A study was done in diesel-powered light-duty pickup truck to see if humidity, air temperature, and barometricpressure influence emission of nitrous oxide (in ppm). Emission measurements were taken at different times,with varying experimental conditions.a. Fit the multiple linear regression model.b. Estimate the amount of nitrous oxide emitted for the conditions where humidity is 50%,temperature is 76 deg F, and barometric pressure is 29.30.NitrousHumidity, Temp.,Pressure.NitrousHumidity, Temp.,Pressure,Oxide, yOxide, y0.9072.476.329.181.0723.276.829.380.9141.670.329.350.9447.486.629.350.9634.377.129.21.1031.576.929.630.8935.168.029.271.1010.686.329.561.0010.779.029.781.1086.076.311.229.481.1012.967.429.390.9173.329.401.158.366.829.690.8775.477.929.281.0320.176.929.480.7896.678.729.290.7772.277.729.090.82107.486.829.031.0724.067.729.600.9554.970.929.37

For the given data find multiple regression model and estimate the amount of nitrous oxide

Answered: Study dorie in diesel- ered light-duty…

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 collected teacher ratings for 25 courses taught by an instructor over a six-vear period. The students' ratings of the instructor are on a scale of 1 to 9. We found that The linear regression equation is: Average Rating = 7,88 -0.068 Numher of Students 1. Interpret the slope of this model including units: The average rating decreases per each additional student 0.068 ed teacher rating for a class size of 15 students using the given 7.88 prediction equation is Next page CS Scanned with CamScanner
Table 8: Regression of Weight Loss on Hours of Exercise and Caloric Intalk Variable se b 95% CI Hours of Exercise 0.27 0.11 0.05, 0.49 Caloric Intake -0.002 .0008 -0.0035, -0.0004 Intercept 8.50 0.73 7.05, 9.95 Note: R? = .35, adj. R? = .35, F = 22.18, df = 2,117; n = 120 %3D swer the following questions about this regression analysis. Which predictors of weight loss are statistically significant, if either? What is the predicted weight loss for someone who consumes 2,000 calori hour per day on average? What is the literal interpretation for the slope of hours of aerobic exercise
Ms. Patsy Knowlet, a water quality engineer, noted that there seemed to be a close connection between an important streamflow water quality parameter, Y, and the flow, X m/s. She found that 9 pairs of observations yielded the following data: E*= 152 E* = 576 Ey= Ey* = 5183 y = 1726 45.6 Find the 95% confidence limits of the slope of the regression.
Designers of backpacks use exotic material to make packs that fit comfortably and distribute weight to eliminate pressure points. For fitting a regression model of price of backpack on the capacity (cubic inches) and comfort rating of backpacks, a data for 10 backpacks are used. Comfort was measured using a rating from 1 to 5, with a rating of 1 denoting average comfort and a rating of 5 denoting excellent comfort. The output of the regression model is as follows on the next page: Residuals: 10 Median |-84.12 -27.18 10.61 36.90 48.26 Min 30 Маx Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) 356.12083 197.17401 1.806 0.113859 xl -0.09874 0.04588 -2.152 0.068372 . x2 122.86721 21.79975 5.636 0.000786 *** Signif. codes: 0 **** o.001 *** 0.01 ** 0.05 '.' 0.1 '' 1 Residual standard error: 51.14 on 7 degrees of freedom Multiple R-squared: 0.8318, F-statistic: 17.31 on 2 and 7 DF, p-value: 0.00195 Adjusted R-squared: 0.7838 a. Determine the estimated regression equation that…
Let x be the size of a house (in square feet) and y be the amount of natural gas used (therms) during a specified period. Suppose that for a particular community, x and y are related according to the simple linear regression model with the following values. ? = slope of population regression line = 0.016 ? = y intercept of population regression line = −7 Question: Graph the population regression line by first finding the point on the line corresponding to x = 1,000 and then the point corresponding to x = 2,000, and drawing a line through these points.
The table contains data on vehicle speed (h) and fuel consumption (lt / 100km) of 5 randomly selected vehicles. Estimate the average fuel consumption of a vehicle traveling at 45 km / h using the simple linear regression equation between vehicle speed and fuel consumption. Speed 55 60 65 70 75 Consumption 13 12 11 10 9 a. 15 b. 8 c. 7 d. 20
The following table gives the regression results of consumption behaviour between female and male. Consumption (C), in RM Model 1 Model 2 Constant 0.7515 0.6587 (0.024) (0.037) Y 0.8603 0.5442 (0.0301) (0.2881) Gen 0.2001 (0.0004) Y*Gen 0.3105 (0.0076) Adjusted R 0.78 0.78 No. of Observations 47 47 Note: Y=income (RM) Gen= Gender ( It is equal to 1 if person is female; 0 is for male) Figures in the parentheses are p- values a) Identify which are the qualitative, quantitative and interaction variable. Refer to Model 1: b) Write the regression equations for female and male. Draw the regression lines for female and male on the same diagram. What can you c) say? d) Interpret all the estimated slope coefficients. e) Conduct an appropriate test to determine whether variable gender is statistically significant at 5%. Should this variable be dropped from this regression model.
The birth lengths in cm (x) and birth weights in kg (y) of a sample of 50 newborn female babies are compared, yielding a correlation coefficient of r=0.578 and a linear regression equation of ŷ =−8.89+0.243x The babies all had lengths between 46.5 and 53.0 cm, and weights between 2.50 and 4.05 kg. Based on this, predict the birth weight of a newborn female baby with a birth length of 48.5 cm.
The following data on x maternal age in years of the young birth mothers and y = weight of baby born in grams summarizes the result of a study. Assume that a simple linear regression model y = Bo + B1x + e is an appropriate model for the study. x-bar = 17 (avg of x's) y-bar = 3004.1 (avg of y's) SSx = 20 SSxy %3D 4903 SSw 1539182.9 n 10 Calculate the value of s-(standard error of regression) and enter the answer to the nearest tenth (1 decimal place).
Solvent cement is used to join PVC joints. Researchers are interested in predicting the amount of time needed for the joint to set (measured in hours) as it is related to the temperature measured in degrees F for 4 to 8-inch diameter pieces. A random sample of PVC joints was taken and a linear regression model was found. Use the following output and the fact that R2 = 0.965 to answer the following questions. 1. Find the equation of the regression line. 2. Interpret the slope in the words of the problem. 3. Find the Coefficient of Determination and interpret in the words of the problem
The estimated regression equation for a model involving two independent variables and 10 observations follows. ý = 22.1370 + 0.5303Xq + 0.4920X2 (a) Interpret b₁ in this estimated regression equation. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₁ when X₂ is held constant. O b₁ = 22.1370 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. O b₁ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. Interpret b₂ in this estimated regression equation. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 22.1370 is an estimate of the change in y corresponding to a…
Given the table below, Let y, x1, x2, and x3 be the time of concentration (t, in hr), watershed length (L in ft), watershed slope (S in %), and runoff curve number (CN): Evaluate the linear multiple regression model with two or three predictor variables. Compute the multiple correlation coefficient, the standard error of estimate, and the standard deviation of the variable y. Discuss the reliability of the model

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