 10.1: Residuals. Suppose you have fit a linear model tosome data and now ...
 10.2: Residuals. Suppose you have fit a linear model tosome data and now ...
 10.3: Airline passengers revisited. In Chapter 9, Exercise 9,we created a...
 10.4: Hopkins winds, revisited. In Chapter 5, we examinedthe wind speeds ...
 10.5: Models. For each of the models listed below, predict ywhen
 10.6: More models. For each of the models listed below,predict y when
 10.7: Gas mileage. As the example in the chapter indicates,one of the imp...
 10.8: Crowdedness. In a Chance magazine article (Summer2005), Danielle Va...
 10.9: Gas mileage revisited. Lets try the reexpressed variable Fuel Con...
 10.10: Crowdedness again. In Exercise 8 we looked at UnitedNations data ab...
 10.11: GDP. The scatterplot shows the gross domestic product(GDP) of the U...
 10.12: Treasury Bills. The 3month Treasury bill interest rateis watched b...
 10.13: Better GDP model? Consider again the post1950trend in U.S. GDP we ...
 10.14: Pressure. Scientist Robert Boyle examined the relationship between...
 10.15: Brakes. The table below shows stopping distances infeet for a car t...
 10.16: Pendulum. A student experimenting with a pendulumcounted the number...
 10.17: Baseball salaries 2005. Ballplayers have been signingever larger co...
 10.18: Planet distances and years 2006. At a meeting of theInternational A...
 10.19: Planet distances and order 2006. Lets look again atthe pattern in t...
 10.20: Planets 2006, part 3. The asteroid belt between Marsand Jupiter may...
 10.21: Eris: Planets 2006, part 4. In July 2005, astronomersMike Brown, Ch...
 10.22: Models and laws: Planets 2006 part 5. The modelyou found in Exercis...
 10.23: Logs (not logarithms). The value of a log is based onthe number of ...
 10.24: Weightlifting 2004. Listed below are the gold medalwinning mens we...
 10.25: Life expectancy. The data in the next column list theLife Expectanc...
 10.26: Lifting more weight 2004. In Exercise 24 you examined the winning ...
 10.27: Slower is cheaper? Researchers studying how a carsFuel Efficiency v...
 10.28: Orange production. The table below shows that as thenumber of orang...
 10.29: Years to live 2003. Insurance companies and other organizations us...
 10.30: Tree growth. A 1996 study examined the growth ofgrapefruit trees in...
Solutions for Chapter 10: Reexpressing Data: Get It Straight!
Full solutions for Stats: Modeling The World  3rd Edition
ISBN: 9780131359581
Solutions for Chapter 10: Reexpressing Data: Get It Straight!
Get Full SolutionsStats: 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. This textbook survival guide was created for the textbook: Stats: Modeling The World , edition: 3. Since 30 problems in chapter 10: Reexpressing Data: Get It Straight! have been answered, more than 45068 students have viewed full stepbystep solutions from this chapter. Chapter 10: Reexpressing Data: Get It Straight! includes 30 full stepbystep solutions.

2 k p  factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

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

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

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

Bimodal distribution.
A distribution with two modes

Bivariate normal distribution
The joint distribution of two normal random variables

Coeficient of determination
See R 2 .

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

Comparative experiment
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

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 matrix
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the offdiagonal elements rij are the correlations between Xi and Xj .

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

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.

Error of estimation
The difference between an estimated value and the true value.

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

Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.

Fraction defective control chart
See P chart

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 .