 Chapter 26.26.1: (a) Arrange the 12 observations in order and find the ranks.(b) Tak...
 Chapter 26.26.2: (a) Make a graph to describe the data. What does it show?(b) The DD...
 Chapter 26.26.3: Attracting beetles, continued. In Exercise 26.1, you found the Wilc...
 Chapter 26.26.4: DDT poisoning, continued. Use your values of W, W, and W from Exerc...
 Chapter 26.26.5: A study of early childhood education asked kindergarten students to...
 Chapter 26.26.6: Attracting beetles: software. Use your software to carry out the on...
 Chapter 26.26.7: DDT poisoning: software. Use your software to repeat the Wilcoxon t...
 Chapter 26.26.8: (a) Is there evidence that 9 weeds per meter reduces corn yields wh...
 Chapter 26.26.9: We could use either twosample t or theWilcoxon rank sum to test th...
 Chapter 26.26.10: DDT poisoning: hypotheses. We are interested in whether DDT changes...
 Chapter 26.26.11: (a) What are the null and alternative hypotheses for the Wilcoxon t...
 Chapter 26.26.12: Reducing wrinkles. Durable press treatment of fabrics reduces wrink...
 Chapter 26.26.13: The null hypothesis is no difference in timing; the alternative hyp...
 Chapter 26.26.14: (a) Make a backtoback stemplot of the 5 responses in each group. ...
 Chapter 26.26.15: Cicadas as fertilizer? Exercise 7.9 (text page 173) gives data from...
 Chapter 26.26.16: Food safety in restaurants. Example 26.5 describes a study of the a...
 Chapter 26.26.17: More on food safety. The data file used in Example 26.5 and Exercis...
 Chapter 26.26.18: The investigators used the matched pairs t test. With only 3 pairs,...
 Chapter 26.26.19: The matched pairs t test (Example 18.4) works well, and gives P = 0...
 Chapter 26.26.20: Growing trees faster: Normal approximation. Continue your work from...
 Chapter 26.26.21: W+ versus t. Find the onesided Pvalue for the matched pairs t tes...
 Chapter 26.26.22: Floral scents and learning: Normal approximation. Use the Normalapp...
 Chapter 26.26.23: (a) Graph the data, and comment on skewness and outliers. A rank te...
 Chapter 26.26.24: (a) There are several ties among the absolute differences. Find the...
 Chapter 26.26.25: (a) These data are the differences from a matched pairs design. Sta...
 Chapter 26.26.26: Mutual funds often compare their performance with a benchmark provi...
 Chapter 26.26.27: Fungus in the air. The air in poultry processing plants often conta...
 Chapter 26.26.28: Which color attracts beetles best? Example 25.4 (text page 634) use...
 Chapter 26.26.29: (a) Make a graph to compare the distributions of richness for the t...
 Chapter 26.26.30: Does polyester decay? Here are the breaking strengths (in pounds) o...
 Chapter 26.26.31: Compressing soil. Farmers know that driving heavy equipment on wet ...
 Chapter 26.26.32: Food safety. Example 26.5 describes a study of the attitudes of peo...
 Chapter 26.26.33: A study of road rage gives randomly selected drivers a test that me...
 Chapter 26.26.34: You interview college students who have done community service and ...
 Chapter 26.26.35: You interview 75 students in their freshman year and again in their...
 Chapter 26.26.36: When some plants are attacked by leafeating insects, they release ...
 Chapter 26.26.37: If there is no difference in emissions between the attacked group a...
 Chapter 26.26.38: Suppose that the 12 observations in Exercise 26.36 wereControl grou...
 Chapter 26.26.39: Interview 10 young married couples, wife and husband separately. On...
 Chapter 26.26.40: If husbands and wives dont differ in how important the attractivene...
 Chapter 26.26.41: Suppose that the responses in Exercise 26.39 areCouple1 2 3 4 5 6 7...
 Chapter 26.26.42: You compare the incomes of 4 college freshmen, 5 sophomores, 6 juni...
 Chapter 26.26.43: Table 19.1 (text page 488) gives thepretest and posttest scores for...
 Chapter 26.26.44: Which blue is most blue? The color of a fabric depends on the dye u...
 Chapter 26.26.45: Right versus left. Table 18.5 (text page 458) contains data from a ...
 Chapter 26.26.46: Logging in the rain forest. Investigators compared the number of tr...
 Chapter 26.26.47: Food safety at fairs and restaurants. Example 26.5 describes a stud...
 Chapter 26.26.48: Food safety at fairs and fastfood restaurants. The food safety sur...
 Chapter 26.26.49: Nematodes and plant growth. A botanist prepares 16 identical planti...
 Chapter 26.26.50: We identified a significant positive effect oftributaries on Amazon...
 Chapter 26.26.51: Tributary versus upstream. Second, we found that species richness w...
 Chapter 26.26.52: Tributary versus downstream. Species richness was comparable betwee...
Solutions for Chapter Chapter 26: Nonparametric Tests
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 26: Nonparametric Tests
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Chapter Chapter 26: Nonparametric Tests includes 52 full stepbystep solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 52 problems in chapter Chapter 26: Nonparametric Tests have been answered, more than 7627 students have viewed full stepbystep solutions from this chapter.

Addition rule
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).

Alternative hypothesis
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

Attribute
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.

Backward elimination
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

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

Biased estimator
Unbiased estimator.

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

Coeficient of determination
See R 2 .

Conidence level
Another term for the conidence coeficient.

Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.

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.

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

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).

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.

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.

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

Frequency distribution
An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

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 .