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business statistics communicating

Business Statistics Communicating With Numbers 1st Edition Kelly Jaggia - Solutions

FILE In a sem inal study, researchers docum ented race-based hiring in th e Boston and Chicago labor m arkets (American Economic Review, Septem ber 2004). They sen t o ut identical resum es to em ployers, half w ith traditionally African-American nam es and th e o th er half w ith traditionally
FILE A financial analyst w ould like to determ ine w h eth er th e return on Fidelity's M agellan m utual fund varies depending on th e quarter; th a t is, if th ere is a seasonal com ponent describing return. He collects 10 years of quarterly return d ata. A portion is show n in th e accom panying
F IL E Estimate and interpret the logit model with the above Divorce data.a. Do the data support the article's claim that the divorce rate is higher for those aged 25–29 years old? Explain.b. Use the above estimates to predict the probability of divorce for an individual who is 27 years old,
FILE According to a recent estimate, the divorce rate in England has fallen to a 26-year low (The Guardian, August 29 , 2008).However, it is documented that the rate of divorce is more than twice as high for men and wom en aged 25 to 29. John Haddock is a sociologist from Sussex University who
FILE Analyze a logit model with the above Health Insurance data. Consider an individual with an income of $60,000.What is the probability that she has insurance coverage if her employer contributes 50% of the premium? What if the employer contributes 75% of the premium?
F IL E A ccording to th e National C oalition on Health Care, there has been a steady decline in th e p ro p o rtio n o f Am ericans w h o have health insurance. The rising insurance premiums have made it difficult for small employers to offer insurance and those that do offer insurance are
FILE Use the above data labeled Purchase to estimate a logit model.a. C om pute the predicted probability o f an Under A rm our purchase fo r a 20-year-old custom er and a 30-year-old customer.b. Test Annabel's belief that the Under Armour brand attracts a younger customer at the 5% level.
F IL E Annabel, a retail analyst, has been following Under Armour, Inc., the pioneer in the compression-gear market.Compression garments are meant to keep moisture away from a wearer's body during athletic activities in warm and cool w eather. A nnabel believes th a t th e U nder A rm o u r brand
Using 40 observations, the following output was obtained when estimating the logit model.Predictor Coef SE Z P Constant 1.609 1.405 1.145 0.252 x1 – 0.194 0.143 – 1.357 0.177 x2 0.202 0.215 0.940 0.348 x3 0.223 0.086 2.593 0.010a. What is the predicted probability when x1 = 15, x2 = 10, and x3
Using 30 observations, the following output was obtained when estimating the logit model.Predictor Coef SE Z PConstant – 0.188 0.083 2.27 0.024 x 3.852 1.771 2.18 0.030a. What is the predicted probability when x = 0.40?b. Is x significant at the 5% level?
Using 30 observations, the follow ing regression output is obtained from estimating the linear probability model y = β 0 + β1 x + ε .Coefficients Standard Error t Stat p-value Intercept 1.31 0.31 4.17 0.0002 x – 0.04 0.01 – 2.67 0.0125a. What is the predicted probability when x = 20?b. Is x
Consider a binary response variable y and two explanatory variables x 1 and x2. The follow ing table contains the param eter estimates of the linear probability model (LPM) and the logit model, w ith the associated p-values shown in parentheses.Variable LPM Logit Constant– 0.40(0.03)–
Consider a binary response variable y and an explanatory variable x. The following table contains the parameter estim ates o f the linear p ro b a b ility m odel (LPM) and the lo g it model, w ith the associated p-values shown in parentheses.Variable LPM Logit Constant– 0.40(0.03)– 4.50(0.01)x
Consider a binary response variable y and an explanatory variable x that varies between 0 to 50. The linear probability model is estimated as ŷ = 0.92 – 0.02x.a. Com pute the estim ated p robability fo r x = 25 a n d x = 40.b. For what values of x is the estimated probability negative?
FILE The savings rate has declined dramatically over the past few decades (CNNMoney.com, June 30, 2010). While some economists are extremely concerned about this decline, others believe that it is a nonissue. Consider the following monthly data on the personal savings rate (Savings) and the
F I L E O ne of th e th eo ries regarding initial public offering(IPO) pricing is th a t th e initial return (change from offer to o p en price) on an IPO d e p en d s on th e price revision(change from pre-offer to offer price). A nother factor th a t m ay influence th e initial return is a
F IL E According to th e World Health Organization, obesity has reached ep idem ic p ro p o rtio n s globally. While obesity has generally b een linked w ith chronic disease an d disability, researchers arg u e th a t it m ay also affect w ages. In o th e r w ords, th e body m ass index (BMI) of an
FILE A sociologist is looking at th e relationship betw een consum ption expenditures y of families in th e United States, family incom e x, and w h eth er or not th e family lives in an urban or rural com m unity (Urban = 1 if urban, 0 otherw ise).She collects data on 50 families across th e
House pricey is estim ated as a function of th e square footage of a house x ; a dum m y variable d th a t equals 1 if th e house has ocean views and 0 otherw ise; and a product of this dum m y and th e square footage xd. The estim ated house price, m easured in $ 1,000s, is given by ŷ = 80 +
Using 20 o b se rv atio n s, th e follow ing reg ressio n o u tp u t is obtained from estim ating y = β0 + β 1x + β 2d + β 3xd + ε.Coefficients Standard Error t Stat p -value Intercept 13.56 3.31 4.09 0.0009 x 4.62 0.56 8.31 0.0000 d – 5.15 4.97 – 1.04 0.3156 xd 2.09 0.79 2.64 0.0178a. C
Consider a linear regression m odel w h e re y represents th e response variable and x and d are th e explanatory variables;d is a dum m y variable assum ing values 0 or 1. A m odel with th e dum m y d and th e interaction xd variables is estim ated asŷ = 5.2 + 0.9x + 1.4 d + 0.2xd.a. C om pute ŷ
FILE The issues regarding executive compensation have received extensive media attention. The government is even considering a cap on high-flying salaries for executives(New York Times, February 9 , 2009). Consider a regression model that links executive compensation w ith the total assets of the
F IL E A government researcher is analyzing the relationship between retail sales and the gross national product (GNP).He also wonders whether there are significant differences in retail sales related to the quarters of the year. He collects ten years of quarterly data. A portion is shown in the
F IL E A researcher wonders whether males get paid more, on average, than females at a large firm . She interviews 50 employees and collects data on each worker's hourly wage (Wage), years of higher education (EDUC), experience(EXPER), age (AGE), and gender of the employee (d equals 1 if male). A
FILE A manager at an ice cream store is trying to determine how many customers to expect on any given day. Overall business has been relatively steady over the past several years, but the customer count seems to have ups and downs. He collects data over 30 days and records the number of customers,
FILE Use the data described in Exercise 7 to estimate a linear regression model w ith math score as the response variable and GPA and the female dummy variable as the explanatory variables.a. Compute the predicted score for a male student w ith a GPA of 3.5. Repeat the analysis for a female
F I L E The SAT has gone through many revisions over the years. In 2005, a new writing section was introduced that includes a direct writing measure in the form of an essay.People argue that female students generally do worse on math tests but better on writing tests. Therefore, the new section may
FILE In the United States, baseball has always been a favorite pastime and is rife with statistics and theories. While baseball purists may disagree, to an applied statistician no topic in baseball is too small or hypothesis too unlikely. In a recent paper, researchers at Wayne State University
In an a tte m p t to "tim e the market," a financial analyst studies the quarterly returns of a stock. He uses the model y = β 0 +β1 d1 + β 2d2 + β 3d3 + ε where y is the quarterly return of a stock, d1 is a dummy variable that equals 1 if quarter 1 an d 0 otherwise, d2 is a dummy variable
Using 30 observations, the following regression output is obtained from estimating ln(y) = β 0 + β1 x + β 2d + ε , where ln(y) is the natural log o f y .Coefficients Standard Error t Stat p-value Intercept 1.56 0.73 2.14 0.0415 x 0.21 0.08 2.63 0.0139 d 0.15 0.04 3.75 0.0008 The standard error
Using 50 observations, the following regression output is obtained from estimating y = β 0 + β1x + β 2d1 + β 3d2 + ε .Coefficients Standard Error t Stat p-value Intercept – 0.61 0.23 – 2.75 0.0074 x 3.12 1.04 3.01 0.0034 d1 – 13.22 15.65 – 0.85 0.4006 d2 5.35 1.25 4.27 0.0000a. Compute
Consider a linear regression model where y represents the response variable, d1 is a dummy variable assuming values of 0 or 1, and d2 is another dummy variable assuming values of 0 or 1. The model is estimated as ŷ = 160 + 15d1 + 32d2.a. Compute ŷ for d1 = 1 and d2 = 1.b. Compute ŷ for d1 = 0
Consider a linear regression model where y represents the response variable and x and d are the explanatory variables;d is a dummy variable assuming values 0 or 1. The model is estimated asŷ = 14.8 + 4.4x – 3.8d.a. Interpret the dummy variable coefficient.b. Compute ŷ fo r x = 3 and d = 1.c.
Interpret the results from a logit model. P-36
Use a linear probability model to estimate a binary response variable. P-36
Use dummy variables to capture a shift of the intercept and/or slope. P-36
Test for differences between the categories of a qualitative variable. P-36
Use dummy variables to capture a shift of the intercept. P-36
T a b le 16.6 sh o w s a p o rtio n o f d a ta o n th e to ta l c o s t (in $ 1 ,0 0 0 s ) a n d o u tp u t fo r p ro d u c in g a p a rtic u la r p ro d u c t; th e e n tire d a ta set ca n be fo u n d o n th e te x t w e b s ite , la b e le d Total Cost. W e a ls o in c lu d e a p o rtio n o f th
R e fe r b a c k to the fo o d e x p e n d itu re e x a m p le w h e re y re p re se n ts e x p e n d itu re o n fo o d and x represents income. Let the sample regression be w ith the standard error o f the estimate se = 0.18.a. W hat is the predicted food expenditure fo r an individual whose
C o n tin u in g w ith th e e a rlie r e x a m p le o f fo o d e x p e n d itu re , le t th e e s tim a te d re g re s s io n b e ŷ = 12 + 5 6 6 In (x ).a. W h a t is the p re d ic te d fo o d e x p e n d itu re fo r an in d iv id u a l w h o se in c o m e is $20 ,0 0 0 ?b . W h a t is th e p re
Continuing again w ith the example o f expenditure on food, let the estimated re g re s s io n be w ith the standard error o f the estimate, se = 0.20.a. W hat is the predicted expenditure on food fo r an in d ivid u a l whose incom e is$2 0 ,0 0 0 ?b. W hat is the predicted value i f incom e
T h e o b je c tiv e o u tlin e d in th e in tro d u c to ry case w a s to e v a lu a te th e in flu e n c e o f th e n u m b e r o f b e d ro o m s (B e d s ), th e n u m b e r o f b a th ro o m s (B a th s ), an d th e sq u a re fo o tage (S q ft) o n m o n th ly re n t (R e n t). U se th e A n n
Revisit the four regression models in Example 16.8 and determine which model is best suited for making predictions. PLO5
Compare the above quadratic regression model with a linear model that uses BA, RBI, and Experience as the explanatory variables. PLO5
Estimate a quadratic regression model using Salary as the response variable and BA, RBI, Experience, and Experience2 as the explanatory variables. PL5
Discuss the potential problems of serial correlation in this time series data application.
Estimate a linear and an exponential regression model and use numerical measures to select the most appropriate model for prediction.
Construct scatterplots to quantify the relationship of housing starts with the mortgage rate and the unemployment rate.
F I L E savings rate has declined dram atically over the past few decades (CNNMoney.com, June 3 0 , 2010). W hile some economists are extrem ely concerned about this decline, others believe th a t it is a nonissue. Consider the follow ing m onthly data on the personal savings rate (Savings) and the
F IL E A nutritionist wants to understand the influence of income and healthy food on the incidence of smoking. He collects 2009 data on the percentage o f smokers in each state in the U.S. and the corresponding median income and the percentage of the population that regularly eats fruits and
FILE A realtor examines the factors that influence the price of a house. He collects data on the prices for 36 single-fam ily homes in Arlington, Massachusetts sold in the first quarter of 2009. For explanatory variables, he uses the house's square footage (Sqft), as well as its number of bedrooms
F IL E A sports enthusiast wants to examine the factors that influence a quarterback's salary (Salary). In particular, he wants to assess the influence o f the pass com pletion rate (PC), the total touchdow ns scored (TD), and a quarterback's age (Age) on Salary. He uses 2009 data, a portion o f
F I L E Economists often exam ine the relationship betw een th e inputs o f a production fu n ction and th e resulting o u tpu t. A com m on w ay o f m odeling this relationship is referred to as the Cobb-Douglas production function.This function can be expressed as ln(Q) = β 0 + β 1 In(L) +β
F I L E Use th e d a ta in E xercise 19 t o a n s w e r th e s a m e fo u r q u e s tio n s re g a rd in g life e x p e c ta n c y o f m a le s. W h o is m o re lik e ly to b e n e fit fr o m a d d in g m o re p h y s ic ia n s to th e p o p u la tio n ? E xp la in.
F I L E L ife e x p e c ta n c y a t b ir th is th e a v e ra g e n u m b e r o f ye ars th a t a p e rs o n is e x p e c te d to live . T h e re is a h u g e v a ria tio n in life e x p e c ta n c ie s b e tw e e n c o u n trie s w it h th e h ig h e s t b e in g in J a p a n , a n d th e lo w e s
F IL E Chad Dobson has heard about the positive outlook fo r real estate investm ent in college towns. He is interested in investing in Davis, California, w hich houses one o f the University o f California campuses. He uses zillow.com to access data on 2011 m onthly rent for 27 houses, along w ith
F I L E P ro fe sso r O rle y A s h e n fe lte r o f P rin c e to n U n iv e rs ity is a p io n e e r in th e fie ld o f w in e e c o n o m ic s . H e c la im s th a t, c o n tr a r y to o ld o rth o d o x y , th e q u a lity o f w in e can b e e x p la in e d m o s tly in te rm s o f w e a th e r
F I L E A c c o rd in g to th e W o rld H e a lth O rg a n iz a tio n , o b e s ity has re a c h e d e p id e m ic p ro p o rtio n s g lo b a lly . W h ile o b e s ity has g e n e ra lly b e e n lin k e d w ith c h ro n ic d ise a se a n d d is a b ility , re sea rch e rs a rg u e th a t it m a y a
C o n s id e r th e fo llo w in g 10 o b s e rv a tio n s o f y a n d x .34.62 8.06 12.67 23.02 11.82 27.23 18.23 11.23 11.00 21.07 22 2 11 19 2 21 18 11 19 20a. P lo t th e a b o v e d a ta to c h o o s e b e tw e e n th e lin e a r a n d th e e x p o n e n tia l m o d e l.b. J u s tify y o u r c
Consider the follow ing 10 observations o f y and x .22.21 21.94 22.83 22.66 21.44 22.51 22.87 22.50 22.88 23.16 12 5 15 16 4 8 11 12 18 16a. P lo t th e a b o v e d a ta to c h o o s e b e tw e e n th e lin e a r a n d th e lo g a rith m ic m o d e ls .b. Ju stify y o u r ch oice u sin g th e a p
Consider the sample regressions for the linear, the logarithm ic, the exponential, and the log-log models. For each o f the estimated models, predict y when x equals 50.Response V a ria b le : y R espo nse V a ria b le : ln (y )M o d e l 1 M o d e l 2 M o d e l 3 M o d e l 4 In te rc e p t 18.52
C o n s id e r th e s a m p le re g re s s io n s fo r th e lin e a r, th e lo g a rith m ic , th e e x p o n e n tia l, a n d th e lo g -lo g m o d e ls . For e a ch o f th e e s tim a te d m o d e ls , p r e d ic t y w h e n x e q u a ls 100.Response Variable: y Response Variable: ln(y )M odel 1
C o n s id e r th e fo llo w in g e s tim a te d m o d e ls :a. In te r p r e t th e s lo p e c o e ffic ie n t in e a ch o f th e s e e s tim a te d m o d e ls .b. For e ach m o d e l, w h a t is th e p re d ic te d c h a n g e in y w h e n x incre a ses b y 5 % , fro m 10 to 10.5?
F I L E You collect data on 26 metropolitan areas to analyze average monthly debt payments in terms of income and the unemployment rate. A portion of the data is shown in the accompanying table; the complete data set can be found on the text website, labeled Debt Payments.Metropolitan Area
F I L E Consider a sample comprised o f firm s that were targets o f tender offers during the period 1978– 1985. Conduct an analysis where the response variable represents the num ber o f bids (Bids) received prior to the takeover of the firm . The explanatory variables include the bid prem ium
F I L E Numerous studies have shown that watching too much television hurts school grades. Others have argued that television is not necessarily a bad thing for children (Mail Online, July 18, 2009). Like books and stories, television not only entertains, it also exposes a child to new information
Consider the follow ing sample regressions for the linear, the quadratic, and the cubic models along w ith their respective R2 and adjusted R2.Linear Quadratic Cubic Intercept 19.80 20.08 20.07 X 1.35 1.50 1.58 X2 NA − 0.31 − 0.27 X3 NA NA − 0.03 R2 0.640 0.697 0.698 Adjusted R2. 0.636 0.691
Consider the follow ing sample regressions fo r the linear, the quadratic, and the cubic models along w ith their respective R2 and adjusted R2.Linear Quadratic Cubic Intercept 9.66 10.00 10.06 X 2.66 2.75 1.83 X2 NA − 0.31 − 0.33 X3 NA NA 0.26 R2 0.810 0.836 0.896 Adjusted R2 0.809 0.833
Consider the follow ing 10 observations on the response variable y and the explanatory variable x.9.42 4.88 3.36 3.28 1.67 7.35 6.30 4.67 9.33 5.04 19 5 5 4 10 3 10 11 8a. Plot the above data and estimate the linear and the quadratic regression models.b. Use the appropriate numerical measure to
Consider the fo llow ing 10 observations on the response variable y and the explanatory variable x.13.82 19.06 16.67 13.30 11.77 13.64 18.30 20.78 13.02 16.13 66 5 3 3 12 10 8 5 11a. Plot the above data and then estimate the linear and the quadratic regression models.b. Use the appropriate
Consider the following two estimated models:For each o f the estimated models, predict y w hen x equals 5 and 10.
Describe the method used to compare linear with log transformed models. P-693
Use and evaluate log transformed models. P-693
Use and evaluate polynomial regression models. P-693
Johnson & Johnson (J&J) was founded more than 120 years ago on the premise that doctors and nurses should use sterile products to treat people’s wounds. Since that time, J&J products have become staples in most people’s homes. Consider the CAPM where the J&J risk-adjusted stock return R − Rf
Let’s revisit Model 3, Win = β 0 + β1 BA + β 2ERA + ε , estimated with the sample data in Table 15.1. Conduct a test to determine if batting average and earned run average are jointly significant in explaining winning percentage at α = 0.05.
A manager at a car wash company in Missouri wants to measure the effectiveness of price discounts and various types of advertisement expenditures on sales. For the analysis, he uses varying price discounts (Discount) and advertisement expenditures on radio (Radio) and newspapers (Newspaper) in 40
We again reference the data from Table 15.1 and the regression model Win = β 0 +β1 BA + β 2ERA + ε . Construct a 95% confidence interval for expected winning percentage if BA is 0.25 and ERA is 4.
D is c u s s th e p o te n tia l p ro b le m s o f m u ltic o llin e a r ity a nd s e ria l c o rre la tio n in th is tim e se ries d a ta a p p lic a tio n .50. Revisit the introductory case, where we used the data labeled Baseball to estimate a multiple regression model as Examine the
A t th e 5 % s ig n ific a n c e le v e l, e va lu a te th e in d iv id u a l an d jo in t s ig n ific a n c e o f th e e x p la n a to ry v a ria b le s .
E s tim a te a m u ltip le re g re s s io n m o d e l fo r h o u s in g sta rts u s in g th e m o rtg a g e ra te and th e u n e m p lo y m e n t ra te as th e e x p la n a to ry v a ria b le s .
F ILE A researcher examines the factors that influence student performance. She gathers data on 224 school districts in Massachusetts. The response variable is the students'm ean score on a standardized test (Score). She uses four explanatory variables in her analysis: the studentto-teacher ratio
F IL E A nutritionist wants to understand the influence of income and healthy food on the incidence o f smoking. He collects 2009 data on the percentage o f smokers in each state in the U.S. and the corresponding median income and the percentage o f the population that regularly eats fruits and
F IL E A research analyst is trying to determ ine w hether a firm's price-earnings (P/E) and price-sales (P/S) ratios can explain the firm's stock performance over the past year.Generally, a high P/E ratio suggests th a t investors are expecting higher earnings grow th in the future compared to
F IL E A governm ent researcher examines the factors that influence a city's crime rate. For 41 cities, she collects the crime rate (crimes per 100,000 residents), the poverty rate(in %), the median income (in $ 1,000s), the percent of residents younger than 18, and the percent o f residents older
F IL E George believes th a t returns o f m utual funds are influenced by annual turnover rates and annual expense ratios. In order to substantiate his claim, he random ly selects eight m utual funds and collects data on each fund's five-year annual return (Return), its annual holding turnover rate
F IL E A researcher studies the relationship between SAT scores, the test-taker's fam ily income (Income), and his/her grade point average (GPA). Data are collected from 24 students. A portion o f the data is shown; the entire data set can be found on the te xt website, labeled SAT.SAT Income GPA
F IL E The h o m eo w n ersh ip ra te in th e U.S. w as 67.4% in 2009. In o rd er to d e te rm in e if h o m eo w n ersh ip is linked w ith income, 2009 sta te level d a ta on th e h o m eo w n ersh ip rate (O w nership) an d m edian h o u seh o ld incom e (Incom e)w ere co llected . A p o rtio n o
FILE A sociologist w ishes to stu d y th e relationship betw een hap p in ess and age. He interview s 24 individuals and collects d ata on age and happiness, m easured on a scale from 0 to 100. A portion of th e d ata is show n; th e en tire d ata se t is found on th e te x t w ebsite, labeled H a
In an a tte m p t to d e te rm in e w h e th e r or n o t a linear re latio n sh ip ex ists b e tw e e n th e p rice o f a h o m e (in $1,000s)a n d th e n u m b e r o f d ay s it ta k e s to sell th e h o me, a real e s ta te a g e n t co llected d a ta from re c e n t sales in his city a n d e
FILE In August 2010, the Departm ent of Commerce reported that economic weakness continues across the country w ith consumer spending continuing to stagnate.The governm ent is considering various tax benefits to stim ulate consumer spending through increased disposable income. The consum ption
FILE A capital asset pricing model (CAPM) for Johnson &Johnson (J&J) was discussed in Example 15.3. The model uses the risk-adjusted stock return R − Rf for J&J as the response variable and the risk-adjusted market return RM − Rf as the explanatory variable. The data for the model can be found
F IL E Healthy living has always been an im portant goal for any society. In a recent ad campaign for Walt Disney, First Lady M ichelle Obama shows parents and children tha t eating well and exercising can also be fun (USA Today, September 3 0 , 2010). Consider a regression model that conjectures
F ILE Consider the m onthly rent o f a home in Ann Arbor, Michigan (Rent) as a function o f the num ber of bedrooms (Beds), the num ber o f bathrooms (Baths), and square footage (Sqft).a. Access the data labeled Ann Arbor Rental from the text website and estimate Rent = 1 0 + β1 Beds + β 2Baths
Consider the results of a survey where students were asked about their GPA and also to break down their typical 24-hour day into study, leisure (including work), and sleep. Consider the model GPA = β 0 + β1 Study + β 2Leisure + β 3Sleep + ε .a. What is wrong w ith this model?b. Suggest a
A simple linear regression, y = β0 + β1 x + ε, is estimated w ith tim e series data. The resulting residuals e and the tim e variable fare shown below.1 2 3 4 5 6 7 8 9 10- 5 - 4 - 2 3 6 8 4 - 5 - 3 - 2a. Graph the residuals against tim e and look for any discernible pattern.b. Which assumption
A simple linear regression, y = β 0 + β1 x + ε, is estimated w ith cross-sectional data. The resulting residualse, along w ith the values of the explanatory variable x, are shown below.1 2 5 7 10 14 15 20 24 30− 2 1 − 3 2 4 − 5 − 6 8 11 − 10a. Graph the residuals e against the values
Using 20 observations, the m ultiple regression model y = β 0 + β1 x1 + β2x2 + ε was estimated. Excel produced the follow ing relevant results.d f SS MS F Significance F Regression 2 2.12E + 12 1.06E + 12 56.5561 3.07E-08 Residual 17 3.19E + 11 1.88E + 10 Total 19 2.44E + 12 Coefficients
FILE Access the data labeled Arlington Homes from the text website and estimate: Price = β 0 + β 1 Sqft + β 2Beds +β 2Baths + ε , where Price, Sqft, Beds, and Baths refer to home price, square footage, number of bedrooms, and number of bathrooms, respectively. Construct a 95% confidence

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