Psyc 181 (2/2016)
R Commandsand Output for Data Analysis Problems
Problem 1-1cd
dollars= c(30, 20, 15, 10, 10, 60, 20, 25, 20, 30, 10, 5, 50, 40, 20, 10, 10, 0, 20, 50)
t.test(dollars, mu = 15, conf.level = 0.95)
sd(dollars)
One Sample t-test
data: dollars
t = 2.1315, df = 19, p-value = 0.04633
alternative hypothesis: true mean is not equal to 15
95 percent confidence interval:
15.13979 30.36021
sample estimates:
mean of x
22.75
sd(dollars)
[1] 16.26062
Note: If the data had been saved in an SPSS .sav file called HW1-1.sav on the working directory, the following commands will give the same results as shown above. First, install the foreign package using the command: install.packages("foreign", dep = TRUE)
library(foreign, pos=4)
data=read.spss("HW1-1.sav", to.data.frame = T)
attach(data)
t.test(dollars, mu = 15, conf.level = 0.95)
sd(dollars)
Note: When reading SPSS files, R will display the warning: Unrecognized record type 7, subtype 18 encountered in system file. This warning can be ignored.
Problem 1-1e
[copy sizeCIMean1code and paste at R prompt]
sizeCImean1(.05, 264.39, 10)
[1] 41
Problem 1-2cd
hours= c(5.5, 5.0, 6.5, 7.0, 4.5, 6.0, 5.0, 7.5, 5.0, 6.0, 8.0, 5.0, 6.5, 5.5, 7.0)
t.test(hours, mu = 6.8, conf.level = 0.95)
sd(hours)
One Sample t-test
data: hours
t = -2.9447, df = 14, p-value = 0.01066
alternative hypothesis: true mean is not equal to 6.8
95 percent confidence interval:
5.417306 6.582694
sample estimates:
mean of x
6
sd(hours)
[1] 1.052209
Problem 1-1e
sizeCImean1(.05, 1.107, .5)
[1] 69
Problem2-1c
group1 =c(32, 39, 26, 35, 43, 27, 40, 37, 34, 29)
group2 =c(36, 44, 47, 42, 49, 39, 46, 31, 33, 48)
t.test(group1,group2, conf.level = 0.95, var.equal=T)
sd(group1)
sd(group2)
Two Sample t-test
data: group1 and group2
t = -2.6793, df = 18, p-value = 0.01531
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-13.024129 -1.575871
sample estimates:
mean of x mean of y
34.2 41.5
sd(group1)
[1] 5.711587
sd(group2)
[1] 6.450667
Problem2-1d
[copy sizeCIMean2 code and paste at R prompt]
sizeCImean2(.05, 37.1, 5)
[1] 46
Problem2-2c
group1 = c(41,59,34,76,59,44,32,29,68,71,50,62,36,55,39,72,63,45,38,60,48,63)
group2 = c(64,49,54,36,49,64,74,35,58,40,60,51,68,67,25,37,40,53,72,38,61,40)
t.test(group1,group2, conf.level = 0.95, var.equal=T)
sd(group1)
sd(group2)
Two Sample t-test
data: group1 and group2
t = 0.097, df = 42, p-value = 0.9232
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-8.100917 8.919099
sample estimates:
mean of x mean of y
52.00000 51.59091
sd(group1)
[1] 14.19926
sd(group2)
[1] 13.76904
Problem2-2d
sizeCImean2(.05, 195.6, 8)
[1] 94
Problem2-3c
placebo= c(940, 1120, 1000, 880, 1140, 950, 900, 1060)
betablock= c(1270, 1050, 1010, 1190, 1030, 1230, 1140, 980)
t.test(placebo,betablock, conf.level = 0.95, var.equal=T)
sd(placebo)
sd(betablock)
Two Sample t-test
data: placebo and betablock
t = -2.1816, df = 14, p-value = 0.04668
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-225.578092 -1.921908
sample estimates:
mean of x mean of y
998.75 1112.50
sd(placebo)
[1] 98.62447
sd(betablock)
[1] 109.6423
Note: If the data had been saved in an SPSS or PSPP .sav file called HW2-3.sav on the working directory with a group indicator variable (1 for placebo and 2 for betablock)in the first column and the 16 SAT scores in the second column, the following commands will give the same results as shown above.
library(foreign, pos = 4)
data = read.spss("HW2-3.sav", to.data.frame = T)
attach(data)
t.test(SAT~group, conf.level = 0.95, var.equal = T)
sd(SAT[(group==1)]); sd(SAT[(group==2)])
Problem2-3d
sizeCImean2(.05, 10873, 100)
[1] 34
Problem3-1bc
pdsc=c(12, 10, 11, 14, 15, 9, 11, 12, 13, 10, 15, 10, 12, 13, 17, 11, 16, 13, 12, 14, 17, 12, 16, 18, 14, 21, 17, 16, 17, 22, 16, 22, 19, 20, 18, 16)
groups=c(1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3)
group=factor(groups, labels=c("Escitalopram", "Citalopram", "Placebo"))
panicData=data.frame(pdsc, group)
out=aov(pdsc~group, data=panicData)
summary(out)
TukeyHSD(out)
print(model.tables(out,"means"))
Df Sum Sq Mean Sq F value Pr(>F)
group 2 245.2 122.58 21.8 9.25e-07 ***
Residuals 33 185.6 5.62
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(out)
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = pdsc ~ group, data = panicData)
$group
diff lwr upr p adj
Citalopram-Escitalopram 2.416667 0.04105701 4.792276 0.0454940
Placebo-Escitalopram 6.333333 3.95772367 8.708943 0.0000006
Placebo-Citalopram 3.916667 1.54105701 6.292276 0.0008425
print(model.tables(out,"means"))
Tables of means
group
Escitalopram Citalopram Placebo
11.833 14.250 18.167
Note: If the data had been saved in an SPSS or PSPP .sav file called HW3-1.sav on the working directory, the following commands will give the same results as shown above.
library(foreign, pos=4)
data=read.spss("HW3-1.sav", to.data.frame = T)
attach(data)
group= factor(drug, labels=c("Escitalopram", "Citalopram", "Placebo"))
out=aov(PDSC~group)
summary(out)
TukeyHSD(out)
Problem3-1d
Install MBESS package using the following command: install.packages("MBESS", dep = TRUE)
library(MBESS)
# set R2 equal to sample eta-squared (or sample partial eta-squared)
# set N equal to total sample size
# set K equal to number of levels of factor minus one
ci.R2(R2=.569, N=36, K=2, conf.level=.95, Random.Predictors=F)
$Lower.Conf.Limit.R2
[1] 0.301424
$Upper.Conf.Limit.R2
[1] 0.6937411
Problem3-1e
[copysizeCImeanBS (or sizeCIMean2) code and paste at R prompt]
h = c(1, -1, 0)
sizeCImeanBS(.0167, 5.62, 2.5, h)
[,1]
[1,] 42
Problem3-2c
score= c(14, 15, 11, 7, 16, 12, 15, 16, 10, 9, 18, 24, 14, 18, 22, 21, 16, 17, 14, 13, 16, 11, 10, 17, 13, 18, 12, 16, 6, 15, 18, 17, 11, 9, 9, 13, 18, 15, 14, 11)
gender=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2)
payment=c(1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2)
gender=factor(gender, labels=c("male", "female"))
payment=factor(payment, labels=c("flat_rate", "per_item"))
mydata=data.frame(score, gender, payment)
out=aov(score~gender+payment+gender*payment, data=mydata)
summary(out)
print(model.tables(out,"means"))
Df Sum Sq Mean Sq F value Pr(>F)
gender 1 27.2 27.23 2.200 0.1467
payment 1 70.2 70.23 5.675 0.0226 *
gender:payment 1 65.0 65.02 5.255 0.0278 *
Residuals 36 445.5 12.38
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
print(model.tables(out,"means"))
Tables of means
gender
male female
15.10 13.45
payment
flat_rate per_item
12.95 15.60
gender:payment
payment
gender flat_rateper_item
male 12.5 17.7
female 13.4 13.5
[continued]
group1 =c(14, 15, 11, 7, 16, 12, 15, 16, 10, 9)
group2 =c(18, 24, 14, 18, 22, 21, 16, 17, 14, 13)
group3 =c(16, 11, 10, 17, 13, 18, 12, 16, 6, 15)
group4 =c(18, 17, 11, 9, 9, 13, 18, 15, 14, 11)
m1 =mean(group1)
m2 =mean(group2)
m3 =mean(group3)
m4 =mean(group4)
sd1 =sd(group1)
sd2 =sd(group2)
sd3 =sd(group3)
sd4 =sd(group4)
m =c(m1, m2, m3, m4)
sd=c(sd1, sd2, sd3, sd4)
n =c(length(group1),length(group2), length(group3), length(group4))
[copyCIMeancode and paste at R prompt]
h =c(1, -1, 0, 0)
CIMean(.025, m, sd, n, h)
Estimate SE t df p-value LL UL
Equal Variances Assumed: -5.2 1.573213 -3.305337 36.00000 0.002154912 -8.879842 -1.520158
Equal Variances Not Assumed: -5.2 1.536952 -3.383319 17.61093 0.003393857 -8.965364 -1.434636
h =c(0, 0, 1, -1)
CIMean(.025, m, sd, n, h)
Estimate SE t df p-value LL UL
Equal Variances Assumed: -0.1 1.573213 -0.06356417 36.0000 0.9496689 -3.779842 3.579842
Equal Variances Not Assumed: -0.1 1.608657 -0.06216365 17.9165 0.9511208 -4.034831 3.834831
Problem3-2d
[copy sizeCIMean2 code and paste at R prompt]
sizeCImean2(.025, 12.38, 3)
[1] 56
Problem3-3a
speed = c(265,300,253,270,240,245,251,210,214,290,334,302,268,311,308,250,274,265,295,300)
style = c(1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2)
size = c(1,1,1,1,1,2,2,2,2,2,1,1,1,1,1,2,2,2,2,2)
style= factor(style, labels=c("Ariel", "Times"))
size= factor(size, labels=c("12", "10"))
mydata=data.frame(speed, style, size)
out=aov(speed~style + size + style*size, data=mydata)
summary(out)
print(model.tables(out,"means"))
Df Sum Sq Mean Sq F value Pr(>F)
style 1 6808 6808 10.661 0.00486 **
size 1 3302 3302 5.172 0.03708 *
style:size 1 22 22 0.035 0.85492
Residuals 16 10217 639
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
style
Ariel Times
253.8 290.7
size
12 10
285.1 259.4
Problem3-3b
group1 = c(265,300,253,270,240)
group2 = c(245,251,210,214,290)
group3 = c(334,302,268,311,308)
group4 = c(250,274,265,295,300)
m1 = mean(group1)
m2 = mean(group2)
m3 = mean(group3)
m4 = mean(group4)
sd1 = sd(group1)
sd2 = sd(group2)
sd3 = sd(group3)
sd4 = sd(group4)
m = c(m1, m2, m3, m4)
sd = c(sd1, sd2, sd3, sd4)
n = c(length(group1), length(group2), length(group3), length(group4))
[copyCIMeanBScode and paste at R prompt]
h =c(.5, .5, -.5, -.5)
CIMeanBS(.05, m, sd, n, h)
Estimate SE t df p-value LL UL
Equal Variances Assumed: -36.9 11.30111 -3.265167 16.00000 0.004863265 -60.85727 -12.94273
Equal Variances Not Assumed: -36.9 11.30111 -3.265167 13.98525 0.005646721 -61.14086 -12.65914
h =c(.5, -.5, .5, -.5)
CIMeanBS(.05, m, sd, n, h)
Estimate SE t df p-value LL UL
Equal Variances Assumed: 25.7 11.30111 2.274114 16.00000 0.03708247 1.742725 49.65727
Equal Variances Not Assumed: 25.7 11.30111 2.274114 13.98525 0.03924479 1.459140 49.94086
Problem3-3c
[copysizeCImeanBS code and paste at R prompt]
h = c(.5, .5, -.5, -.5)
sizeCImeanBS(.025, 639, 20, h)
[,1]
[1,] 33
Homework 4-1de
volume = c(23, 21, 21, 20, 24, 23, 22, 23, 19, 24, 23, 22, 23, 22)
hours = c(3, 0, 2, 0, 5, 4, 1, 2, 0, 8, 4, 3, 5, 6)
out= lm(volume~hours)
summary(out)
confint(out)
plot(volume, hours)
Residuals:
Min 1Q Median 3Q Max
-1.7112 -0.5926 0.1235 0.7231 1.3565
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.7112 0.4218 49.10 3.33e-15 ***
hours 0.4661 0.1092 4.27 0.00109 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9575 on 12 degrees of freedom Note: MSe = .95752
Multiple R-squared: 0.6031, Adjusted R-squared: 0.57
F-statistic: 18.23 on 1 and 12 DF, p-value: 0.001088
confint(out)
2.5 % 97.5 %
(Intercept) 19.7922481 21.6302217
hours 0.2282612 0.7039579
Homework 4-1f
[copysizeCIslope code and paste at R prompt]
sizeCIslope(.05, 0.92, 5.9, .25)
[1] 40
Problem4-2de
child = c(16.10, 11.20, 13.20, 14.10, 7.20, 13.00, 10.50, 11.70, 15.80, 7.20, 13.10, 17.90, 14.20, 10.70, 16.70, 12.20, 13.40, 9.60, 13.90, 15.80, 18.40, 11.90, 16.30, 15.50, 19.10, 8.80, 14.60, 15.00, 16.00, 11.90)
parent =c(12.20, 12.60, 8.90, 13.50, 14.10, 8.20, 11.70, 15.20, 19.30, 11.20, 13.90, 16.00, 16.70, 13.00, 13.60, 11.30, 19.20, 13.60, 12.60, 13.10, 20.20, 13.00, 11.80, 11.80, 22.20, 10.10, 14.50, 19.30, 19.70, 18.80)
out= lm(child~parent)
summary(out)
confint(out)
cor.test(child, parent, alternative="two.sided", method="pearson")
plot(child, parent)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.5914 2.1205 3.580 0.00128 **
parent 0.4110 0.1433 2.868 0.00776 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.751 on 28 degrees of freedom
Multiple R-squared: 0.2271, Adjusted R-squared: 0.1995
F-statistic: 8.226 on 1 and 28 DF, p-value: 0.007764
confint(out)
2.5 % 97.5 %
(Intercept) 3.247773 11.9349873
parent 0.117458 0.7045155
cor.test(child, parent, alternative="two.sided", method="pearson")
Pearson's product-moment correlation
data: child and parent
t = 2.8681, df = 28, p-value = 0.007764
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1403478 0.7141834
sample estimates:
cor
0.4765226
Note: If the data had been saved in an SPSS or PSPP .sav file called HW4-2.sav on the working directory, the following commands will give the same results as shown above.
library(foreign, pos=4)
Dataset =read.spss("HW4-2.sav", to.data.frame = T)
attach(Dataset)
out= lm(child~parent, data=Dataset)
summary(out)
confint(out)
cor.test(child, parent, alternative="two.sided", method="pearson")
plot(parent, child)
Problem4-2h
[copysizeCIcorr code and paste at R prompt]
sizeCIcorr(.05, .14, .25)
[1] 237
Problem4-3bcde
[copyCIcorrand sizeCIcorrcode and paste at R prompt]
CIcorr(.05, .82, 200)
[1] 0.7687137 0.8608088
CIcorr(.05, .68, 200)
[1] 0.5976429 0.7481570
CIcorr(.05, .87, 200)
[1] 0.8316421 0.9000953
sizeCIcorr(.05, .598, .1)
[1] 638
Problem5-1d
sonaggr= c(50, 62, 62, 50, 49, 60, 40, 36, 79, 39, 36, 55, 52, 42, 59, 50, 57, 60, 58, 67)
hours= c(5, 6, 2, 5, 3, 6, 4, 2, 7, 4, 0, 4, 2, 0, 4, 4, 5, 7, 4, 3)
fatheraggr= c(62, 54, 73, 39, 51, 57, 30, 45, 64, 33, 45, 46, 52, 35, 51, 42, 38, 65, 47, 76)
out= lm(sonaggr~fatheraggr+hours)
summary(out)
confint(out)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.4598 6.1211 2.852 0.011018 *
fatheraggr 0.5128 0.1173 4.371 0.000416 ***
hours 2.5774 0.7635 3.376 0.003591 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.415 on 17 degrees of freedom
Multiple R-squared: 0.6985, Adjusted R-squared: 0.663
F-statistic: 19.69 on 2 and 17 DF, p-value: 3.748e-05
confint(out)
2.5 % 97.5 %
(Intercept) 4.5454324 30.3742633
fatheraggr 0.2652648 0.7602899
hours 0.9665498 4.1883029
Note: If the data had been saved in an SPSS or PSPP .sav file called HW5-1.sav on the working directory, the following commands will give the same results as shown above.
library(foreign, pos=4)
Dataset =read.spss("HW5-1.sav", to.data.frame=T)
attach(Dataset)
out= lm(sonaggr~fatheraggr+hours, data=Dataset)
summary(out)
confint(out)
Problem5-1e
efather = residuals(lm(fatheraggr ~ hours, data = Dataset))
ehours = residuals(lm(hours ~ fatheraggr, data = Dataset))
cor(sonaggr, efather)
cor(sonaggr, ehours)
cor(sonaggr, efather)
[1] 0.5820847
cor(sonaggr, ehours)
[1] 0.4495506
[pasteCIsemipartcorr at R prompt]
CIsemipartcorr(.05, .582, .699, 20)
[1] 0.2525662 0.7905182
CIsemipartcorr(.05, .450, .699, 20)
[1] 0.1359897 0.6818499
Problem5-1f
library(MBESS)
# set R2 equal to sample squared multiple correlation
# set N equal to total sample size
# set K equal to number of predictor variables
ci.R2(R2=.6985, N=20, K=2, conf.level=.95, Random.Predictors=T)
$Lower.Conf.Limit.R2
[1] 0.3368534
$Upper.Conf.Limit.R2
[1] 0.8551323
Problem5-1g
[copysizeCImulticorr code and paste at R prompt]
sizeCImultcorr(.05, 2, .6, .2)
[1] 152
Problem5-2c
lifesat=c(11,12,11,11,9,8,8,6,8,10,8,7,9,11,9,10,11,11,11,10,7,9,8,9,10,6,6,11,12,7,7,10,9,11,8, 8,7,8,9,9)
commit=c(5,4,4,5,5,4,4,5,4,4,4,6,4,4,4,6,5,5,3,3,5,6,5,5,4,6,4,5,5,3,4,5,4,3,3,5,5,3,5,5)
jobsat=c(20,22,18,21,19,11,15,15,18,17,17,18,16,19,15,21,21,20,18,16,17,20,13,19,18,16,13,21,22,
12,23,15,16,16,15,19,16,13,19,20)
out= lm(jobsat~lifesat+commit)
summary(out)
confint(out)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4019 2.5933 0.541 0.592022
lifesat 1.0069 0.1970 5.113 9.96e-06 ***
commit 1.5697 0.3844 4.083 0.000228 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.095 on 37 degrees of freedom
Multiple R-squared: 0.5169, Adjusted R-squared: 0.4908
F-statistic: 19.79 on 2 and 37 DF, p-value: 1.429e-06
2.5 % 97.5 %
(Intercept) -3.8525734 6.656453
lifesat 0.6078764 1.406022
commit 0.7908063 2.348595
Problem5-2d
library(MBESS)
# set R2 equal to sample squared multiple correlation
# set N equal to total sample size
# set K equal to number of predictor variables
ci.R2(R2=.5169, N=40, K=2, conf.level=.95, Random.Predictors=T)
$Lower.Conf.Limit.R2
[1] 0.2514043
$Upper.Conf.Limit.R2
[1] 0.6946407
Problem5-3d
read= c(20, 36, 72, 40, 95, 71, 65, 48, 55, 85, 92, 45)
TVhours=c(15, 12, 8, 10, 5, 8, 8, 10, 10, 7, 4, 12)
IQ=c(82, 96, 112, 90, 130, 121, 115, 98, 105, 120, 128, 95)
out= lm(read~TVhours+ IQ)
summary(out)
confint(out)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.4151 38.0910 -0.247 0.81032
TVhours -3.0628 1.2699 -2.412 0.03913 *
IQ 0.9062 0.2511 3.609 0.00567 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.506 on 9 degrees of freedom
Multiple R-squared: 0.9702, Adjusted R-squared: 0.9636
F-statistic: 146.5 on 2 and 9 DF, p-value: 1.362e-07
confint(out)
2.5 % 97.5 %
(Intercept) -95.5828158 76.7526479
TVhours -5.9354538 -0.1901863
IQ 0.3381213 1.4743065
Problem6-1c
oct= c(35, 19, 20, 31, 25, 20, 39, 29, 18, 22, 25, 30)
nov= c(27, 18, 22, 24, 15, 17, 30, 22, 14, 19, 19, 26)
t.test(oct, nov, paired=T)
cor(oct, nov); sd(oct); sd(nov)
Paired t-test
data: oct and nov
t = 4.9625, df = 11, p-value = 0.0004271
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
2.782403 7.217597
sample estimates:
mean of the differences
5
cor(oct, nov); sd(oct); sd(nov)
[1] 0.8667044
[1] 6.761634
[1] 4.96274
Problem6-1d
[copysizeCImeanWS code and paste at R prompt]
sizeCImeanWS(.05, 35.15, .867, 2.5)
[1] 23
Problem6-2c
wb1 =c(25, 20, 39, 29, 18, 22, 25, 30)
wb2 =c(22, 18, 34, 25, 16, 21, 22, 27)
wb3 =c(15, 17, 30, 22, 14, 19, 19, 26)
t.test(wb1, wb2, paired=T, conf.level = .9833)
t.test(wb1, wb3, paired=T, conf.level = .9833)
t.test(wb2, wb3, paired=T, conf.level = .9833)
sd(wb1); sd(wb2);sd(wb3)
cor(wb1, wb2); cor(wb1, wb3); cor(wb2, wb3)
Paired t-test
data: wb2 and wb3
t = 4.1501, df = 7, p-value = 0.004295
alternative hypothesis: true difference in means is not equal to 0
98.33 percent confidence interval:
0.7093593 5.0406407
sample estimates:
mean of the differences
2.875
sd(wb1); sd(wb2);sd(wb3)
[1] 6.676184
[1] 5.617257
[1] 5.496752
cor(wb1, wb2); cor(wb1, wb3); cor(wb2, wb3)
[1] 0.994237
[1] 0.9187123
[1] 0.9380638
Note: If the data had been saved in an SPSS or PSPP .sav file called HW6-2.sav on the working directory, the following commands will give the same results as shown above.
library(foreign, pos=4)
data=read.spss("HW6-2.sav", to.data.frame = T)
attach(data)
t.test(WB1, WB2, paired= T, conf.level = .9833)
t.test(WB1, WB3, paired= T, conf.level = .9833)
t.test(WB2, WB3, paired= T, conf.level = .9833)
sd(WB1); sd(WB2); sd(WB3)
cor(WB1, WB2); cor(WB1, WB3); cor(WB2, WB3)
Problem6-2d
[copysizeCImeanWS code and paste at R prompt]
sizeCImeanWS(.0167, 44.6, .919, 2)
[1] 42
Problem6-3cd
LR =c(21, 39, 32, 29, 27, 17, 27, 21, 28, 17, 12, 27)
LS =c(20, 36, 33, 27, 28, 14, 30, 20, 27, 15, 11, 22)
HR =c(21, 36, 30, 27, 28, 15, 27, 18, 29, 16, 11, 22)
HS =c(17, 33, 28, 27, 27, 16, 26, 20, 25, 15, 13, 22)
Interaction = (LR - LS) - (HR - HS)
Traffic <- (LR + LS)/2 - (HR + HS)/2
Mode <- (LR + HR)/2 - (LS + HS)/2
t.test(Interaction, mu = 0, conf.level = .95)
t.test(Traffic, mu = 0, conf.level = .975)
t.test(Mode, mu = 0, conf.level = .975)
One Sample t-test
data: Interaction
t = 0.2736, df = 11, p-value = 0.7895
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-1.761497 2.261497
One Sample t-test
data: Traffic
t = 3.5245, df = 11, p-value = 0.004761
alternative hypothesis: true mean is not equal to 0
97.5 percent confidence interval:
0.3413349 2.2419984
One Sample t-test
data: Mode
t = 2.6286, df = 11, p-value = 0.02347
alternative hypothesis: true mean is not equal to 0
97.5 percent confidence interval:
0.01407164 2.06926170
Problem7-1c
[copyCIprop1 code and paste at R prompt]
CIprop1(.05, 60, 500)
[1] 0.09434049 0.15169126
Problem 7-2d
[copyCIprop2 code and paste at R prompt]
CIprop2(.05, 41, 68, 150, 150)
[1] -0.28401891 -0.07124424
Problem 7-3c
[copyCIpropWScode and paste at R prompt]
CIpropWS(.05, 96, 68, 400)
[1] 0.007204993 0.132098489