
a.
Find the predicted price of a 2 bedroom, 1,000 sq-ft house.
a.

Answer to Problem 1E
The predicted price of a 2 bedroom, 1,000 sq-ft house in upstate New York is likely to be $99,859.89.
Explanation of Solution
Calculation:
The regression model for the price of houses in upstate New York with respect to number of bedrooms and living area is given as:
Substitute
Thus, the predicted price of a 2 bedroom, 1,000 sq-ft house in upstate New York is likely to be $99,859.89.
b.
Find the residual corresponding to a house that sold for $135,000.
b.

Answer to Problem 1E
The residual corresponding to a house that sold for $135,000 is $35,140.11.
Explanation of Solution
Calculation:
Residual:
The residual corresponding to a predictor variable is given as the difference between actual value of the response variable and the predicted value. That is,
Put the actual value of ‘price’ as
Then,
Thus, the residual corresponding to a house that sold for $135,000 is $35,140.11.
c.
Explain what the residual says about the transaction.
c.

Explanation of Solution
The real price, at which the house is sold, is $135,000. The regression model predicted that the price of the house would be sold at $99,859.89.
The residual $35,140.11 means that the house is sold at a price of $35,140.11 more than what was predicted.
Want to see more full solutions like this?
Chapter 25 Solutions
Intro STATS, Books a la Carte Plus New Mystatlab with Pearson Etext -- Access Card Package
- 18 Using the results from the rainfall versus corn production data in Question 14, answer DOV 15 the following: a. Find and interpret the slope in the con- text of this problem. 79 b. Find the Y-intercept in the context of this problem. alb to sig c. Can the Y-intercept be interpreted here? (.ob or grinisiques xs as 101 gniwollol edt 958 orb sz) asiques sich ed: flow wo PEMAIarrow_forwardVariable Total score (Y) Putts hit (X) Mean. 93.900 35.780 Standard Deviation 7.717 4.554 Correlation 0.896arrow_forward17 Referring to the figures and tables from the golf data in Questions 3 and 13, what hap- pens as you keep increasing X? Does Y increase forever? Explain. comis word ே om zol 6 svari woy wol visy alto su and vibed si s'ablow it bas akiog vino b tad) beil Bopara Aon csu How wod griz -do 30 義arrow_forward
- Variable Temperature (X) Coffees sold (Y) Mean 35.08 29,913 Standard Deviation 16.29 12,174 Correlation -0.741arrow_forward13 A golf analyst measures the total score and number of putts hit for 100 rounds of golf an amateur plays; you can see the summary of statistics in the following table. (See the figure in Question 3 for a scatterplot of this data.)noitoloqpics bella a. Is it reasonable to use a line to fit this data? Explain. 101 250 b. Find the equation of the best fitting 15er regression line. ad aufstuess som 'moob Y lo esulav in X ni ognado a tad Variable on Mean Standard Correlation 92 Deviation Total score (Y) 93.900 7.717 0.896 Putts hit (X) 35.780 4.554 totenololbenq axlam riso voy X to asulisy datdw gribol anil er 08,080.0 zl noitsism.A How atharrow_forwardVariable Bone loss (Y) Age (X) Mean 35.008. 67.992 Standard Deviation 7.684 10.673 Correlation 0.574arrow_forward
- 50 Bone Loss 30 40 20 Scatterplot of Bone Loss vs. Age . [902) 10 50 60 70 80 90 Age a sub adi u xinq (20) E 4 adw I- nyd med ivia .0 What does a scatterplot that shows no linear relationship between X and Y look like?arrow_forwardVariable Temperature (X) Coffees sold (Y) Mean 35.08 29,913 Standard Deviation 16.29 12,174 Correlation -0.741arrow_forward2 Find and interpret the value of r² for the rainfall versus corn data, using the table from Question 14.2291992 b sgen gnome vixists 992 ms up? 2910 1999 bio .blos estos $22 tolqis2 qs rieds ni zoti swoH iisqa vilsen od 1'meo DOV to mogers boangas mus jil Reustar enou Leption20th ) abnuin Hagodt graub 032 Carrow_forward
- 18 Using the results from the rainfall versus corn production data in Question 14, answer oy the following: DOY 98 103 LA Find and interpret the slope in the con- text of this problem. b. Find the Y-intercept in the context of this problem. roy gatiigisve Toy c. Can the Y-intercept be interpreted here? (.ob o grinisq blo eiqmaxs as 101 galwollol edt 998 ds most notamotni er griau sib 952) siqmaxs steb godt llaw worl pun MAarrow_forwardVariable mean standard variation correlation temperature(X) 35.08 16.29. -0,741 coffees sold(Y). 29,913. 12.174.arrow_forward12 ம் Y si to no 1672 1 A medical researcher measures bone density and the age of 125 women; you can see the o lesummary of statistics in the following table. (See the figure in Question 2 for a scatterplot of this data.) a. How well will a line fit this data? b. Find the equation of the best fitting regression line. Variable Mean Standard Correlation Deviation Bone loss (Y) 35.008 7.684 0.574 A Age (X) 19 67.992 10.673 T in send art lo (d) sqala sala bolt 3 esmit sqola ad garrow_forward
- MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning
- Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman





