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# Solutions for Chapter Chapter 6: Discrete Probability Distributions

## Full solutions for Statistical Techniques in Business and Economics | 15th Edition

ISBN: 9780073401805

Solutions for Chapter Chapter 6: Discrete Probability Distributions

Solutions for Chapter Chapter 6
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##### ISBN: 9780073401805

This textbook survival guide was created for the textbook: Statistical Techniques in Business and Economics, edition: 15. This expansive textbook survival guide covers the following chapters and their solutions. Statistical Techniques in Business and Economics was written by and is associated to the ISBN: 9780073401805. Since 71 problems in chapter Chapter 6: Discrete Probability Distributions have been answered, more than 32740 students have viewed full step-by-step solutions from this chapter. Chapter Chapter 6: Discrete Probability Distributions includes 71 full step-by-step solutions.

Key Statistics Terms and definitions covered in this textbook
• 2 k factorial experiment.

A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

• a-error (or a-risk)

In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

• 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

• Analysis of variance (ANOVA)

A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

• Analytic study

A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

• Causal variable

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

• Cause-and-effect diagram

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

• Chi-square test

Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

• Conditional probability

The probability of an event given that the random experiment produces an outcome in another event.

• Continuity correction.

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

• Contour plot

A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

• 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 off-diagonal elements rij are the correlations between Xi and Xj .

• 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 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.

• Crossed factors

Another name for factors that are arranged in a factorial experiment.

• Defect concentration diagram

A quality tool that graphically shows the location of defects on a part or in a process.

• Error of estimation

The difference between an estimated value and the true value.

• 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.

• Generating function

A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function

• 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 .

• Goodness of fit

In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

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