- 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 13-2 in Section 13-1 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 A-9 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
See Arithmetic mean.
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.
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.
A distribution with two modes
The joint probability distribution of two random variables.
Bivariate normal distribution
The joint distribution of two normal random variables
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable
Central composite design (CCD)
A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level 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 second-order 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.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
Another term for the conidence coeficient.
A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.
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.
Formulas used to determine the number of elements in sample spaces and events.
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 off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.
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.
A property of a collection of events that indicates that their union equals the sample space.
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 .