ="ERIC x-axis"))+geom_line(aes(y=lac_Y_value,color ="ERIC y-axis"))+geom_line(aes(y=lac_Z_value,color="ERIC z-axis"))
+

+
GH1 <- abs(GH1)
+GH2 <- abs(GH2)
+mean.gh1 <- c(mean(GH1$lac_X_value),mean(GH1$lac_Y_value),mean(GH1$lac_Z_value))
+var.gh1 <- c(var(GH1$lac_X_value),var(GH1$lac_Y_value),var(GH1$lac_Z_value))
+sd.gh1 <- sqrt(var.gh1)
+mean.gh1
+
## [1] 17.65577 11.31354 18.38602
+
var.gh1
+
## [1] 103.72839  76.66497 155.98830
+
sd.gh1
+
## [1] 10.184713  8.755853 12.489528
+
mean.gh2 <- c(mean(GH2$lac_X_value),mean(GH2$lac_Y_value),mean(GH2$lac_Z_value))
+var.gh2 <- c(var(GH2$lac_X_value),var(GH2$lac_Y_value),var(GH2$lac_Z_value))
+sd.gh2 <- sqrt(var.gh2)
+mean.gh2
+
## [1] 12.502761  8.803834 13.379346
+
var.gh2
+
## [1]  86.35995  70.95982 137.56951
+
sd.gh2
+
## [1]  9.293005  8.423765 11.729003
+
CWJ1 <- abs(CWJ1)
+CWJ2 <- abs(CWJ2)
+mean.CWJ1 <- c(mean(CWJ1$lac_X_value),mean(CWJ1$lac_Y_value),mean(CWJ1$lac_Z_value))
+var.CWJ1 <- c(var(CWJ1$lac_X_value),var(CWJ1$lac_Y_value),var(CWJ1$lac_Z_value))
+sd.CWJ1 <- sqrt(var.CWJ1)
+mean.CWJ1
+
## [1] 26.58828 30.50502 20.03264
+
var.CWJ1
+
## [1] 544.5335 702.4998 247.6344
+
sd.CWJ1
+
## [1] 23.33524 26.50471 15.73640
+
mean.CWJ2 <- c(mean(CWJ2$lac_X_value),mean(CWJ2$lac_Y_value),mean(CWJ2$lac_Z_value))
+var.CWJ2 <- c(var(CWJ2$lac_X_value),var(CWJ2$lac_Y_value),var(CWJ2$lac_Z_value))
+sd.CWJ2 <- sqrt(var.CWJ2)
+mean.CWJ2
+
## [1] 36.48182 36.30400 32.91006
+
var.CWJ2
+
## [1] 566.6613 880.6457 611.2729
+
sd.CWJ2
+
## [1] 23.80465 29.67567 24.72393
+
YCH1 <- abs(YCH1)
+YCH2 <- abs(YCH2)
+mean.YCH1 <- c(mean(YCH1$lac_X_value),mean(YCH1$lac_Y_value),mean(YCH1$lac_Z_value))
+var.YCH1 <- c(var(YCH1$lac_X_value),var(YCH1$lac_Y_value),var(YCH1$lac_Z_value))
+sd.YCH1 <- sqrt(var.YCH1)
+mean.YCH1
+
## [1] 17.043955 17.091830  7.198124
+
var.YCH1
+
## [1] 190.73751  75.01579  18.44315
+
sd.YCH1
+
## [1] 13.810775  8.661166  4.294549
+
mean.YCH2 <- c(mean(YCH2$lac_X_value),mean(YCH2$lac_Y_value),mean(YCH2$lac_Z_value))
+var.YCH2 <- c(var(YCH2$lac_X_value),var(YCH2$lac_Y_value),var(YCH2$lac_Z_value))
+sd.YCH2 <- sqrt(var.YCH2)
+mean.YCH2
+
## [1] 18.518670 19.959625  6.036058
+
var.YCH2
+
## [1] 255.80427 120.05320  20.65752
+
sd.YCH2
+
## [1] 15.993882 10.956879  4.545055
+
ERIC1 <- abs(ERIC1)
+ERIC2 <- abs(ERIC2)
+mean.ERIC1 <- c(mean(ERIC1$lac_X_value),mean(ERIC1$lac_Y_value),mean(ERIC1$lac_Z_value))
+var.ERIC1 <- c(var(ERIC1$lac_X_value),var(ERIC1$lac_Y_value),var(ERIC1$lac_Z_value))
+sd.ERIC1 <- sqrt(var.ERIC1)
+mean.ERIC1
+
## [1] 14.289840 14.460356  6.805683
+
var.ERIC1
+
## [1] 296.09811 224.29023  65.34541
+
sd.ERIC1
+
## [1] 17.207501 14.976322  8.083651
+
mean.ERIC2 <- c(mean(ERIC2$lac_X_value),mean(ERIC2$lac_Y_value),mean(ERIC2$lac_Z_value))
+var.ERIC2 <- c(var(ERIC2$lac_X_value),var(ERIC2$lac_Y_value),var(ERIC2$lac_Z_value))
+sd.ERIC2 <- sqrt(var.ERIC2)
+mean.ERIC2
+
## [1] 2.370068 2.819741 2.340801
+
var.ERIC2
+
## [1]  8.464091 13.856002  7.138585
+
sd.ERIC2
+
## [1] 2.909311 3.722365 2.671813
+
CB1 <- rbind(GH1,CWJ1,YCH1,ERIC1)
+CB2 <- rbind(GH2,CWJ2,YCH2,ERIC2)
+group <- c(1)
+CB1 <- cbind(CB1,group)
+group <- c(2)
+CB2 <- cbind(CB2,group)
+CB <- rbind(CB1,CB2)
+str(CB)
+
## 'data.frame':    1977 obs. of  5 variables:
+##  $ time_tick  : num  0.515 0.555 0.585 0.616 0.646 ...
+##  $ lac_X_value: num  5.98 12.36 17.61 20.93 20.92 ...
+##  $ lac_Y_value: num  5.805 3.815 0.282 3.505 1.258 ...
+##  $ lac_Z_value: num  7.12 9.16 10.15 5.4 11.66 ...
+##  $ group      : num  1 1 1 1 1 1 1 1 1 1 ...
+
res1 <- t.test(CB1$lac_X_value,CB2$lac_X_value)
+res1
+
## 
+##  Welch Two Sample t-test
+## 
+## data:  CB1$lac_X_value and CB2$lac_X_value
+## t = 2.3499, df = 1850.1, p-value = 0.01888
+## alternative hypothesis: true difference in means is not equal to 0
+## 95 percent confidence interval:
+##  0.3292568 3.6519906
+## sample estimates:
+## mean of x mean of y 
+##  19.23048  17.23985
+
res2 <- t.test(CB1$lac_Y_value,CB2$lac_Y_value)
+res2
+
## 
+##  Welch Two Sample t-test
+## 
+## data:  CB1$lac_Y_value and CB2$lac_Y_value
+## t = 2.5019, df = 1833.8, p-value = 0.01244
+## alternative hypothesis: true difference in means is not equal to 0
+## 95 percent confidence interval:
+##  0.4834682 3.9909953
+## sample estimates:
+## mean of x mean of y 
+##  18.94015  16.70292
+
res3 <- t.test(CB1$lac_Z_value,CB2$lac_Z_value)
+res3
+
## 
+##  Welch Two Sample t-test
+## 
+## data:  CB1$lac_Z_value and CB2$lac_Z_value
+## t = -0.22899, df = 1581.6, p-value = 0.8189
+## alternative hypothesis: true difference in means is not equal to 0
+## 95 percent confidence interval:
+##  -1.577594  1.247746
+## sample estimates:
+## mean of x mean of y 
+##  13.38379  13.54871
+
#z-axis has no significant difference
+
time1<-c(10.754328-1.659422,10.10797-2.003494,12.410064-2.118693,8.82169-0.515078)
+time2<-c(8.452365-0.890054,10.01876-2.752813,14.771162-7.462382,11.44068-3.715343)
+time1
+
## [1]  9.094906  8.104476 10.291371  8.306612
+
time2
+
## [1] 7.562311 7.265947 7.308780 7.725337
+
height <- c(183,180,183,173)
+weight<- c(95,80,80,48)
+gender <-c('male','male','male','female')
+group <- c(1)
+D1 <- cbind(time1,height,weight,gender,group)
+group <- c(2)
+D2 <- cbind(time2,height,weight,gender,group)
+D3 <- rbind(D1,D2)
+D3 <- as.data.frame(D3)
+D3$time1 <- as.character(D3$time1)
+D3$height <-as.numeric(D3$height)
+D3$weight <- as.numeric(D3$weight)
+D3$group <- as.numeric(D3$group)
+D3$group[which(D3$group == 1)] <- "before"
+D3$group[which(D3$group == 2)] <- "after"
+lm1 <- lm(formula =time1~height+weight+gender+group,data = D3)
+summary(lm1)
+
## 
+## Call:
+## lm(formula = time1 ~ height + weight + gender + group, data = D3)
+## 
+## Residuals:
+##        1        2        3        4        5        6        7        8 
+##  0.02442 -0.32261  0.74942 -0.45124 -0.02442  0.32261 -0.74942  0.45124 
+## 
+## Coefficients:
+##             Estimate Std. Error t value Pr(>|t|)   
+## (Intercept)   6.6307     0.9708   6.830  0.00642 **
+## height        1.1149     0.7615   1.464  0.23942   
+## weight       -0.4715     0.7615  -0.619  0.57969   
+## gendermale   -0.9742     1.3190  -0.739  0.51372   
+## groupbefore   1.4837     0.5385   2.755  0.07042 . 
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.7615 on 3 degrees of freedom
+## Multiple R-squared:  0.7678, Adjusted R-squared:  0.4582 
+## F-statistic:  2.48 on 4 and 3 DF,  p-value: 0.2407
+
options(contrasts = c("contr.sum", "contr.poly")) 
+lm2 <- lm(formula =time1~group,data = D3)
+summary(lm2)
+
## 
+## Call:
+## lm(formula = time1 ~ group, data = D3)
+## 
+## Residuals:
+##      Min       1Q   Median       3Q      Max 
+## -0.84487 -0.31042 -0.03005  0.17411  1.34203 
+## 
+## Coefficients:
+##             Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)   8.2075     0.2537  32.346 5.81e-08 ***
+## group1       -0.7419     0.2537  -2.924   0.0265 *  
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.7177 on 6 degrees of freedom
+## Multiple R-squared:  0.5876, Adjusted R-squared:  0.5188 
+## F-statistic: 8.548 on 1 and 6 DF,  p-value: 0.0265
+
lm3 <- lm(formula =time1~1,data = D3)
+summary(lm3)
+
## 
+## Call:
+## lm(formula = time1 ~ 1, data = D3)
+## 
+## Residuals:
+##     Min      1Q  Median      3Q     Max 
+## -0.9415 -0.7085 -0.2926  0.2962  2.0839 
+## 
+## Coefficients:
+##             Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)   8.2075     0.3658   22.44 8.84e-08 ***
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 1.035 on 7 degrees of freedom
+
anova(lm2,lm3)
+
## Analysis of Variance Table
+## 
+## Model 1: time1 ~ group
+## Model 2: time1 ~ 1
+##   Res.Df    RSS Df Sum of Sq      F Pr(>F)  
+## 1      6 3.0904                             
+## 2      7 7.4934 -1    -4.403 8.5484 0.0265 *
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+ + + + +
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