Statistics

# Key Terms

StatisticsKey Terms
binomial distribution
a discrete random variable (RV) that arises from Bernoulli trials; there are a fixed number, n, of independent trials
Independent means that the result of any trial (for example, trial 1) does not affect the results of the following trials, and all trials are conducted under the same conditions. Under these circumstances, the binomial RV X is defined as the number of successes in n trials. The notation is X~B(n,p). The mean is μ = np, and the standard deviation is σ = $npq npq$. The probability of exactly x successes in n trials is $P ( X = x ) = n x p x q n − x P(X=x)= n x p x q n − x$.
confidence interval (CI)
an interval estimate for an unknown population parameter.
This depends on the following:
• the desired confidence level,
• information that is known about the distribution (for example, known standard deviation), and
• the sample and its size.
confidence level (CL)
the percentage expression for the probability that the confidence interval contains the true population parameter; for example, if the CL = 90 percent, then in 90 out of 100 samples, the interval estimate will enclose the true population parameter
degrees of freedom (df)
the number of objects in a sample that are free to vary
error bound for a population mean (EBM)
the margin of error; depends on the confidence level, sample size, and known or estimated population standard deviation
error bound for a population proportion (EBP)
the margin of error; depends on the confidence level, the sample size, and the estimated (from the sample) proportion of successes
inferential statistics
also called statistical inference or inductive statistics; this facet of statistics deals with estimating a population parameter based on a sample statistic
For example, if four out of the 100 calculators sampled are defective, we might infer that 4 percent of the production is defective.
normal distribution
a bell-shaped continuous random variable X, with center at the mean value (μ) and distance from the center to the inflection points of the bell curve given by the standard deviation (σ).
We write $X~N( μ,σ ) X~N( μ,σ )$. If the mean value is 0 and the standard deviation is 1, the random variable is called the standard normal distribution, and it is denoted with the letter Z
parameter
a numerical characteristic of a population
plus-four confidence interval
plus-four confidence interval when you add two imaginary successes and two imaginary failures (four overall) to your sample
point estimate
a single number computed from a sample and used to estimate a population parameter
standard deviation
a number that is equal to the square root of the variance and measures how far data values are from their mean; notation: s for sample standard deviation and σ for population standard deviation
Student's t-distribution
investigated and reported by William S. Gossett in 1908 and published under the pseudonym Student
the major characteristics of the random variable (RV) are as follows:
• It is continuous and assumes any real values.
• The pdf is symmetrical about its mean of zero. However, it is more spread out and flatter at the apex than the normal distribution.
• It approaches the standard normal distribution as n get larger.
• There is a family of t-distributions: Each representative of the family is completely defined by the number of degrees of freedom, which is one less than the number of data.
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