 6.1: Shipments. A company selling clothing on the Internetreports that t...
 6.2: Hotline. A companys customer service hotline handlesmany calls rela...
 6.3: Payroll. Here are the summary statistics for the weeklypayroll of a...
 6.4: Hams. A specialty foods company sells gourmethams by mail order. Th...
 6.5: SAT or ACT? Each year thousands of high school students take eithe...
 6.6: Cold U? A high school senior uses the Internet to get information ...
 6.7: Stats test. Suppose your Statistics professor reportstest grades as...
 6.8: Checkup. One of the authors has an adopted grandsonwhose birth fami...
 6.9: Stats test, part II. The mean score on the Stats examwas 75 points ...
 6.10: Mensa. People with zscores above 2.5 on an IQ test aresometimes cl...
 6.11: Temperatures. A towns January high temperaturesaverage with a stand...
 6.12: Placement exams. An incoming freshman took hercolleges placement ex...
 6.13: Combining test scores. The first Stats exam had amean of 65 and a s...
 6.14: Combining scores again. The first Stat exam had amean of 80 and a s...
 6.15: Final exams. Anna, a language major, took final examsin both French...
 6.16: MP3s. Two companies market new batteries targeted atowners of perso...
 6.17: Cattle. The Virginia Cooperative Extension reports thatthe mean wei...
 6.18: Car speeds. John Beale of Stanford, CA, recorded thespeeds of cars ...
 6.19: More cattle. Recall that the beef cattle described inExercise 17 ha...
 6.20: Car speeds again. For the car speed data of Exercise 18,recall that...
 6.21: Cattle, part III. Suppose the auctioneer in Exercise 19sold a herd ...
 6.22: Caught speeding. Suppose police set up radar surveillance on the S...
 6.23: Professors. A friend tells you about a recent studydealing with the...
 6.24: Rock concerts. A popular band on tour played a seriesof concerts in...
 6.25: Guzzlers? Environmental Protection Agency (EPA)fuel economy estimat...
 6.26: IQ. Some IQ tests are standardized to a Normal model,with a mean of...
 6.27: Small steer. In Exercise 17 we suggested the modelN(1152, 84) for w...
 6.28: High IQ. Exercise 26 proposes modeling IQ scores withN(100, 16). Wh...
 6.29: Trees. A forester measured 27 of the trees in a largewoods that is ...
 6.30: Rivets. A company that manufactures rivets believesthe shear streng...
 6.31: Trees, part II. Later on, the forester in Exercise 29shows you a hi...
 6.32: Car speeds, the picture. For the car speed data of Exercise 18, he...
 6.33: Winter Olympics 2006 downhill. Fiftythree menqualified for the men...
 6.34: Check the model. The mean of the 100 car speeds inExercise 20 was 2...
 6.35: Receivers. NFL data from the 2006 football seasonreported the numbe...
 6.36: Customer database. A large philanthropic organization keeps record...
 6.37: Normal cattle. Using N(1152, 84), the Normal modelfor weights of An...
 6.38: IQs revisited. Based on the Normal model N(100, 16)describing IQ sc...
 6.39: More cattle. Based on the model N(1152, 84) describing Angus steer...
 6.40: More IQs. In the Normal model N(100, 16), what cutoffvalue boundsa)...
 6.41: Cattle, finis. Consider the Angus weights modelN(1152, 84) one last...
 6.42: IQ, finis. Consider the IQ model N(100, 16) one last time.a) What I...
 6.43: Cholesterol. Assume the cholesterol levels of adultAmerican women c...
 6.44: Tires. A tire manufacturer believes that the treadlife ofits snow t...
 6.45: Kindergarten. Companies that design furniture for elementary schoo...
 6.46: Body temperatures. Most people think that the normal adult body te...
 6.47: Eggs. Hens usually begin laying eggs when they areabout 6 months ol...
Solutions for Chapter 6: The Standard Deviation as a Ruler and the Normal Model
Full solutions for Stats: Modeling The World  3rd Edition
ISBN: 9780131359581
Solutions for Chapter 6: The Standard Deviation as a Ruler and the Normal Model
Get Full SolutionsThis textbook survival guide was created for the textbook: Stats: Modeling The World , edition: 3. Stats: Modeling The World was written by and is associated to the ISBN: 9780131359581. This expansive textbook survival guide covers the following chapters and their solutions. Since 47 problems in chapter 6: The Standard Deviation as a Ruler and the Normal Model have been answered, more than 27962 students have viewed full stepbystep solutions from this chapter. Chapter 6: The Standard Deviation as a Ruler and the Normal Model includes 47 full stepbystep solutions.

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

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

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

Biased estimator
Unbiased estimator.

Bimodal distribution.
A distribution with two modes

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

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

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.

Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

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

Conditional probability
The probability of an event given that the random experiment produces an outcome in another event.

Conidence level
Another term for the conidence coeficient.

Continuity correction.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

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

Convolution
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

Degrees of freedom.
The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

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

Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

Exhaustive
A property of a collection of events that indicates that their union equals the sample space.

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