Některé mezivýsledky z 12. cvičení
mtcars
mpg spotřeba paliva osobních automobil (počet mil/galon)
cyl počet válců
disp objem válců (kubické palce)
hp výkon (počet koní)
drat převodový poměr zadní nápravy
wt hmotnost vozidla (kilo-libry)
qsec zrychlení (počet sekund z 0 na 1/4 míle)
vs uspořádaní válců (1 vedle sebe, 0 za sebou)
am převodovka (0 automat, 1 manuál)
gear počet převodových stupňů
carb počet karburátorů
• Výkon na počtu válců
KW-test
$statistics
Chisq p.chisq
25.74616 2.566217e-06
shapiro.test(tabulka$mpg)
Shapiro-Wilk normality test
data: tabulka$mpg
W = 0.9476, p-value = 0.1229
shapiro.test(tabulka$disp)
Shapiro-Wilk normality test
data: tabulka$disp
W = 0.92, p-value = 0.02081
> shapiro.test(tabulka$hp)
Shapiro-Wilk normality test
data: tabulka$hp
W = 0.9334, p-value = 0.04881
shapiro.test(tabulka$drat)
Shapiro-Wilk normality test
data: tabulka$drat
W = 0.9459, p-value = 0.1101
shapiro.test(tabulka$wt)
Shapiro-Wilk normality test
data: tabulka$wt
W = 0.9433, p-value = 0.09265
shapiro.test(tabulka$qsec)
Shapiro-Wilk normality test
data: tabulka$qsec
W = 0.9733, p-value = 0.5935
cor.test(tabulka$disp, tabulka$mpg, method="spearman")
Spearman's rank correlation rho
data: tabulka$disp and tabulka$mpg
S = 10414.86, p-value = 6.37e-13
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.9088824
cor.test(tabulka$hp, tabulka$mpg, method="spearman")
Spearman's rank correlation rho
data: tabulka$hp and tabulka$mpg
S = 10337.29, p-value = 5.086e-12
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.8946646
cor.test(tabulka$hp, tabulka$mpg)
Pearson's product-moment correlation
data: tabulka$hp and tabulka$mpg
t = -6.7424, df = 30, p-value = 1.788e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.8852686 -0.5860994
sample estimates:
cor
-0.7761684
cor.test(tabulka$wt, tabulka$mpg)
Pearson's product-moment correlation
data: tabulka$wt and tabulka$mpg
t = -9.559, df = 30, p-value = 1.294e-10
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.9338264 -0.7440872
sample estimates:
cor
-0.8676594
cor.test(tabulka$qsec, tabulka$mpg)
Pearson's product-moment correlation
data: tabulka$qsec and tabulka$mpg
t = 2.5252, df = 30, p-value = 0.01708
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.08195487 0.66961864
sample estimates:
cor
0.418684
t.test(tabulka$mpg[tabulka$vs==0],tabulka$mpg[tabulka$vs==1])
Welch Two Sample t-test
data: tabulka$mpg[tabulka$vs == 0] and tabulka$mpg[tabulka$vs == 1]
t = -4.6671, df = 22.716, p-value = 0.0001098
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.462508 -4.418445
sample estimates:
mean of x mean of y
16.61667 24.55714
> shapiro.test(tabulka$mpg[tabulka$vs==0])
Shapiro-Wilk normality test
data: tabulka$mpg[tabulka$vs == 0]
W = 0.9515, p-value = 0.4491
> shapiro.test(tabulka$mpg[tabulka$vs==1])
Shapiro-Wilk normality test
data: tabulka$mpg[tabulka$vs == 1]
W = 0.9117, p-value = 0.1666
shapiro.test(tabulka$mpg[tabulka$am==1])
Shapiro-Wilk normality test
data: tabulka$mpg[tabulka$am == 1]
W = 0.9458, p-value = 0.5363
> shapiro.test(tabulka$mpg[tabulka$am==0])
Shapiro-Wilk normality test
data: tabulka$mpg[tabulka$am == 0]
W = 0.9768, p-value = 0.8987
> t.test(tabulka$mpg[tabulka$am==0],tabulka$mpg[tabulka$am==1])
Welch Two Sample t-test
data: tabulka$mpg[tabulka$am == 0] and tabulka$mpg[tabulka$am == 1]
t = -3.7671, df = 18.332, p-value = 0.001374
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.280194 -3.209684
sample estimates:
mean of x mean of y
17.14737 24.39231
KWTest <- kruskal (tabulka$mpg, tabulka$gear)
> KWTest
$statistics
Chisq p.chisq
14.32335 0.0007757547
$parameters
Df ntr t.value
2 3 2.04523
$means
tabulka$mpg std r Min Max
3 16.10667 3.371618 15 10.4 21.5
4 24.53333 5.276764 12 17.8 33.9
5 21.38000 6.658979 5 15.0 30.4
$rankMeans
tabulka$gear tabulka$mpg r
1 3 10.13333 15
2 4 23.79167 12
3 5 18.10000 5
$comparison
NULL
$groups
trt means M
1 4 23.79167 a
2 5 18.10000 a
3 3 10.13333 b
KWTest <- kruskal (tabulka$mpg, tabulka$carb)
> KWTest
$statistics
Chisq p.chisq
15.94149 0.007013126
$parameters
Df ntr t.value
5 6 2.055529
$means
tabulka$mpg std r Min Max
1 25.34286 6.001349 7 18.1 33.9
2 22.40000 5.472152 10 15.2 30.4
3 16.30000 1.053565 3 15.2 17.3
4 15.79000 3.911081 10 10.4 21.0
6 19.70000 NA 1 19.7 19.7
8 15.00000 NA 1 15.0 15.0
$rankMeans
tabulka$carb tabulka$mpg r
1 1 24.85714 7
2 2 20.60000 10
3 3 10.16667 3
4 4 9.35000 10
5 6 18.00000 1
6 8 6.00000 1
$comparison
NULL
$groups
trt means M
1 1 24.85714 a
2 2 20.60000 a
3 6 18.00000 ab
4 3 10.16667 b
5 4 9.35000 b
6 8 6.00000 b