 11.11.1: Describing a multiple regression. Traditionally, demographic and hi...
 11.11.2: Understanding the fitted regression line. The fitted regression equ...
 11.11.3: Significance tests for regression coefficients. As part of a study ...
 11.11.4: ANOVA table for multiple regression. Use the following information ...
 11.11.5: Pairwise relationships among variables in the GPA data set. Using a...
 11.11.6: Residual plots for the GPA analysis. Using a statistical package, f...
 11.11.7: 95% confidence intervals for regression coefficients. In each of th...
 11.11.8: Significance tests for regression coefficients. For each of the set...
 11.11.9: Whats wrong? In each of the following situations, explain what is w...
 11.11.10: Whats wrong? In each of the following situations, explain what is w...
 11.11.11: Constructing the ANOVA table. Seven explanatory variables are used ...
 11.11.12: (d) What proportion of the variation in the response variable is ex...
 11.11.13: Refining the GPA model using all variables. Figure 11.9 (page 629) ...
 11.11.14: Predicting college debt: combining measures. Refer to Exercises 10....
 11.11.15: Predicting college debt: a simpler model. Refer to the previous exe...
 11.11.16: Comparison of prediction intervals. Refer to the previous two exerc...
 11.11.17: Predicting energydrink consumption. Energydrink advertising consis...
 11.11.18: Consider the gender of the students. Refer to Exercise 11.13. The s...
 11.11.19: A mechanistic explanation of popularity. In Exercise 10.59 (page 60...
 11.11.20: Is the number of tornadoes increasing? In Exercise 10.29, data on t...
 11.11.21: (d) Test the hypothesis that the coefficient of the variable 1PA 8....
 11.11.22: Architectural firm billings. A summary of firms engaged in commerci...
 11.11.23: Predicting movie revenuepreliminary analysis. The response variable...
 11.11.24: Predicting movie revenuesimple linear regressions. Now lets look at...
 11.11.25: Predicting movie revenuemultiple linear regression. Now consider fi...
 11.11.26: A simpler model. In the multiple regression analysis using all four...
 11.11.27: 3911 theaters during the first weekend, grossing $38.7 million doll...
 11.11.28: Effect of potential outliers. Consider the simpler model of Exercis...
 11.11.29: Annual ranking of world universities. Lets consider developing a mo...
 11.11.30: Looking at the simple linear regressions. Now lets look at the rela...
 11.11.31: Multiple linear regression model. Now consider a regression model u...
 11.11.32: Predicting GPA of seventhgraders. Refer to the educational data fo...
 11.11.33: Predicting a nations average happiness score. Consider the five sta...
 11.11.34: Building a multiple linear regression model. Lets now build a model...
 11.11.35: Selecting from among several models. Refer to the results from the ...
 11.11.36: Bone formation and resorption. Consider the following four variable...
 11.11.37: Predicting bone formation. Lets use regression methods to predict V...
 11.11.38: More on predicting bone formation. Now consider a regression model ...
 11.11.39: Predicting bone formation using transformed variables. Because the ...
 11.11.40: Predicting bone resorption. Refer to Exercises 11.36 to 11.38. Answ...
 11.11.41: Predicting bone resorption using transformed variables. Refer to th...
 11.11.42: Relationships among PCB congeners. Consider the following variables...
 11.11.43: Predicting the total amount of PCB. Use the four congeners PCB52, P...
 11.11.44: Adjusting the analysis for potential outliers. The examination of t...
 11.11.45: More on predicting the total amount of PCB. Run a regression to pre...
 11.11.46: Multiple regression model for total TEQ. Dioxins and furans are oth...
 11.11.47: Multiple regression model for total TEQ, continued. The information...
 11.11.48: Predicting total amount of PCB using transformed variables. Because...
 11.11.49: Predicting total amount of PCB using transformed variables, continu...
 11.11.50: Even more on predicting total amount of PCB using transformed varia...
 11.11.51: Predicting total TEQ using transformed variables. Use the log data ...
 11.11.52: Interpretation of coefficients in log PCB regressions. Use the resu...
 11.11.53: Interpretation of coefficients in log PCB regressions. Use the resu...
 11.11.54: Pairwise scatterplots of the explanatory variables. Make a scatterp...
 11.11.55: Simple linear regression model of Taste. Perform a simple linear re...
 11.11.56: Another simple linear regression model of Taste. Repeat the analysi...
 11.11.57: The final simple linear regression model of Taste. Repeat the analy...
 11.11.58: Comparing the simple linear regression models. Compare the results ...
 11.11.59: Multiple regression model of Taste. Carry out a multiple regression...
 11.11.60: Another multiple regression model of Taste. Carry out a multiple re...
 11.11.61: The final multiple regression model of Taste. Use the three explana...
 11.11.62: Finding a multiple regression model on the Internet. Search the Int...
Solutions for Chapter 11: Multiple Regression
Full solutions for Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card  8th Edition
ISBN: 9781464158933
Solutions for Chapter 11: Multiple Regression
Get Full SolutionsSince 62 problems in chapter 11: Multiple Regression have been answered, more than 34029 students have viewed full stepbystep solutions from this chapter. Chapter 11: Multiple Regression includes 62 full stepbystep solutions. This textbook survival guide was created for the textbook: Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card, edition: 8. Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card was written by and is associated to the ISBN: 9781464158933. This expansive textbook survival guide covers the following chapters and their 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

Addition rule
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).

Axioms of probability
A set of rules that probabilities deined on a sample space must follow. See Probability

Bayesâ€™ 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.

Chisquare (or chisquared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous 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

Contingency table.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

Continuous distribution
A probability distribution for a continuous random variable.

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

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.

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.

Crossed factors
Another name for factors that are arranged in a factorial experiment.

Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.

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.

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.

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to 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 .