 7.1: You go to an open house and find that the house is 1 standard devia...
 7.2: You read an ad for a house priced 2 standard deviations below the m...
 7.3: A friend tells you about a house whose size in square meters (hes E...
 7.4: What does the slope of 94.454 mean?
 7.5: What are the units of the slope?
 7.6: Your house is 2000 sq ft bigger than your neighbors house. How much...
 7.7: Is the yintercept of 3.117 meaningful? Explain.
 7.8: Would you prefer to find a home with a negative or a positive resid...
 7.9: You plan to look for a home of about 3000 square feet. How much sho...
 7.10: You find a nice home that size selling for $300,000. Whats the resi...
 7.11: What does the R2 value mean about the relationship of Price and Size?
 7.12: Is the correlation of Price and Size positive or negative? How do y...
 7.13: If we measure house Size in square meters instead, would R2 change?...
 7.14: You find that your house in Saratoga is worth $50,000 more than the...
 7.15: True or false If false, explain briefly. a) We choose the linear mo...
 7.16: True or false II If false, explain briefly. a) Some of the residual...
 7.17: Bookstore sales revisited Recall the data we saw in Chapter 6, Exer...
 7.18: Disk drives again In Chapter 6, Exercise 4, we saw some data on har...
 7.19: Bookstore sales once more Here are the residuals for a regression o...
 7.20: Disk drives once more Here are the residuals for a regression of Pr...
 7.21: Bookstore sales last time For the regression model for the bookstor...
 7.22: Disk drives encore For the hard drive data of Exercise 18, interpre...
 7.23: Residual plots Here are residual plots (residuals plotted against p...
 7.24: Disk drives last time Here is a scatterplot of the residuals from t...
 7.25: Real estate A random sample of records of sales of homes from Febru...
 7.26: Roller coaster The Mitch Hawker poll ranked the Top 10 steel roller...
 7.27: Real estate again The regression of Price on Size of homes in Albuq...
 7.28: Coasters again Exercise 26 examined the association between the Dur...
 7.29: Real estate redux The regression of Price on Size of homes in Albuq...
 7.30: Another ride The regression of Duration of a roller coaster ride on...
 7.31: More real estate Consider the Albuquerque home sales from Exercise ...
 7.32: Last ride Consider the roller coasters described in Exercise 26 aga...
 7.33: Misinterpretations A Biology student who created a regression model...
 7.34: More misinterpretations A Sociology student investigated the associ...
 7.35: ESP People who claim to have ESP participate in a screening test in...
 7.36: SI jinx Players in any sport who are having great seasons, turning ...
 7.37: Cigarettes Is the nicotine content of a cigarette related to the ta...
 7.38: Attendance 2010 In the previous chapter, you looked at the relation...
 7.39: Another cigarette Consider again the regression of Nicotine content...
 7.40: Second inning 2010 Consider again the regression of Average Attenda...
 7.41: Last cigarette Take another look at the regression analysis of tar ...
 7.42: Last inning 2010 Refer again to the regression analysis for average...
 7.43: Income and housing revisited In Chapter 6, Exercise 32, we learned ...
 7.44: Interest rates and mortgages again In Chapter 6, Exercise 33, we sa...
 7.45: Online clothes An online clothing retailer keeps track of its custo...
 7.46: Online clothes II For the online clothing retailer discussed in the...
 7.47: Success in college Colleges use SAT scores in the admissions proces...
 7.48: SAT scores The SAT is a test often used as part of an application t...
 7.49: SAT, take 2 Suppose the AP calculus students complained and insiste...
 7.50: Success, part 2 The standard deviation of the residuals in Exercise...
 7.51: Wildfires 2010 The National Interagency Fire Center (www.nifc.gov) ...
 7.52: Wildfires 2010 The National Interagency Fire Center (www.nifc.gov) ...
 7.53: Used cars 2011 Carmax.com lists numerous Toyota Corollas for sale w...
 7.54: Drug abuse In the exercises of the last chapter you examined result...
 7.55: More used cars 2011 Use the advertised prices for Toyota Corollas g...
 7.56: Birthrates 2009 The table shows the number of live births per 1000 ...
 7.57: Burgers In the last chapter, you examined the association between t...
 7.58: Chicken Chicken sandwiches are often advertised as a healthier alte...
 7.59: A second helping of burgers In Exercise 57 you created a model that...
 7.60: A second helping of chicken In Exercise 58 you created a model to e...
 7.61: Body fat It is difficult to determine a persons body fat percentage...
 7.62: Body fat again Would a model that uses the persons Waist size be ab...
 7.63: Heptathlon 2004 We discussed the womens Olympic heptathlon in Chapt...
 7.64: Heptathlon 2004 again We saw the data for the womens 2004 Olympic h...
 7.65: Least squares I Consider the four points (10, 10), (20, 50), (40, 2...
 7.66: Least squares II Consider the four points (200,1950), (400,1650), (...
Solutions for Chapter 7: Linear Regression
Full solutions for Stats Modeling the World  4th Edition
ISBN: 9780321854018
Solutions for Chapter 7: Linear Regression
Get Full SolutionsChapter 7: Linear Regression includes 66 full stepbystep solutions. Since 66 problems in chapter 7: Linear Regression have been answered, more than 21308 students have viewed full stepbystep solutions from this chapter. This textbook survival guide was created for the textbook: Stats Modeling the World, edition: 4. This expansive textbook survival guide covers the following chapters and their solutions. Stats Modeling the World was written by and is associated to the ISBN: 9780321854018.

aerror (or arisk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

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

Bimodal distribution.
A distribution with two modes

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

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.

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

Contour plot
A twodimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

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.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

Correlation
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

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.

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.

Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

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

Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a modelitting process and not on replication.

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

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

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model