 32.1: Apply Chebyshevs theorem to the systolic blood pressure of normoten...
 32.2: At least how many of those men in the study fall within 2 standard ...
 32.3: Give ranges for the diastolic blood pressure (normotensive and hype...
 32.4: Do the normotensive, male, systolic blood pressure ranges overlap w...
 32.1: What is the relationship between the variance and the standard devi...
 32.2: Why might the range not be the best estimate of variability?
 32.3: What are the symbols used to represent the population variance and ...
 32.4: What are the symbols used to represent the sample variance and stan...
 32.5: Why is the unbiased estimator of variance used?
 32.6: The three data sets have the same mean and range, but is the variat...
 32.7: For Exercises 713, find the range, variance, and standard deviation...
 32.8: For Exercises 713, find the range, variance, and standard deviation...
 32.9: For Exercises 713, find the range, variance, and standard deviation...
 32.10: For Exercises 713, find the range, variance, and standard deviation...
 32.11: For Exercises 713, find the range, variance, and standard deviation...
 32.12: For Exercises 713, find the range, variance, and standard deviation...
 32.13: For Exercises 713, find the range, variance, and standard deviation...
 32.14: Home Runs Find the range, variance, and standard deviation for the ...
 32.15: Use the data for Exercise 11 in Section 31. Find the range, varianc...
 32.16: Deficient Bridges in U.S. States The Federal Highway Administration...
 32.17: Find the range, variance, and standard deviation for the data in Ex...
 32.18: Baseball Team Batting Averages Team batting averages for major leag...
 32.19: Cost per Load of Laundry Detergents The costs per load (in cents) o...
 32.20: Automotive Fuel Efficiency Thirty automobiles were tested for fuel ...
 32.21: Murders in Cities The data show the number of murders in 25 selecte...
 32.22: Reaction Times In a study of reaction times to a specific stimulus,...
 32.23: Lightbulb Lifetimes Eighty randomly selected lightbulbs were tested...
 32.24: Murder Rates The data represent the murder rate per 100,000 individ...
 32.25: Battery Lives Eighty randomly selected batteries were tested to det...
 32.26: Find the variance and standard deviation for the two distributions ...
 32.27: Word Processor Repairs This frequency distribution represents the d...
 32.28: Exam Scores The average score of the students in one calculus class...
 32.29: Suspension Bridges The lengths (in feet) of the main span of the lo...
 32.30: Exam Scores The average score on an English final examination was 8...
 32.31: Ages of Accountants The average age of the accountants at Three Riv...
 32.32: Using Chebyshevs theorem, solve these problems for a distribution w...
 32.33: The mean of a distribution is 20 and the standard deviation is 2. U...
 32.34: In a distribution of 160 values with a mean of 72, at least 120 fal...
 32.35: Calories The average number of calories in a regularsize bagel is 2...
 32.36: Time Spent Online Americans spend an average of 3 hours per day onl...
 32.37: Solid Waste Production The average college student produces 640 pou...
 32.38: Sale Price of Homes The average sale price of new onefamily houses...
 32.39: Trials to Learn a Maze The average of the number of trials it took ...
 32.40: Farm Sizes The average farm in the United States in 2004 contained ...
 32.41: Citrus Fruit Consumption The average U.S. yearly per capita consump...
 32.42: Work Hours for College Faculty The average fulltime faculty member...
 32.43: Serum Cholesterol Levels For this data set, find the mean and stand...
 32.44: Ages of Consumers For this data set, find the mean and standard dev...
 32.45: Using Chebyshevs theorem, complete the table to find the minimum pe...
 32.46: Use this data set: 10, 20, 30, 40, 50 a. Find the standard deviatio...
 32.47: The mean deviation is found by using this formula: where X value me...
 32.48: A measure to determine the skewness of a distribution is called the...
 32.49: All values of a data set must be within of the mean. If a person co...
Solutions for Chapter 32: Data Description
Full solutions for Elementary Statistics: A Step by Step Approach  7th Edition
ISBN: 9780073534978
Solutions for Chapter 32: Data Description
Get Full SolutionsElementary Statistics: A Step by Step Approach was written by and is associated to the ISBN: 9780073534978. Chapter 32: Data Description includes 53 full stepbystep solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. This expansive textbook survival guide covers the following chapters and their solutions. Since 53 problems in chapter 32: Data Description have been answered, more than 32754 students have viewed full stepbystep solutions from this chapter.

Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chisquare with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chisquare random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chisquare random variables.

Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

Backward elimination
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

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

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

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.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

Conditional mean
The mean of the conditional probability distribution of a random variable.

Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

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

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

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

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).

Decision interval
A parameter in a tabular CUSUM algorithm that is determined from a tradeoff between false alarms and the detection of assignable causes.

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

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

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

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 .