 8.2.1E: (In some of the exercises that follow, we must make assumptions suc...
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 8.2.14E: (In some of the exercises that follow, we must make assumptions suc...
 8.2.8.21: The botanist in Example 8.21 is really interested in testing for s...
 8.2.8.22: Let X and Y denote the weights in grams of male and female common g...
 8.2.8.23: Let X equal the weight in grams of a LowFat Strawberry Kudo and Y ...
 8.2.8.24: Among the data collected for the World Health Organization air qual...
 8.2.8.25: Some nurses in county public health conducted a survey of women who...
 8.2.8.26: Let X and Y equal the forces required to pull stud No. 3 and stud N...
 8.2.8.27: Let X and Y equal the number of milligrams of tar in filtered and n...
 8.2.8.28: Let X and Y denote the tarsus lengths of male and female grackles, ...
 8.2.8.29: When a stream is turbid, it is not completely clear due to suspende...
 8.2.8.210: Plants convert carbon dioxide (CO2) in the atmosphere, along with w...
 8.2.8.211: Let X equal the fill weight in April and Y the fill weight in June ...
 8.2.8.212: Let X and Y denote the respective lengths of male and female green ...
 8.2.8.213: Students looked at the effect of a certain fertilizer on plant grow...
 8.2.8.214: An ecology laboratory studied tree dispersion patterns for the suga...
 8.2.8.215: Say X and Y are independent random variables with distributions tha...
 8.2.8.216: To measure air pollution in a home, let X and Y equal the amount of...
 8.2.8.217: Consider the distributions N(X , 400) and N(Y , 225). Let = X Y . S...
Solutions for Chapter 8.2: Tests of Statistical Hypotheses
Full solutions for Probability and Statistical Inference  9th Edition
ISBN: 9780321923271
Solutions for Chapter 8.2: Tests of Statistical Hypotheses
Get Full SolutionsChapter 8.2: Tests of Statistical Hypotheses includes 31 full stepbystep solutions. Since 31 problems in chapter 8.2: Tests of Statistical Hypotheses have been answered, more than 94164 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Probability and Statistical Inference was written by and is associated to the ISBN: 9780321923271. This textbook survival guide was created for the textbook: Probability and Statistical Inference , edition: 9.

aerror (or arisk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.

Bayesâ€™ theorem
An equation for a conditional probability such as PA B (  ) in terms of the reverse conditional probability PB A (  ).

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

Block
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

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).

Causeandeffect diagram
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

Central tendency
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous 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

Contingency table.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

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 .

Critical value(s)
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.

Dispersion
The amount of variability exhibited by data

Enumerative study
A study in which a sample from a population is used to make inference to the population. See Analytic study

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .