 1.3.79: Quiz grades Joeys first 14 quiz grades in a marking period were 86 ...
 1.3.80: Cowboys The 2011 roster of the Dallas Cowboys professional football...
 1.3.81: Quiz grades Refer to Exercise 79. (a) Find the median by hand. Show...
 1.3.82: Cowboys Refer to Exercise 80. (a) Find the median by hand. Show you...
 1.3.83: Incomes of college grads According to the Census Bureau, the mean a...
 1.3.84: House prices The mean and median selling prices of existing single...
 1.3.85: Baseball salaries Suppose that a Major League Baseball teams mean y...
 1.3.86: Mean salary? Last year a small accounting firm paid each of its fiv...
 1.3.87: Domain names When it comes to Internet domain names, is shorter bet...
 1.3.88: Do adolescent girls eat fruit? We all know that fruit is good for u...
 1.3.89: Quiz grades Refer to Exercise 79. (a) Find and interpret the interq...
 1.3.90: Cowboys Refer to Exercise 80. (a) Find and interpret the interquart...
 1.3.91: Dont call me In a September 28, 2008, article titled Letting Our Fi...
 1.3.92: Acing the first test Here are the scores of Mrs. Liaos students on ...
 1.3.93: Texts or calls? Refer to Exercise 91. A boxplot of the difference (...
 1.3.94: Electoral votes To become president of the United States, a candida...
 1.3.95: Comparing investments Should you put your money into a fund that bu...
 1.3.96: Income and education level Each March, the Bureau of Labor Statisti...
 1.3.97: Phosphate levels The level of various substances in the blood influ...
 1.3.98: Feeling sleepy? The first four students to arrive for a firstperio...
 1.3.99: Shopping spree The figure displays computer output for data on the ...
 1.3.100: Csections Do male doctors perform more cesarean sections (Csectio...
 1.3.101: The IQR Is the interquartile range a resistant measure of spread? G...
 1.3.102: What do they measure? For each of the following summary statistics,...
 1.3.103: SD contest This is a standard deviation contest. You must choose fo...
 1.3.104: Measuring spread Which of the distributions shown has a larger stan...
 1.3.105: SSHA scores Here are the scores on the Survey of Study Habits and A...
 1.3.106: Hummingbirds and tropical flower Researchers from Amherst College s...
 1.3.107: Multiple choice: Select the best answer for Exercises 107 to 110. I...
 1.3.108: Multiple choice: Select the best answer for Exercises 107 to 110. T...
 1.3.109: Multiple choice: Select the best answer for Exercises 107 to 110. T...
 1.3.110: Multiple choice: Select the best answer for Exercises 107 to 110. W...
 1.3.111: Exercises 111 and 112 refer to the following setting. We used Censu...
 1.3.112: Exercises 111 and 112 refer to the following setting. We used Censu...
 1.3.113: Success in college (1.1) The 2007 Freshman Survey asked firstyear ...
Solutions for Chapter 1.3: Describing Quantitative Data with Numbers
Full solutions for The Practice of Statistics  5th Edition
ISBN: 9781464108730
Solutions for Chapter 1.3: Describing Quantitative Data with Numbers
Get Full SolutionsThe Practice of Statistics was written by and is associated to the ISBN: 9781464108730. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 1.3: Describing Quantitative Data with Numbers includes 35 full stepbystep solutions. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 5. Since 35 problems in chapter 1.3: Describing Quantitative Data with Numbers have been answered, more than 8360 students have viewed full stepbystep solutions from this chapter.

2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

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).

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).

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.

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 mean
The mean of the conditional probability distribution of a 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

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

Control limits
See Control chart.

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

Covariance
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

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.

Defectsperunit control chart
See U chart

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.

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.

Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

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 .
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