 4.3.1: Statistical Literacy What is the main difference between a situatio...
 4.3.2: Statistical Literacy Consider a series of events. How does a tree d...
 4.3.3: Critical Thinking For each of the following situations, explain why...
 4.3.4: Critical Thinking You need to know the number of different arrangem...
 4.3.5: Tree Diagram (a) Draw a tree diagram to display all the possible he...
 4.3.6: Tree Diagram (a) Draw a tree diagram to display all the possible ou...
 4.3.7: Tree Diagram There are six balls in an urn. They are identical exce...
 4.3.8: Tree Diagram (a) Make a tree diagram to show all the possible seque...
 4.3.9: Multiplication Rule Four wires (red, green, blue, and yellow) need ...
 4.3.10: Multiplication Rule A sales representative must visit four cities: ...
 4.3.11: Counting: Agriculture Barbara is a research biologist for Green Car...
 4.3.12: Counting: Outcomes You toss a pair of dice. (a) Determine the numbe...
 4.3.13: Compute P5,2.
 4.3.14: Compute P8,3
 4.3.15: Compute P7,7.
 4.3.16: Compute P9,9.
 4.3.17: Compute C5,2.
 4.3.18: Compute C8,3
 4.3.19: Compute C7,7.
 4.3.20: Compute C8,8
 4.3.21: Counting: Hiring There are three nursing positions to be filled at ...
 4.3.22: Counting: Lottery In the Cash Now lottery game there are 10 finalis...
 4.3.23: Counting: Sports The University of Montana ski team has five entran...
 4.3.24: Counting: Sales During the Computer Daze special promotion, a custo...
 4.3.25: Counting: Hiring There are 15 qualified applicants for 5 trainee po...
 4.3.26: Counting: Grading One professor grades homework by randomly choosin...
 4.3.27: Counting: Hiring The qualified applicant pool for six management tr...
Solutions for Chapter 4.3: Elementary Probability Theory
Full solutions for Understandable Statistics  9th Edition
ISBN: 9780618949922
Solutions for Chapter 4.3: Elementary Probability Theory
Get Full SolutionsUnderstandable Statistics was written by and is associated to the ISBN: 9780618949922. This textbook survival guide was created for the textbook: Understandable Statistics, edition: 9. Since 27 problems in chapter 4.3: Elementary Probability Theory have been answered, more than 33842 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 4.3: Elementary Probability Theory includes 27 full stepbystep solutions.

Alternative hypothesis
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

Bayes’ estimator
An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

Bivariate normal distribution
The joint distribution of two normal random variables

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete 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.

Consistent estimator
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

Contingency table.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

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

Correlation
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

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

Discrete distribution
A probability distribution for a discrete random variable

Estimate (or point estimate)
The numerical value of a point estimator.

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

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

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

Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

Gaussian distribution
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

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 .