Fundamentals of statistics using R
the hypothesis of McCulloch and Pitts
We shall not often be astray if we draw a conventional line at .05 , and consider that higher values of [the test statistic] indicate a real discrepancy.
the Bernoulli distribution
the binomial distribution
dbinom(x, size, prob, log = FALSE)
dbinom はパラメータ size と prob の二項分布の確率関数
s <- 0:527135
d <- dbinom(s, 527135, 0.2)
the Neyman-Pearson paradigm
the power of a statistical test
the probability of accepting some alternate hypothesis
cumulative distribution function
probability mass function
pbinom()
pbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE)
central tendency
mode: the most common value
The arithmetic mean, or simply the mean, also called the expected value
It is not meaningful for nominal variables or for ordinal variables, that is, for numerical variables
dispersion: how spread out are the likely outcomes
quartiles
The inter-quartile range (IQR)
the variance
skewness
right-skew
positive skew
left-skew or has negative skew
If the skew is zero, then the distribution is symmetric
log()
exp()
hist()
mean(), var(), or sd()
the normal or Gaussian distribution
It is a continuous distribution, unlike the binomial distribution
It is a symmetric distribution.
it has skewness zero
its mean is equal to its median
It has two parameters.
called μ and σ
μ , equal to its mean
σ , equal to its variance
rnorm(): the relevant normal distribution
pnorm(): to calculate an exact p -value
seq()
the hypothesis of McCulloch and Pitts
We shall not often be astray if we draw a conventional line at .05 , and consider that higher values of [the test statistic] indicate a real discrepancy.
the Bernoulli distribution
the binomial distribution
dbinom(x, size, prob, log = FALSE)
dbinom はパラメータ size と prob の二項分布の確率関数
s <- 0:527135
d <- dbinom(s, 527135, 0.2)
the Neyman-Pearson paradigm
the power of a statistical test
the probability of accepting some alternate hypothesis
cumulative distribution function
probability mass function
pbinom()
pbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE)
central tendency
mode: the most common value
The arithmetic mean, or simply the mean, also called the expected value
It is not meaningful for nominal variables or for ordinal variables, that is, for numerical variables
dispersion: how spread out are the likely outcomes
quartiles
The inter-quartile range (IQR)
the variance
skewness
right-skew
positive skew
left-skew or has negative skew
If the skew is zero, then the distribution is symmetric
log()
exp()
hist()
mean(), var(), or sd()
the normal or Gaussian distribution
It is a continuous distribution, unlike the binomial distribution
It is a symmetric distribution.
it has skewness zero
its mean is equal to its median
It has two parameters.
called μ and σ
μ , equal to its mean
σ , equal to its variance
rnorm(): the relevant normal distribution
pnorm(): to calculate an exact p -value
seq()