| d.single.t | R Documentation |
This function displays d and non-central confidence interval for single t from means.
d.single.t(m, u, sd, n, a = 0.05)
m |
sample mean |
u |
population mean |
sd |
sample standard deviation |
n |
sample size |
a |
significance level |
To calculate d, the population is subtracted from the sample mean, which is then divided by the standard deviation.
d = (m - u) / sd
Learn more on our example page.
d |
effect size |
dlow |
lower level confidence interval d value |
dhigh |
upper level confidence interval d value |
m |
sample mean |
sd |
standard deviation of the sample |
se |
standard error of the sample |
Mlow |
lower level confidence interval of the sample mean |
Mhigh |
upper level confidence interval of the sample mean |
u |
population mean |
n |
sample size |
df |
degrees of freedom (n - 1) |
t |
t-statistic |
p |
p-value |
estimate |
the d statistic and confidence interval in APA style for markdown printing |
statistic |
the t-statistic in APA style for markdown printing |
#The following example is derived from the "singt_data" dataset included
#in the MOTE library.
#A school has a gifted/honors program that they claim is
#significantly better than others in the country. The gifted/honors
#students in this school scored an average of 1370 on the SAT,
#with a standard deviation of 112.7, while the national average
#for gifted programs is a SAT score of 1080.
gift = t.test(singt_data, mu = 1080, alternative = "two.sided")
#You can type in the numbers directly as shown below,
#or refer to your dataset within the function.
d.single.t(m = 1370, u = 1080, sd = 112.7, n = 14, a = .05)
d.single.t(1370, 1080, 112.7, 100, .05)
d.single.t(gift$estimate, gift$null.value,
sd(singt_data$SATscore),
length(singt_data$SATscore), .05)