Q. 1. Fit a straight line trend by least square method from thefollowing data :Value of X:12345Value of Y :35810

Answered: Image /qna-images/answer/7ff58359-3569-4a2c-ba11-a75d6d5ab36d.jpg

Answered: Q. 1. Fit a straight line trend by…

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

The amount of time adults spend watching television is closely monitored by firms because this helps to determine advertising pricing for commercials. Complete parts (a) through (d). (a) Do you think the variable "weekly time spent watching television" would be normally distributed? If not, what shape would you expect the variable to have? A. The variable "weekly time spent watching television" is likely skewed left, not normally distributed. B. The variable "weekly time spent watching television" is likely normally distributed. C. The variable "weekly time spent watching television" is likely uniform, not normally distributed. D. The variable "weekly time spent watching television" is likely symmetric, but not normally distributed. E. The variable "weekly time spent watching television" is likely skewed right, not normally distributed.
Suppose you ran a quadratic trend forecasting model on some time series data for 18 months. The Excel Data Analysis regression output provided the following: Intercept: 45.140 Slope 1: -1.940 Slope 2: 0.195 What is the forecast value for time period 21? 4.4 90.395 43.395 8.495
You build a linear model to predict Calc2 grades from Calc1 grades. Residuals: Min: -17.674 1Q: -6.759 Median: 0.899 3Q: 6.725 Coefficients: Estimate Std. Error t value (Intercept) 11.866 12.6782 gradeCalc1 0.9581 0.4149 2.309 Residual standard error: 11.299 on 98 degrees of freedom Multiple R-squared: 8.98, Adjusted R-squared: 8.531 F-statistic: 50 on 1 and 98 DF, p-value: 2.290764244961E-10 Max: 15.674 Pr(>It|) 0.1746 0.0105
For the provided data, develop a regression model for overall satisfaction as a function of years of service and department that has the largest R. Note that the categorical variable department has multiple levels and will require the use of multiple dummy variables. Which department, if any, has the highest impact on roduction satisfaction? Production Production E Click the icon to view the job satisfaction data. Determine the regression model for overall satisfaction as a function of years of service and department that has the largest R. Let "Administrative" be the baseline department, let X, represent Maintenance, let X, represent Management, let X, represent Production, let X, represent Quality Control, and let Xg represent Shipping / Receiving, coding each department variable with a 1 if the person is in that department and 0 otherwise. In addition, let Xe represent Years. Enter the terms of the equation so that the X-values are in ascending numerical order by base. Select the…
The amount of time adults spend watching television is closely monitored by firms because this helps to determine advertising pricing for commercials. Complete parts (a) through (d). (a) Do you think the variable "weekly time spent watching television" would be normally distributed? If not, what shape would you expect the variable to have? A. The variable "weekly time spent watching television" is likely normally distributed. B. The variable "weekly time spent watching television" is likely skewed right, not normally distributed. C. The variable "weekly time spent watching television" is likely symmetric, but not normally distributed. D. The variable "weekly time spent watching television" is likely skewed left, not normally distributed. E. The variable "weekly time spent watching television" is likely uniform, not normally distributed. (b) According to a certain survey, adults spend 2.35 hours per day watching television on a weekday. Assume that the standard deviation for "time spent…
Please help me better understand this problem. Researchers want to determine if heavier cars use more gasoline? To answer this question, a researcher randomly selected 15 cars. He collected their weight (in hundreds of pounds) and the mileage (MPG) for each car. From a scatterplot made with the data, a linear model seemed appropriate and the following output was obtained in photo below. Question: Determine the proportion of the variation in mileage is accounted for by the linear relationship with the weight of the car and also determine the estimate of the regression standard deviation.
Regression may not be an appropriate tool if a. there are two variables that are measured on an interval or ratio scale. b. multicollinearity is present c. you want to make predictions for one variable based on information about another variable d. the pattern of data points forms a reasonably straight line
lecture(11.12): A large amount of scatter in a scatter plot is an indication that the relationship between the two variables is (a) negative (b) strong (c) weak (d) moderate (e) positive
A. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
The National Football League (NFL) records a variety of performance data for individuals and teams. To investigate the importance of passing on the percentage of games won by a team, the folloving data show the average number of passing yards per attempt (Yds/Att) and the percentage of games won (WinPct) for a random sample of 10 NFL teams for the 2011 season (NFL vwebsite). Team Yds/Att WinPct Arizona Cardinals 6.9 44 Atlanta Falcons 8.0 69 Carolina Panthers 5.3 19 Chicago Bears 8.3 75 Dallas Covboys 5.4 26 New England Patriots 7.1 54 Philadelphia Eagles 8.4 79 Seattle Seahavwks 8.4 80 St. Louis Rams 6.1 33 Tampa Bay Buccaneers 7.8 70 a. Choose the correct a scatter diagram with the number of passing yards per attempt on the horizontal axis and the percentage of games won on the vertical axis. A. В. Win Pct WinPct! 80- 80 70 70 60 60 50 50 40 40 30- 30 20 20 10- 10- 80- 80 70 70 60 60 50- 50 40 40 30 30 20 20 10- 10- 6. 8. 9. 8. Yds/Att Yds/Att b. What does the scatter diagram…
Data were gathered and displayed on the scatter plot. 15 y 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Which of the following is the best equation to model the data? ŷ = 11.5(0.81)* Oŷ = 8.1(0.115)* Oŷ= -0.115x+8.1 Oŷ=-0.81x+11.5
Which is correct?

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

Q. 1. Fit a straight line trend by least square method from the following data : Value of X : 1 2 3 4 5 Value of Y : 3 5 8 10