Distributia t

pt(0.8, 15, lower.tail=FALSE)   

## [1] 0.218099

## probabilitatea T>0.8 cu 15 df
qt(0.95, 15,lower.tail = TRUE)

## [1] 1.75305

## pragul pentru p<0.95 cu 15 df

Puterea la distributia t

power.t.test(n=16, delta=0.5, type="one.sample",   
             alt="one.sided")

## 
##      One-sample t test power calculation 
## 
##               n = 16
##           delta = 0.5
##              sd = 1
##       sig.level = 0.05
##           power = 0.6040329
##     alternative = one.sided

power.t.test(n=16, delta=0.5, sd=1.2, sig.level=0.01,   
             type="one.sample", alt="one.sided")

## 
##      One-sample t test power calculation 
## 
##               n = 16
##           delta = 0.5
##              sd = 1.2
##       sig.level = 0.01
##           power = 0.2098781
##     alternative = one.sided

library (pwr)
pwr.t.test(n=16, d=0.5, sig.level=0.05, type="one.sample", alternative="greater")

## 
##      One-sample t test power calculation 
## 
##               n = 16
##               d = 0.5
##       sig.level = 0.05
##           power = 0.6040329
##     alternative = greater

pwr.t.test(d=1, power=0.8, sig.level=0.05, type="paired", alternative="two.sided")

## 
##      Paired t test power calculation 
## 
##               n = 9.93785
##               d = 1
##       sig.level = 0.05
##           power = 0.8
##     alternative = two.sided
## 
## NOTE: n is number of *pairs*

pwr.t.test(d=1, power=0.8, sig.level=0.05, type="two.sample", alternative="two.sided")

## 
##      Two-sample t test power calculation 
## 
##               n = 16.71473
##               d = 1
##       sig.level = 0.05
##           power = 0.8
##     alternative = two.sided
## 
## NOTE: n is number in *each* group

Date binare – teste Chi-patrat si Fisher

dat1 <- matrix(c(4, 1, 2, 3), 2)
fisher.test(dat1, alternative="greater")

## 
##  Fisher's Exact Test for Count Data
## 
## data:  dat1
## p-value = 0.2619
## alternative hypothesis: true odds ratio is greater than 1
## 95 percent confidence interval:
##  0.3152217       Inf
## sample estimates:
## odds ratio 
##   4.918388

dat2 <- matrix(c(44, 77, 56, 43), 2)
chisq.test(dat2)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  dat2
## X-squared = 8.1667, df = 1, p-value = 0.004267

chisq.test(dat2, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  dat2
## X-squared = 8.963, df = 1, p-value = 0.002755

pchisq(20.59, 3, lower.tail=F)

## [1] 0.0001280682