 26.1: Graduation rates A prestigious college is interested in factors tha...
 26.2: Shoot to score A college hockey coach collected data from the 20102...
 26.3: Graduation rates, part II Using the regression output in Exercise 1...
 26.4: Shoot to score another one Using the regression output from Exercis...
 26.5: Graduation rates, part III Continuing with the regression of Exerci...
 26.6: Shoot to score, hat trick Returning to the results of Exercise 2, w...
 26.7: Graduation, part IV The college administrators in Exercise 1 tested...
 26.8: Shoot to score, number four What can the hockey coach in Exercise 2...
 26.9: Graduation, part V The college administrators in Exercise 1 constru...
 26.10: Shoot to score, overtime The coach in Exercise 2 found a 95% confid...
 26.11: Tracking hurricanes In Chapter 6, we looked at data from the Nation...
 26.12: Drug use The 2011 World Drug Report investigated the prevalence of ...
 26.13: Movie budgets How does the cost of a movie depend on its length? Da...
 26.14: House prices How does the price of a house depend on its size? Data...
 26.15: Movie budgets: the sequel Exercise 13 shows computer output examini...
 26.16: Second home Exercise 14 shows computer output examining the associa...
 26.17: Hot dogs Healthy eating probably doesnt include hot dogs, but if yo...
 26.18: Cholesterol Does a persons cholesterol level tend to change with ag...
 26.19: Second frank Look again at Exercise 17s regression output for the c...
 26.20: More cholesterol Look again at Exercise 18s regression output for a...
 26.21: Last dog Based on the regression output seen in Exercise 17, create...
 26.22: Cholesterol, finis Based on the regression output seen in Exercise ...
 26.23: Marriage age The scatterplot below suggests a decrease in the diffe...
 26.24: Used cars Vehix.com offered several used Toyota Corollas for sale. ...
 26.25: Marriage age , again Based on the analysis of marriage ages since 1...
 26.26: Used cars , again Based on the analysis of used car prices you did ...
 26.27: Fuel economy A consumer organization has reported test data for 50 ...
 26.28: SAT scores How strong was the association between student scores on...
 26.29: Fuel economy, part II Consider again the data in Exercise 27 about ...
 26.30: SATs, part II Consider the high school SAT scores data from Exercis...
 26.31: *Fuel economy, part III Consider again the data in Exercise 27 abou...
 26.32: *SATs again Consider the high school SAT scores data from Exercise ...
 26.33: Cereal A healthy cereal should be low in both calories and sodium. ...
 26.34: Brain size Does your IQ depend on the size of your brain? A group o...
 26.35: Another bowl Further analysis of the data for the breakfast cereals...
 26.36: Winter The output shows an attempt to model the association between...
 26.37: Acid rain Biologists studying the effects of acid rain on wildlife ...
 26.38: Climate change and CO Data collected from around the globe show tha...
 26.39: Ozone The Environmental Protection Agency is examining the relation...
 26.40: Sales and profits A business analyst was interested in the relation...
 26.41: Ozone, again Consider again the relationship between the population...
 26.42: More sales and profits Consider again the relationship between the ...
 26.43: Tablet computers In October 2011, cnet.com listed the battery life ...
 26.44: Crawling Researchers at the University of Denver Infant Study Cente...
 26.45: Body fat Do the data shown in the table below indicate an associati...
 26.46: Body fat, again Use the data from Exercise 45 to examine the associ...
 26.47: Grades The data set below shows midterm scores from an Introductory...
 26.48: Grades? The professor teaching the Introductory Statistics class di...
 26.49: Strike two Remember the Little League instructional video discussed...
 26.50: All the efficiency money can buy 2011 A sample of 84 model2011 car...
 26.51: Education and mortality The software output below is based on the m...
 26.52: Property assessments The software outputs below provide information...
Solutions for Chapter 26: Inferences for Regression
Full solutions for Stats Modeling the World  4th Edition
ISBN: 9780321854018
Solutions for Chapter 26: Inferences for Regression
Get Full SolutionsThis expansive textbook survival guide covers the following chapters and their solutions. Chapter 26: Inferences for Regression includes 52 full stepbystep solutions. This textbook survival guide was created for the textbook: Stats Modeling the World, edition: 4. Stats Modeling the World was written by and is associated to the ISBN: 9780321854018. Since 52 problems in chapter 26: Inferences for Regression have been answered, more than 21324 students have viewed full stepbystep solutions from this chapter.

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

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.

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

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.

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

Bimodal distribution.
A distribution with two modes

C chart
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defectsperunit or U chart.

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

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Control limits
See Control chart.

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.

Deming
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

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

Enumerative study
A study in which a sample from a population is used to make inference to the population. See Analytic study

Error propagation
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

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

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

Ftest
Any test of signiicance involving the F distribution. The most common Ftests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

Forward selection
A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.