Q5/ Use Linear Regression to fit the following data:X12456Y4101093

Answered: Image /qna-images/answer/c26046ca-d840-4e6c-b09c-ac39eea4556a.jpg

Answered: Q5/ Use Linear Regression to fit the…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

See similar textbooks

Similar questions

The sweetness, y, of the fruit is supposed to be related to the average daily sunshine hours, x. The following data shows the sweetness of the same type of fruit at different locations (sunshine hours). Fit the data to a simple linear regression model. x: 5, 6, 7, 6, 6, 8, 7, 5. y: 9, 10, 10, 11, 12, 13, 12, 8. Predict the true mean sweetness for average daily hours of 8 hours, and calculate the residual for average daily sunshine hours of 8 hours.
Suppose there is 1 dependent variable (dissolved oxygen, Y) and 3 independent variables (water temp X1, depth X2, and hardness of water X3). Below is the result of the multiple linear regression.Which of the following is the equation of the multiple regression model? Y = 4.36 + 0.37X1 + 0.24X2 + 0.04X3 Y = 0.00 + 0.02X1 + 0.56X2 + 0.37X3 Y = 24.84 - 1.17X1 - 0.15X2 - 0.04X3 Y = 5.69 – 3.20X1 - 0.61X2 -0.95X3
Fourteen hikers were surveyed at Algonquin Park, and asked for how many days have you been hikingand far did your travel in that time? The equation for the linear regression line is y+3x + 10.3 where x is the number of days and y is the distance travelled. Estimate the number of whole days a hiker would need to travel 35KM? Number of days hiked 1 1 2 3 3 5 5 6 7 7 9 10 11 12 Distance Traveled (km) 12 17 18 19 21 23 25 23 30 31 37 39 41 52
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes
A biologist wants to predict the height of male giraffes, y, in feet, given their age, x1, in years, weight, x2, in pounds, and neck length, x3, in feet. She obtains the multiple regression equation yˆ=7.36+0.00895x1+0.000426x2+0.913x3. Predict the height of a 12-year-old giraffe that weighs 3,100 pounds and has a 7-foot-long neck, rounding to the nearest foot.
Explain the concept of Linear Regression with Multiple Regressors?
Question 39 Solve the problem. Find the general solution of the homogeneous system below. Give your answer as a vector. x1 + 2x2-3x3 = 0 4x1 + 7x2-9x3 = 0 -X1 - 4x2 + 9x3 = 0 8-10
A sports statistician was interested in the relationship between game attendance (in thousands) and the number of wins for baseball teams. Information was collected on several teams and was used to obtain the regression equation ý = 4.9 + 15.2, where x represents attendance (in thousands) and ý is the predicted number of wins. Which statement best describes the meaning of the slope of the regression line? For each increase in attendance by 1,000, the predicted number of wins increases by 4.9. O For each increase in attendance by 1,000, the predicted number of wins increases by 15.2. O For each increase in the number of wins by 1, the predicted attendance increases by 4,900. O For each increase in the number of wins by 1, the predicted attendance increases by 15,200.
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY - 3.17 +0.823HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2700 lb, it has an engine displacement of 1.6 L, and its highway fuel consumption is 35 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.). Regression Table I R² Adjusted R2 WT/DISP WT/HWY Predictor (x) Variables P-Value WT/DISP/HWY 0.000 0.942 0.000 0.748 0.000 0.942 0.000…

Recommended textbooks for you

Advanced Engineering Mathematics

Advanced Math

ISBN:

9780470458365

Author:

Erwin Kreyszig

Publisher:

Wiley, John & Sons, Incorporated

Numerical Methods for Engineers

Advanced Math

ISBN:

9780073397924

Author:

Steven C. Chapra Dr., Raymond P. Canale

Publisher:

McGraw-Hill Education

Introductory Mathematics for Engineering Applicat…

Advanced Math

ISBN:

9781118141809

Author:

Nathan Klingbeil

Publisher:

WILEY

Advanced Engineering Mathematics

Advanced Math

ISBN:

9780470458365

Author:

Erwin Kreyszig

Publisher:

Wiley, John & Sons, Incorporated

Numerical Methods for Engineers

Advanced Math

ISBN:

9780073397924

Author:

Steven C. Chapra Dr., Raymond P. Canale

Publisher:

McGraw-Hill Education

Introductory Mathematics for Engineering Applicat…

Advanced Math

ISBN:

9781118141809

Author:

Nathan Klingbeil

Publisher:

WILEY

Mathematics For Machine Technology

Advanced Math

ISBN:

9781337798310

Author:

Peterson, John.

Publisher:

Cengage Learning,

Basic Technical Mathematics

Advanced Math

ISBN:

9780134437705

Author:

Washington

Publisher:

PEARSON

Topology

Advanced Math

ISBN:

9780134689517

Author:

Munkres, James R.

Publisher:

Pearson,

SEE MORE TEXTBOOKS

Q5/ Use Linear Regression to fit the following data: 2 10 1 4 5 6 Y 4 10 9 3