 51.1: Define and give three examples of a random variable.
 51.2: Explain the difference between a discrete and a continuous random v...
 51.3: Give three examples of a discrete random variable.
 51.4: Give three examples of a continuous random variable.
 51.5: What is a probability distribution? Give an example.
 51.6: For Exercises 6 through 11, determine whether the distribution repr...
 51.7: For Exercises 6 through 11, determine whether the distribution repr...
 51.8: For Exercises 6 through 11, determine whether the distribution repr...
 51.9: For Exercises 6 through 11, determine whether the distribution repr...
 51.10: For Exercises 6 through 11, determine whether the distribution repr...
 51.11: For Exercises 6 through 11, determine whether the distribution repr...
 51.12: For Exercises 12 through 18, state whether the variable is discrete...
 51.13: For Exercises 12 through 18, state whether the variable is discrete...
 51.14: For Exercises 12 through 18, state whether the variable is discrete...
 51.15: For Exercises 12 through 18, state whether the variable is discrete...
 51.16: For Exercises 12 through 18, state whether the variable is discrete...
 51.17: For Exercises 12 through 18, state whether the variable is discrete...
 51.18: For Exercises 12 through 18, state whether the variable is discrete...
 51.19: Medical Tests The probabilities that a patient will have 0, 1, 2, o...
 51.20: Student Volunteers The probabilities that a student volunteer hosts...
 51.21: Birthday Cake Sales The probabilities that a bakery has a demand fo...
 51.22: DVD Rentals The probabilities that a customer will rent 0, 1, 2, 3,...
 51.23: Loaded Die A die is loaded in such a way that the probabilities of ...
 51.24: Item Selection The probabilities that a customer selects 1, 2, 3, 4...
 51.25: Student Classes The probabilities that a student is registered for ...
 51.26: Garage Space The probabilities that a randomly selected home has ga...
 51.27: Selecting a Monetary Bill A box contains three $1 bills, two $5 bil...
 51.28: Family with Children Construct a probability distribution for a fam...
 51.29: Drawing a Card Construct a probability distribution for drawing a c...
 51.30: Rolling Two Dice Using the sample space for tossing two dice, const...
 51.31: For Exercises 31 through 36, write the distribution for the formula...
 51.32: For Exercises 31 through 36, write the distribution for the formula...
 51.33: For Exercises 31 through 36, write the distribution for the formula...
 51.34: For Exercises 31 through 36, write the distribution for the formula...
 51.35: For Exercises 31 through 36, write the distribution for the formula...
 51.36: For Exercises 31 through 36, write the distribution for the formula...
Solutions for Chapter 51: Discrete Probability Distributions
Full solutions for Elementary Statistics: A Step by Step Approach  7th Edition
ISBN: 9780073534978
Solutions for Chapter 51: Discrete Probability Distributions
Get Full SolutionsElementary Statistics: A Step by Step Approach was written by Patricia and is associated to the ISBN: 9780073534978. This expansive textbook survival guide covers the following chapters and their solutions. Since 36 problems in chapter 51: Discrete Probability Distributions have been answered, more than 5995 students have viewed full stepbystep solutions from this chapter. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. Chapter 51: Discrete Probability Distributions includes 36 full stepbystep solutions.

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

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

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

Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

Bivariate normal distribution
The joint distribution of two normal random variables

C chart
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defectsperunit or U chart.

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

Comparative experiment
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

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 variance.
The variance of the conditional probability distribution of a random variable.

Conidence coeficient
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.

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

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

Dependent variable
The response variable in regression or a designed experiment.

Event
A subset of a sample space.

Factorial experiment
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.

Harmonic mean
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .
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