# 8.8: Chapter Key Terms

- Page ID
- 4594

- Binomial Distribution
- a discrete random variable (RV) which 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: \(X \sim B(\bf{n,p})\). The mean is \(\mu = np\) and the standard deviation is \(\sigma=\sqrt{n p q}\). The probability of exactly \(x\) successes in \(n\) trials is \(P(X=x)=\left(\begin{array}{l}{n} \\ {x}\end{array}\right) p^{x} q^{n-x}\).

- Confidence Interval (CI)
- an interval estimate for an unknown population parameter. This depends on:
- the desired confidence level,
- information that is known about the distribution (for example, known standard deviation),
- the sample and its size.

- Confidence Level (CL)
- the percent expression for the probability that the confidence interval contains the true population parameter; for example, if the CL = 90%, 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 four percent of the production is defective.

**Normal Distribution**- a continuous random variable (RV) with pdf \(f(x)=\frac{1}{\sigma \sqrt{2 \pi}} e^{-(x-\mu)^{2} / 2 \sigma^{2}}\), where \(\mu\) is the mean of the distribution and \(\sigma\) is the standard deviation, notation: \(X \sim N(\mu,\sigma)\). If \(\mu = 0\) and \(\sigma = 1\), the RV is called
**the standard normal distribution**.

**Parameter**- a numerical characteristic of a population

**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 \sigma for population standard deviation

**Student's**-Distribution**t**- investigated and reported by William S. Gossett in 1908 and published under the pseudonym Student; the major characteristics of this random variable (\(RV\)) are:
- It is continuous and assumes any real values.
- The pdf is symmetrical about its mean of zero.
- 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 depends upon the application for which the t is being used.