 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 Patricia 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 6415 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.

Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.

Bayes’ theorem
An equation for a conditional probability such as PA B (  ) in terms of the reverse conditional probability PB A (  ).

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

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.

Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

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 level
Another term for the conidence coeficient.

Critical region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

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.

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

Defect
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

Density function
Another name for a probability density function

Discrete random variable
A random variable with a inite (or countably ininite) range.

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

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

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.

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

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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