 5.1.1E: Define and give three examples of a random variable.
 5.1.2E: Explain the difference between a discrete and a continuous random v...
 5.1.3E: Give three examples of a discrete random variable.
 5.1.4E: Give three examples of a continuous random variable.
 5.1.6E: What is a probability distribution? Give an example.
 5.1.7E: determine whether the distribution represents a probability distrib...
 5.1.8E: determine whether the distribution represents a probability distrib...
 5.1.10E: determine whether the distribution represents a probability distrib...
 5.1.12E: determine whether the distribution represents a probability distrib...
 5.1.13E: state whether the variable is discrete or continuous.The number of ...
 5.1.14E: state whether the variable is discrete or continuous.The number of ...
 5.1.15E: state whether the variable is discrete or continuous.The weight of ...
 5.1.16E: state whether the variable is discrete or continuous.The time it ta...
 5.1.17E: state whether the variable is discrete or continuous.The number of ...
 5.1.18E: state whether the variable is discrete or continuous.The blood pres...
 5.1.19E: construct a probability distribution for the data and draw a graph ...
 5.1.20E: construct a probability distribution for the data and draw a graph ...
 5.1.21E: construct a probability distribution for the data and draw a graph ...
 5.1.22E: construct a probability distribution for the data and draw a graph ...
 5.1.23E: construct a probability distribution for the data and draw a graph ...
 5.1.24E: construct a probability distribution for the data and draw a graph ...
 5.1.25E: construct a probability distribution for the data and draw a graph ...
 5.1.26E: construct a probability distribution for the data and draw a graph ...
 5.1.31EC: write the distribution for the formula anddeterminewhether it is a ...
 5.1.32EC: write the distribution for the formula anddeterminewhether it is a ...
 5.1.33EC: write the distribution for the formula anddeterminewhether it is a ...
 5.1.34EC: write the distribution for the formula and determine whether it is ...
 5.1.35EC: write the distribution for the formula and determine whether it is ...
 5.1.36EC: write the distribution for the formula and determine whether it is ...
Solutions for Chapter 5.1: Elementary Statistics: A Step By Step Approach 9th Edition
Full solutions for Elementary Statistics: A Step By Step Approach  9th Edition
ISBN: 9780073534985
Solutions for Chapter 5.1
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2 k p  factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

Analytic study
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

Causal variable
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

Conditional mean
The mean of the conditional probability distribution of a random variable.

Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

Control limits
See Control chart.

Covariance
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

Defectsperunit control chart
See U chart

Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

Error of estimation
The difference between an estimated value and the true value.

Ftest
Any test of signiicance involving the F distribution. The most common Ftests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

Fisher’s least signiicant difference (LSD) method
A series of pairwise hypothesis tests of treatment means in an experiment to determine which means differ.

Gaussian distribution
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications