 51.1: Define and give three examples of a random variable. A random varia...
 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: The speed of a jet airplane Continuous
 51.13: The number of cheeseburgers a fastfood restaurant serves each day ...
 51.14: The number of people who play the state lottery each day Discrete 3...
 51.15: The weight of an automobile. Continuous
 51.16: The time it takes to have a medical physical exam. Continuous
 51.17: The number of mathematics majors in your school Discrete
 51.18: The blood pressures of all patients admitted to a hospital on a spe...
 51.19: Medical Tests The probabilities that a patient will have 0, 1, 2, o...
 51.20: Investment Return The probabilities of a return on an investment of...
 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: Probability Distributions
Full solutions for Elementary Statistics: A Step by Step Approach 8th ed.  8th Edition
ISBN: 9780073386102
Solutions for Chapter 51: Probability Distributions
Get Full SolutionsThis expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach 8th ed., edition: 8. Chapter 51: Probability Distributions includes 36 full stepbystep solutions. Since 36 problems in chapter 51: Probability Distributions have been answered, more than 11766 students have viewed full stepbystep solutions from this chapter. Elementary Statistics: A Step by Step Approach 8th ed. was written by Patricia and is associated to the ISBN: 9780073386102.

`error (or `risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I 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

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

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

Correlation coeficient
A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

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.

Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Deming
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

Design matrix
A matrix that provides the tests that are to be conducted in an experiment.

Discrete distribution
A probability distribution for a discrete random variable

Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.

Empirical model
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

Event
A subset of a sample space.

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

Fisher’s least signiicant difference (LSD) method
A series of pairwise hypothesis tests of treatment means in an experiment to determine which means differ.

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.
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