 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 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 13127 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.

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

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

Attribute
A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

Bivariate distribution
The joint probability distribution of two random variables.

Block
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

Coeficient of determination
See R 2 .

Convolution
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

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.

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

Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

Error variance
The variance of an error term or component in a model.

Expected value
The expected value of a random variable X is its longterm average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

Exponential random variable
A series of tests in which changes are made to the system under study

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.

False alarm
A signal from a control chart when no assignable causes are present

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.

Fraction defective control chart
See P chart

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function

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 .