 Chapter 14.14.1: More on NAEP test scores. Suppose that you give the NAEP test to an...
 Chapter 14.14.2: A Gallup Poll found that 51% of the people in its sample saidYes wh...
 Chapter 14.14.3: Confidence intervals in action. The idea of an 80% confidence inter...
 Chapter 14.14.4: Find a critical value. The critical value z for confidence level 97...
 Chapter 14.14.5: A manufacturer of pharmaceutical products analyzes each batch of a ...
 Chapter 14.14.6: Here are the IQ test scores of 31 seventhgrade girls in aMidwest s...
 Chapter 14.14.7: Sample size and margin of error. High school students who take the ...
 Chapter 14.14.8: Confidence level and margin of error. A random sample of 1000 high ...
 Chapter 14.14.9: Improving SAT scores. How large a sample of high school students in...
 Chapter 14.14.10: Estimating mean IQ. How large a sample of schoolgirls in Exercise 1...
 Chapter 14.14.11: To give a 98% confidence interval for a population mean , you would...
 Chapter 14.14.12: An opinion poll says that the result of their latest sample has a m...
 Chapter 14.14.13: A laboratory scale is known to have a standard deviation of = 0.001...
 Chapter 14.14.14: Three weighings of a specimen on the scale described in Exercise 14...
 Chapter 14.14.15: Another specimen is weighed 8 times on the same scale as in the pre...
 Chapter 14.14.16: How many times must you weigh a specimen on the scale in Exercise 1...
 Chapter 14.14.17: A government report says that a 90% confidence interval for the mea...
 Chapter 14.14.18: Suppose that the survey in the previous exercise had obtained the r...
 Chapter 14.14.19: Suppose that the report in Exercise 14.17 had used 95% confidence r...
 Chapter 14.14.20: A Gallup Poll asked 1060 randomly selected adults, How would you ra...
 Chapter 14.14.21: Hotel managers personalities. Successful hotel managers must havepe...
 Chapter 14.14.22: 14.22 More about hotel managers. The hotel managers described in th...
 Chapter 14.14.23: Length of a confidence interval. Your confidence interval in Exerci...
 Chapter 14.14.24: How large a sample? You would be satisfied to estimate the BSRImasc...
 Chapter 14.14.25: Bone loss by nursing mothers. Breastfeeding mothers secrete calciu...
 Chapter 14.14.26: Pulling wood apart. How heavy a load (pounds) is needed to pull apa...
 Chapter 14.14.27: This wine stinks. Sulfur compounds cause offodors in wine, so wine...
 Chapter 14.14.28: Pulling wood apart, continued. You want to estimate the mean load n...
 Chapter 14.14.29: Engine crankshafts. Here are measurements (in millimeters) of a cri...
 Chapter 14.14.30: Student study times. A class survey in a large class for firstyear...
 Chapter 14.14.31: A big toe deformity. Table 7.2 (page 177) gives data on 38 consecut...
 Chapter 14.14.32: An outlier strikes. There were actually 270 responses to the class ...
 Chapter 14.14.33: Calibrating a scale. To assess the accuracy of a laboratory scale, ...
 Chapter 14.14.34: Explaining confidence. A student reads that a 95% confidence interv...
 Chapter 14.14.35: Explaining confidence. Here is an explanation from the Associated P...
 Chapter 14.14.36: Crime. A Gallup Poll of 1002 adults found that 25% of the responden...
 Chapter 14.14.37: A newspaper poll. A New York Times poll on womens issues interviewe...
 Chapter 14.14.38: What confidence means. Confidence tells us how often our method wil...
 Chapter 14.14.39: An interactive table of critical values. The bottom row of Table C ...
Solutions for Chapter Chapter 14: Confidence Intervals: The Basics
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 14: Confidence Intervals: The Basics
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. Chapter Chapter 14: Confidence Intervals: The Basics includes 39 full stepbystep solutions. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Since 39 problems in chapter Chapter 14: Confidence Intervals: The Basics have been answered, more than 7732 students have viewed full stepbystep solutions from this chapter.

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

Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

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.

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

Average run length, or ARL
The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

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

Bivariate normal distribution
The joint distribution of two normal random variables

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

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.

Coeficient of determination
See R 2 .

Control limits
See Control chart.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

Correlation
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

Correlation matrix
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the offdiagonal elements rij are the correlations between Xi and Xj .

Decision interval
A parameter in a tabular CUSUM algorithm that is determined from a tradeoff between false alarms and the detection of assignable causes.

Exhaustive
A property of a collection of events that indicates that their union equals the sample space.

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.

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

Gaussian distribution
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

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 .