You work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much ahome's distance from the water affects its selling price. You are going to collect a random sample of 7 recently sold homes in your town. You will note thedistance each home is from the water (denoted by x, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also notethe product x.y of the distance from the water and selling price for each home. (These products are written in the row labeled "xy").(a) Click on "Take Sample" to see the results for your random sample.Distance from thewater, .x(in km)Take SampleSelling price, y(in hundreds of thousandsof dollars)xySend data to calculatorBased on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. Whenyou are done, select "Compute". (In the table below, 72 is the sample size and the symbol Σxy means the sum of the values xy.)72: 0?Sample correlation coefficient (7):x: 07:0Slope (b₁):y-intercept (b):Exy:[]Compute

Answered: Image /qna-images/answer/bc539f62-8eab-4b8e-b7e0-50bfbc82b4bc.jpg

Answered: You work as a data scientist for a real…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

We want to predict the selling price of a house in Newburg Park, Florida, based on the distance the house lies from the beach. Suppose that we're given the data in the table below. These data detail the distance from the beach ( x , in miles) and the selling price ( y , in thousands of dollars) for each of a sample of sixteen homes sold in Newburg Park in the past year. The data are plotted in the scatter plot in Figure 1. Distance from the beach, x(in miles) Selling price, y(in thousands of dollars) 13.3 224.2 10.0 288.0 13.7 272.4 10.0 229.5 4.6 274.1 12.7 190.6 8.2 289.4 9.1 215.4 11.5 272.8 7.0 244.4 4.9 309.5 12.4 198.9 7.0 305.4 11.8 202.5 5.7 277.1 18.0 228.5 y 150 200 250 300 350 x 5 10 15 20 0 Figure 1 The value of the sample correlation coefficient r for these data is approximately −0.577 . Answer the…
A university would like to examine the linear relationship between a faculty member's performance rating (measured on a scale of 1-20) and his or her annual salary increase. The table to the right shows these data for eight randomly selected faculty members. Complete parts a and b.
Given the following data for four houses sold in comparable neighborhoods and their corresponding number of square feet, draw a scatter plot and find a linear regression equation representing a relationship between the number of square feet and the selling price of the house. Square Feet Selling Price 1528 2544 3000 3016 $94,970 $207.000 $252,000 $273,000 Draw a scatter plot. Choose the correct graph below. O A. OB. OC. Clear all Print View an example Help me solve this (? 47°F O Type here to search 近
The data in MEAPSINGLE were used to estimate the following equations relating school-level performance on a fourth-grade math test to socioeconomic characteristics of students attending school. The variable free,
A real estate agent has developed a linear model for the price of a house, P, in dollars in terms of the area, A, in square feet for the homes in a certain neighborhood. The data set had areas ranging from 1,000 square feet to 4,500 square feet. Would predicting the prices of a home that is 4,900 square feet be interpolation or extrapolation? Explain. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is outside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is outside the range of the areas in the data.
Mary used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). Her estimated regression equation was Crime = 428 + 0.050 Income. If Income decreases by 500, we would expect that Crime will Group of answer choices remain unchanged. decrease by 25. increase by 500. decrease by 50.
A frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20frequent flyers of a certain airline. She collected the number of miles flown in the previous year and the total amount of money the flyer spent. A regression line of distance flown on money spent was fit to the data, and the intercept and slope were calculated to be ?=24,000 b=10 . A person who spent $2000 is predicted to have flown: 44000.miles 34000miles 54000miles 24000miles
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.9 8.4 6.6 3.8 2.6 2.3 2.2 0.9 y 14.2 11.1 9.6 7 6.2 6.1 5.8 5 x = thousands of automatic weaponsy = murders per 100,000 residentsThis data can be modeled by the equation y=0.85x+4.03. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator.A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons?
Data was collected on the number of pairs of shoes people own and the number of miles they walk per week. 2 = 0.38 . Then 38% of the variation in miles walked can be explained by the linear relationship between the in the number of shoes owned and the miles walked. 62% is attributable to other factors. false true
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
For the advertised price and number of miles data, the equation of the regression line is price = 18,773-86.18(thousands of miles) Predict the asking price for a car with 50,000 miles. $ type your answer...
A biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? d) Suppose the correlation coefficient was −0.12. How would you classify the correlation between temperature and amount of glucose?

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS