 82.1: What hypotheses would you use?
 82.2: Is the sample considered small or large?
 82.3: What assumption must be met before the hypothesis test can be condu...
 82.4: Which probability distribution would you use?
 82.5: Would you select a one or twotailed test? Why?
 82.6: What critical value(s) would you use?
 82.7: Conduct a hypothesis test. Use s 30.3.
 82.8: What is your decision?
 82.9: What is your conclusion?
 82.10: Write a brief statement summarizing your conclusion.
 82.11: If you lived in a city whose population was about 50,000, how many ...
 82.1: For Exercises 1 through 13, perform each of the following steps. a....
 82.2: For Exercises 1 through 13, perform each of the following steps. a....
 82.3: For Exercises 1 through 13, perform each of the following steps. a....
 82.4: For Exercises 1 through 13, perform each of the following steps. a....
 82.5: For Exercises 1 through 13, perform each of the following steps. a....
 82.6: For Exercises 1 through 13, perform each of the following steps. a....
 82.7: For Exercises 1 through 13, perform each of the following steps. a....
 82.8: For Exercises 1 through 13, perform each of the following steps. a....
 82.9: For Exercises 1 through 13, perform each of the following steps. a....
 82.10: For Exercises 1 through 13, perform each of the following steps. a....
 82.11: For Exercises 1 through 13, perform each of the following steps. a....
 82.12: For Exercises 1 through 13, perform each of the following steps. a....
 82.13: For Exercises 1 through 13, perform each of the following steps. a....
 82.14: What is meant by a Pvalue?
 82.15: State whether the null hypothesis should be rejected on the basis o...
 82.16: Soft Drink Consumption A researcher claims that the yearly consumpt...
 82.17: Stopping Distances A study found that the average stopping distance...
 82.18: Copy Machine Use A store manager hypothesizes that the average numb...
 82.19: Burning Calories by Playing Tennis A health researcher read that a ...
 82.20: Breaking Strength of Cable A special cable has a breaking strength ...
 82.21: Farm Sizes The average farm size in the United States is 444 acres....
 82.22: Farm Sizes Ten years ago, the average acreage of farms in a certain...
 82.23: Transmission Service A car dealer recommends that transmissions be ...
 82.24: Speeding Tickets A motorist claims that the South Boro Police issue...
 82.25: Sick Days A manager states that in his factory, the average number ...
 82.26: Suppose a statistician chose to test a hypothesis at a 0.01. The cr...
 82.27: Hourly Wage The president of a company states that the average hour...
Solutions for Chapter 82: Hypothesis Testing
Full solutions for Elementary Statistics: A Step by Step Approach  7th Edition
ISBN: 9780073534978
Solutions for Chapter 82: Hypothesis Testing
Get Full SolutionsThis expansive textbook survival guide covers the following chapters and their solutions. Chapter 82: Hypothesis Testing includes 38 full stepbystep solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. Elementary Statistics: A Step by Step Approach was written by and is associated to the ISBN: 9780073534978. Since 38 problems in chapter 82: Hypothesis Testing have been answered, more than 35895 students have viewed full stepbystep solutions from this chapter.

`error (or `risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).

Acceptance region
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

All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

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

Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

Bayes’ estimator
An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

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).

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

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

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

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

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

Contour plot
A twodimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Density function
Another name for a probability density function

Dispersion
The amount of variability exhibited by data

Expected value
The expected value of a random variable X is its longterm 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.

False alarm
A signal from a control chart when no assignable causes are present

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.