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Bus Stats Exam 3
Business Stats Ch 5-7
| Term | Definition |
|---|---|
| Binomial Experiment | An experiment having specific four properties |
| Binomial Probability Distribution | A probability distribution showing the probability of x successes in n trials of a binomial experiment |
| Binomial Probability Function | The function used to compute binomial probabilities |
| Bivariate Probability Distribution | A probability distribution involving two random variables. A discrete bivariate probability distribution provides a probability for each pair of values that may occur for the two random variables |
| Continuous Random Variable | A random variable that may assume any numerical value in an interval or collection of intervals |
| Discrete Random Variable | A random variable that may assume either a finite number of values or an infinite sequence of values |
| Discrete Uniform Probability Distribution | A probability distribution for which each possible value of the random variable has the same probability |
| Empirical Discrete Distribution | A discrete probability distribution for which the relative frequency method is used to assign the probabilities |
| Expected Value | A measure of the central location of a random variable |
| Hypergeometric Probability Distribution | A probability distribution showing the probability of x successes in n trials from a population with r successes and n − r failures |
| Hypergeometric Probability Function | The function used to compute hypergeometric probabilities |
| Poisson Probability Distribution | A probability distribution showing the probability of x occurrences of an event over a specified interval of time or space |
| Poisson Probability Function | The function used to compute Poisson probabilities |
| Probability Distribution | A description of how the probabilities are distributed over the values of the random variable |
| Probability Function | A function, denoted by f(x), that provides the probability that x assumes a particular value for a discrete random variable |
| Random Variable | A numerical description of the outcome of an experiment |
| Standard Deviation | The positive square root of the variance |
| Variance | A measure of the variability, or dispersion, of a random variable |
| Continuity Correction Factor | A value of .5 that is added to or subtracted from a value of x when the continuous normal distribution is used to approximate the discrete binomial distribution |
| Exponential Probability Distribution | A continuous probability distribution that is useful in computing probabilities for the time it takes to complete a task |
| Normal Probability Distribution | A continuous probability distribution. Its probability density function is bell-shaped and determined by its mean m and standard deviation s |
| Probability Density Function | A function used to compute probabilities for a continuous random variable. The area under the graph of a probability density function over an interval represents probability |
| Standard Normal Probability Distribution | A normal distribution with a mean of zero and a standard deviation of one |
| Uniform Probability Distribution | A continuous probability distribution for which the probability that the random variable will assume a value in any interval is the same for each interval of equal length |
| Central Limit Theorem | A theorem that enables one to use the normal probability distribution to approximate the sampling distribution of x whenever the sample size is large |
| Cluster Sampling | A probability sampling method in which the population is first divided into clusters and then a simple random sample of the clusters is taken |
| Consistency | A property of a point estimator that is present whenever larger sample sizes tend to provide point estimates closer to the population parameter |
| Convenience Sampling | A nonprobability method of sampling whereby elements are selected for the sample on the basis of convenience |
| Finite Population Correction Factor | The term Square Root (N-n)(N-1) used whenever a finite population, rather than an infinite population, is being sampled. The generally accepted rule of thumb is to ignore the finite population correction factor whenever n/N ≤ .05 |
| Frame | A listing of the elements the sample will be selected from |
| Judgement Sampling | A nonprobability method of sampling whereby elements are selected for the sample based on the judgment of the person doing the study |
| Parameter | A numerical characteristic of a population, such as a population mean m, a population standard deviation s, a population proportion p, and so on |
| Point Estimate | The value of a point estimator used in a particular instance as an estimate of a population parameter |
| Random Sample | A random sample from an infinite population is a sample selected such that the following conditions are satisfied: (1) Each element selected comes from the same population; (2) each element is selected independently |
| Relative Efficiency | Given two unbiased point estimators of the same population parameter, the point estimator with the smaller standard error is more efficient |
| Sampling Distribution | A probability distribution consisting of all possible values of a sample statistic |
| Sampled Population | The population from which the sample is taken |
| Sample Statistic | A sample characteristic, such as a sample mean x, a sample standard deviation s, a sample proportion p, and so on. The value of the sample statistic is used to estimate the value of the corresponding population parameter |
| Sampling Without Replacement | Once an element has been included in the sample, it is removed from the population and cannot be selected a second time |
| Sampling With Replacement | Once an element has been included in the sample, it is returned to the population. A previously selected element can be selected again and therefore may appear in the sample more than once |
| Simple Random Sample | A simple random sample of size n from a finite population of size n is a sample selected such that each possible sample of size n has the same probability of being selected |
| Standard Error | The standard deviation of a point estimator |
| Stratified Random Sampling | A probability sampling method in which the population is first divided into strata and a simple random sample is then taken from each stratum |
| Systematic Sampling | A probability sampling method in which we randomly select one of the first k elements and then select every kth element thereafter |
| Target Population | The population for which statistical inferences such as point estimates are made. It is important for the target population to correspond as closely as possible to the sampled population |
| Unbiased | A property of a point estimator that is present when the expected value of the point estimator is equal to the population parameter it estimates |
| Statistical Inference | The process of using data obtained from a sample to make estimates or test hypotheses about the characteristics of a population |
| Quantitative Data | Numeric values that indicate how much or how many of something. Quantitative data are obtained using either the interval or ratio scale of measurement |
| Relative Frequency Distribution | A tabular summary of data showing the fraction or pro- portion of observations in each of several nonoverlapping categories or classes |
| Interval Scale | The scale of measurement for a variable if the data demonstrate the proper- ties of ordinal data and the interval between values is expressed in terms of a fixed unit of measure. Interval data are always numeric |
| Frequency Distribution | A tabular summary of data showing the number (frequency) of observations in each of several nonoverlapping categories or classes |
| Mean | A measure of central location computed by summing the data values and dividing by the number of observations. |
| Mode | A measure of location, defined as the value that occurs with greatest frequency |
| Range | A measure of variability, defined to be the largest value minus the smallest value. |
| Standard Deviation | A measure of variability computed by taking the positive square root of the variance |
| Sample Statistic | A numerical value used as a summary measure for a sample |
| Skewness | A measure of the shape of a data distribution. Data skewed to the left result in negative skewness; a symmetric data distribution results in zero skewness; and data skewed to the right result in positive skewness |
| Probability | A numerical measure of the likelihood that an event will occur |
| Joint Probability | The probability of two events both occurring; that is, the probability of the intersection of two events |
| Marginal Probability | The probability of an event given that another event already occurred. The conditional probability of a given B is P(a ∣ B) = P(a ∩ B)/P(B) |
| Intersection of A and B | The event containing the sample points belonging to both a and B. The intersection is denoted a ∩ B. |
| Consumer Price Index | A monthly price index that uses the price changes in a market basket of consumer goods and services to measure the changes in consumer prices over time. |
| Union of A and B | The event containing all sample points belonging to a or B or both. The union is denoted a ∙ B |
| Combination | In an experiment we may be interested in determining the number of ways n objects may be selected from among n objects without regard to the order in which the n objects are selected |
| Experiment | A process that generates well-defined outcomes |
| Independent Events | Two events a and B where P(a ∣ B) = P(a) or P(B ∣ a) = P(B); that is, the events have no influence on each other |
| Mutually Exclusive Events | Events that have no sample points in common; that is, a ∩ B is empty and P(a ∩ B) = 0. |
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Faith64
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