 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 11073 students have viewed full stepbystep solutions from this chapter.

2 k p  factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chisquare with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chisquare random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chisquare random variables.

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.

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

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

Bivariate normal distribution
The joint distribution of two normal random variables

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

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

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

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 .

Defect
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

Deming
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

Error mean square
The error sum of squares divided by its number of degrees of freedom.

Error propagation
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

Error variance
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.

Event
A subset of a sample space.

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.