- 7.1: Critical Thinking Let x be a random variable representing the amoun...
- 7.2: Critical Thinking If x has a normal distribution with mean m 15 and...
- 7.3: Job Interview: Length The personnel office at a large electronics f...
- 7.4: Drugs: Effects A new muscle relaxant is available. Researchers from...
- 7.5: Psychology: IQ Scores Assume that IQ scores are normally distribute...
- 7.6: Hatchery Fish: Length A large tank of fish from a hatchery is being...
- 7.7: Whos Who: Misinformation About 11% of Americans believe that Joan o...
- 7.8: Grand Canyon: Boating Accidents Thomas Myers is a staff physician a...
- 7.9: Critical Thinking Suppose we have a binomial distribution with n tr...
Solutions for Chapter 7: INTRODUCTION TO SAMPLING DISTRIBUTIONS
Full solutions for Understandable Statistics | 9th Edition
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test
Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation
A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
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
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the in-control 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 in-control, or free from assignable causes. Points beyond the control limits indicate an out-of-control process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.
A subset of effects in a fractional factorial design that deine the aliases in the design.
Discrete random variable
A random variable with a inite (or countably ininite) range.
A study in which a sample from a population is used to make inference to the population. See Analytic study
The variance of an error term or component in a model.
Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.
The expected value of a random variable X is its long-term average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.
A function used in the probability density function of a gamma random variable that can be considered to extend factorials
Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r