 Chapter 15.15.1: Anemia. Hemoglobin is a protein in red blood cells that carries oxy...
 Chapter 15.15.2: Student attitudes. The Survey of Study Habits and Attitudes (SSHA) ...
 Chapter 15.15.3: Anemia. State the null and alternative hypotheses for the anemia st...
 Chapter 15.15.4: State the null and alternative hypotheses for the study ofolder stu...
 Chapter 15.15.5: Fuel economy. According to the Environmental Protection Agency (EPA...
 Chapter 15.15.6: Travel times to work. A labor specialist thinks that the mean trave...
 Chapter 15.15.7: Stating hypotheses. In planning a study of the birth weights of bab...
 Chapter 15.15.8: Sweetening colas. Figure 15.1 compares two possible results for the...
 Chapter 15.15.9: Anemia. What are the values of the test statistic z for the two out...
 Chapter 15.15.10: Student attitudes. What are the values of the test statistic z for ...
 Chapter 15.15.11: Pvalue automated. Go to the PValue of a Test of Significance appl...
 Chapter 15.15.12: Sweetening colas. Figure 15.1 shows that the outcome x = 0.3 from t...
 Chapter 15.15.13: Anemia. What are the Pvalues for the two outcomes of the anemia st...
 Chapter 15.15.14: Student attitudes. What are the Pvalues for the two outcomes of th...
 Chapter 15.15.15: Anemia. In Exercises 15.9 and 15.13, you found the z test statistic...
 Chapter 15.15.16: Student attitudes. In Exercises 15.10 and 15.14, you found the z te...
 Chapter 15.15.17: Protecting ultramarathon runners. Exercise 9.37 (page 232) describe...
 Chapter 15.15.18: Water quality. An environmentalist group collects a liter of water ...
 Chapter 15.15.19: Improving your SAT score. We suspect that on the average students w...
 Chapter 15.15.20: Reading a computer screen. Does the use of fancy type fonts slow do...
 Chapter 15.15.21: Significance. You are testing H0: = 0 against Ha: > 0 based on an S...
 Chapter 15.15.22: Significance. You are testing H0: = 0 against Ha: = 0 based on an S...
 Chapter 15.15.23: Testing a random number generator. A random number generator is sup...
 Chapter 15.15.24: Test and confidence interval. The Pvalue for a twosided test of t...
 Chapter 15.15.25: Confidence interval and test. A 95% confidence interval for a popul...
 Chapter 15.15.26: The mean score of adult men on a psychological test that measures m...
 Chapter 15.15.27: The researchers alternative hypothesis for the test in Exercise 15....
 Chapter 15.15.28: Suppose that scores of hotel managers on the psychological test of ...
 Chapter 15.15.29: If a z statistic has value z = 1.30, the twosided Pvalue is(a) 0....
 Chapter 15.15.30: If a z statistic has value z = 1.30, the twosided Pvalue is(a) 0....
 Chapter 15.15.31: If a z statistic has value z = 1.30 and Ha says that the population...
 Chapter 15.15.32: If a z statistic has value z = 1.30 and Ha says that the population...
 Chapter 15.15.33: If a z statistic has value z = 9.03, the twosided Pvalue is(a) ve...
 Chapter 15.15.34: You use software to do a test. The program tells you that the Pval...
 Chapter 15.15.35: A government report says that a 90% confidence interval for the mea...
 Chapter 15.15.36: Sulfur compounds cause offodors in wine, so winemakers want to kno...
 Chapter 15.15.37: IQ test scores. Exercise 14.6 (page 352) gives the IQ test scores o...
 Chapter 15.15.38: Hotel managers personalities. Successful hotel managers must have p...
 Chapter 15.15.39: Bone loss by nursing mothers. Exercise 14.25 (page 358) gives the p...
 Chapter 15.15.40: Sample size affects the Pvalue. In Example 15.6, a sample of n = 1...
 Chapter 15.15.41: Tests and confidence intervals. In Exercise 14.22 you found a confi...
 Chapter 15.15.42: The Supreme Court speaks. Court cases in such areas as employmentdi...
 Chapter 15.15.43: The wrong alternative. One of your friends is comparing movie ratin...
 Chapter 15.15.44: The wrong P. The report of a study of seat belt use by drivers says...
 Chapter 15.15.45: Tracking the placebo effect. The placebo effect is particularly str...
 Chapter 15.15.46: Fortified breakfast cereals. The Food and Drug Administration recom...
 Chapter 15.15.47: How to show that you are rich. Every society has its own marks of w...
 Chapter 15.15.48: Cicadas as fertilizer? Every 17 years, swarms of cicadas emerge fro...
 Chapter 15.15.49: Forests and windstorms. Does the destruction of large trees in a wi...
 Chapter 15.15.50: Diet and bowel cancer. It has long been thought that eating a healt...
 Chapter 15.15.51: 5% versus 1%. Sketch the standard Normal curve for the z test stati...
 Chapter 15.15.52: Is this what P means? When asked to explain the meaning of the Pva...
 Chapter 15.15.53: Is this what significance means? Another student, when asked why st...
 Chapter 15.15.54: 15.54 Pulling wood apart. In Exercise 14.26 (page 359), you found a...
 Chapter 15.15.55: Im a great freethrow shooter. The Reasoning of a Statistical Test ...
 Chapter 15.15.56: Significance at the 0.0125 level. The Normal Curve applet allows yo...
Solutions for Chapter Chapter 15: Tests of Significance: The Basics
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 15: Tests of Significance: The Basics
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. Chapter Chapter 15: Tests of Significance: The Basics includes 56 full stepbystep solutions. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Since 56 problems in chapter Chapter 15: Tests of Significance: The Basics have been answered, more than 7534 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions.

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).

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.

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

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).

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

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

Conidence coeficient
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

Conidence interval
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

Conidence level
Another term for the conidence coeficient.

Continuity correction.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

Cook’s distance
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

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

Critical region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

Exponential random variable
A series of tests in which changes are made to the system under study

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.

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

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

Harmonic mean
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .