 13.6.1 BSC: RegressionIf the methods of this section are used with paired sampl...
 13.6.2 BSC: Level of MeasurementWhich of the levels of measurement (nominal, or...
 13.6.3 BSC: Notation What do r, rs, p, and ps denote? Why is the subscript s us...
 13.6.4 BSC: EfficiencyRefer to Table 132 in Section 131 and identify the effi...
 13.6.5 BSC: use the scatterplot to find the value of the rank correlation coeff...
 13.6.6 BSC: use the scatterplot to find the value of the rank correlation coeff...
 13.6.7 BSC: use the rank correlation coefficient to test for a correlation betw...
 13.6.8 BSC: use the rank correlation coefficient to test for a correlation betw...
 13.6.9 BSC: Judges of Marching BandsTwo judges ranked seven bands in the Texas ...
 13.6.10 BSC: Judges of Marching BandsIn the same competition described in Exerci...
 13.6.11 BSC: Measuring Seals from PhotosListed below are the overhead widths (in...
 13.6.12 BSC: Crickets and TemperatureThe association between the temperature and...
 13.6.15 BSC: use the data from Appendix B to test for rank correlation with a 0....
 13.6.16 BSC: use the data from Appendix B to test for rank correlation with a 0....
 13.6.17 BB: Finding Critical ValuesAn alternative to using Table A9 to find cr...
 13.6.1BSC: Regression?If the methods of this section are used with paired samp...
 13.6.2BSC: Level of Measurement?Which of the levels of measurement (nominal, o...
Solutions for Chapter 13.6: Elementary Statistics 12th Edition
Full solutions for Elementary Statistics  12th Edition
ISBN: 9780321836960
Solutions for Chapter 13.6
Get Full SolutionsThis expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Elementary Statistics, edition: 12. Elementary Statistics was written by and is associated to the ISBN: 9780321836960. Chapter 13.6 includes 17 full stepbystep solutions. Since 17 problems in chapter 13.6 have been answered, more than 217739 students have viewed full stepbystep solutions from this chapter.

Average
See Arithmetic mean.

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.

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

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

Bimodal distribution.
A distribution with two modes

Bivariate distribution
The joint probability distribution of two random variables.

Bivariate normal distribution
The joint distribution of two normal random variables

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

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.

Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

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 level
Another term for the conidence coeficient.

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

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.

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

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

Critical value(s)
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

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

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 .