 4.1.1E: What is a random variable? Give an example of a discrete random var...
 4.1.1: What is a random variable? Give an example of a discrete random var...
 4.1.2E: What is a discrete probability distribution? What are the two condi...
 4.1.2: What is a discrete probability distribution? What are the two condi...
 4.1.3E: Is the expected value of the probability distribution of a random v...
 4.1.3: Is the expected value of the probability distribution of a random v...
 4.1.4E: What does the mean of a probability distribution represent?
 4.1.4: What does the mean of a probability distribution represent?
 4.1.5E: True or False? In Exercise, determine whether the statement is true...
 4.1.5: True or False? In Exercises 58, determine whether the statement is ...
 4.1.6E: True or False? In Exercise, determine whether the statement is true...
 4.1.6: True or False? In Exercises 58, determine whether the statement is ...
 4.1.7E: True or False? In Exercise, determine whether the statement is true...
 4.1.7: True or False? In Exercises 58, determine whether the statement is ...
 4.1.8E: True or False? In Exercise, determine whether the statement is true...
 4.1.8: True or False? In Exercises 58, determine whether the statement is ...
 4.1.9E: Graphical Analysis In Exercise, determine whether the number line r...
 4.1.9: Graphical Analysis In Exercises 912, determine whether the number l...
 4.1.10E: Graphical Analysis In Exercise, determine whether the number line r...
 4.1.10: Graphical Analysis In Exercises 912, determine whether the number l...
 4.1.11E: Graphical Analysis In Exercise, determine whether the number line r...
 4.1.11: Graphical Analysis In Exercises 912, determine whether the number l...
 4.1.12E: Graphical Analysis In Exercise, determine whether the number line r...
 4.1.12: Graphical Analysis In Exercises 912, determine whether the number l...
 4.1.13E: Identifying Discrete and Continuous Random Variables In Exercise, d...
 4.1.13: Identifying Discrete and Continuous Random Variables In Exercises 1...
 4.1.14E: Identifying Discrete and Continuous Random Variables In Exercise, d...
 4.1.14: Identifying Discrete and Continuous Random Variables In Exercises 1...
 4.1.15E: Identifying Discrete and Continuous Random Variables In Exercise, d...
 4.1.15: Identifying Discrete and Continuous Random Variables In Exercises 1...
 4.1.16E: Identifying Discrete and Continuous Random Variables In Exercise, d...
 4.1.16: Identifying Discrete and Continuous Random Variables In Exercises 1...
 4.1.17E: Identifying Discrete and Continuous Random Variables In Exercise, d...
 4.1.17: Identifying Discrete and Continuous Random Variables In Exercises 1...
 4.1.18E: Identifying Discrete and Continuous Random Variables In Exercise, d...
 4.1.18: Identifying Discrete and Continuous Random Variables In Exercises 1...
 4.1.19E: Constructing and Graphing Discrete Probability Distributions In Exe...
 4.1.19: Constructing and Graphing Discrete Probability Distributions In Exe...
 4.1.20E: Constructing and Graphing Discrete Probability Distributions In Exe...
 4.1.20: Constructing and Graphing Discrete Probability Distributions In Exe...
 4.1.21E: Finding Probabilities Use the probability distribution you made in ...
 4.1.21: Finding Probabilities Use the probability distribution you made in ...
 4.1.22E: Finding Probabilities Use the probability distribution you made in ...
 4.1.22: Finding Probabilities Use the probability distribution you made in ...
 4.1.23E: Unusual Events In Exercise, would it be unusual for a household to ...
 4.1.23: Unusual Events In Exercise 19, would it be unusual for a household ...
 4.1.24E: Unusual Events In Exercise, would it be unusual for an employee to ...
 4.1.24: Unusual Events In Exercise 20, would it be unusual for an employee ...
 4.1.25E: Determining a Missing Probability In Exercise, determine the missin...
 4.1.25: Determining a Missing Probability In Exercises 25 and 26, determine...
 4.1.26E: Determining a Missing Probability In Exercise, determine the missin...
 4.1.26: Determining a Missing Probability In Exercises 25 and 26, determine...
 4.1.27E: Identifying Probability Distributions In Exercise, determine whethe...
 4.1.27: Identifying Probability Distributions In Exercises 27 and 28, deter...
 4.1.28E: Identifying Probability Distributions In Exercise, determine whethe...
 4.1.28: Identifying Probability Distributions In Exercises 27 and 28, deter...
 4.1.29E: Finding the Mean, Variance, and Standard Deviation In Exercise, (a)...
 4.1.29: Finding the Mean, Variance, and Standard Deviation In Exercises 293...
 4.1.30E: Finding the Mean, Variance, and Standard Deviation In Exercise, (a)...
 4.1.30: Finding the Mean, Variance, and Standard Deviation In Exercises 293...
 4.1.31E: Finding the Mean, Variance, and Standard Deviation In Exercise, (a)...
 4.1.31: Finding the Mean, Variance, and Standard Deviation In Exercises 293...
 4.1.32E: Finding the Mean, Variance, and Standard Deviation In Exercise, (a)...
 4.1.32: Finding the Mean, Variance, and Standard Deviation In Exercises 293...
 4.1.33E: Finding the Mean, Variance, and Standard Deviation In Exercise, (a)...
 4.1.33: Finding the Mean, Variance, and Standard Deviation In Exercises 293...
 4.1.34E: Finding the Mean, Variance, and Standard Deviation In Exercise, (a)...
 4.1.34: Finding the Mean, Variance, and Standard Deviation In Exercises 293...
 4.1.35E: Writing The expected value of an accountant’s profit and loss analy...
 4.1.35: Writing The expected value of an accountants profit and loss analys...
 4.1.36E: Writing In a game of chance, what is the relationship between a “fa...
 4.1.36: Writing In a game of chance, what is the relationship between a fai...
 4.1.37E: Finding Expected Value In Exercise, find the expected net gain to t...
 4.1.37: Finding Expected Value In Exercises 37 and 38, find the expected ne...
 4.1.38E: Finding Expected Value In Exercise, find the expected net gain to t...
 4.1.38: Finding Expected Value In Exercises 37 and 38, find the expected ne...
 4.1.39EC: Linear Transformation of a Random Variable In Exercise, use the fol...
 4.1.39: inear Transformation of a Random Variable In Exercises 39 and 40, u...
 4.1.40EC: Linear Transformation of a Random Variable In Exercise, use the fol...
 4.1.40: inear Transformation of a Random Variable In Exercises 39 and 40, u...
 4.1.41EC: Independent and Dependent Random Variables Two random variables x a...
 4.1.41: What is the average sum of their scores? What is the average differ...
 4.1.42EC: Independent and Dependent Random Variables Two random variables x a...
 4.1.42: What is the standard deviation of the difference of their scores?
Solutions for Chapter 4.1: Probability Distributions
Full solutions for Elementary Statistics: Picturing the World  6th Edition
ISBN: 9780321911216
Solutions for Chapter 4.1: Probability Distributions
Get Full SolutionsSince 84 problems in chapter 4.1: Probability Distributions have been answered, more than 99703 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 4.1: Probability Distributions includes 84 full stepbystep solutions. This textbook survival guide was created for the textbook: Elementary Statistics: Picturing the World , edition: 6. Elementary Statistics: Picturing the World was written by and is associated to the ISBN: 9780321911216.

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

Addition rule
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).

All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

Bivariate normal distribution
The joint distribution of two normal random variables

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

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.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare 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

Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

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.

Control limits
See Control chart.

Cook’s distance
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

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.

Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

Eficiency
A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

Enumerative study
A study in which a sample from a population is used to make inference to the population. See Analytic study

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

Ftest
Any test of signiicance involving the F distribution. The most common Ftests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

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.