Suppose we have a data set with five features, X1 = GPA, X2 = IQ, X3 = Level (1 for College and 0 for High School),X4 = product between GPA and IQ, and X5 = product between GPA and Level. We want to predict student'sstarting salary after graduation (in thousands of dollars). Suppose we use least squares to fit a linear regression model,and estimate the parameters of the model as follows: \beta 0 = 50; \beta 1 = b. Explain the effect of the low values of \beta 2 and \beta 4 comparing to the high absolute values of \beta 1, \beta 3, and \beta 5. 20; \beta 2 = 0.07;3 35; \beta 4 = 0.01;\betaΣQ...

we have a data set with five features as X1 = GPA, X2= IQ, X3 = Level (1 for College and 0 for High…

Answered: Suppose we have a data set with five…

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

A statistics professor bought a new car for $35,000. For the next 5 years, she used several automotive web pages to estimate the value of the car. She then found the least‑squares regression line for the data to be y^ =35035.71−4142.86x where yy is the value of the car and xx is the years since she purchased it. Predict the value of her vehicle after 6 years.
The maintenance manager at a trucking company wants to build a regression model to forecast the time (in years) until the first engine overhaul based on four predictor variables: (1) annual miles driven (in 1,000s of miles), (2) average load weight (in tons), (3) average driving speed (in mph), and (4) oil change interval (in 1,000s of miles). Based on driver logs and onboard computers, data have been obtained for a sample of 25 trucks. A portion of the data is shown in the accompanying table. Time Miles Load Speed Oil 7.7 42.9 22.0 44.0 16.0 0.8 98.3 20.0 47.0 34.0 6.3 61.1 22.0 62.0 15.0 E Click here for the Excel Data File b. Estimate the regression model. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Time = Miles Load Speed oil + + d. What is the predicted time before the first engine overhaul for a particular truck driven 60,000 miles per year with an average load of 25 tons, an average driving speed of 53 mph, and 21,000 miles…
Heights (in centimeters) and weights (in kilograms) of 7 supermodels are given below. Find the regression equation, letting the first variable be the independent (x) variable, and predict the weight of a supermodel who is 171 cm tall. \begin{array}{c|ccccccc} \mbox{Height} & 174 & 166 & 176 & 176 & 178 & 172 & 172 \cr \hline \mbox{Weight} & 55 & 47 & 55 & 56 & 58 & 52 & 53 \cr \end{array} The regression equation is \hat{y} = + x . The best predicted weight of a supermodel who is 171 cm tall is
A researcher collected statistics on the sales amount of a product in 120 different markets and the advertising budgets used in TV, radio and newspaper media channels for each of these markets. The sales amount are expressed in 1000 units, and the budgets are expressed in 1000 $. The researcher wants to create a simple linear regression model by choosing one among the TV, radio and newspaper advertising budgets to explain the amount of sales.a) Which variable should this researcher choose as an independent variable to the simple regression model? Explain your decision by providing its statistical basis.(use formula)b) Construct the simple linear regression model using the argument you chose and write the equation of the model. Comment b0 and b1.(not excel use formula please)c) Test whether there is a statistically significant, linear relationship between the independent variable and the dependent variable by establishing the relevant hypotheses at the level of α = 0.05 significance.…
Explain about the least square regression line?
Please answer as many as your allowed too. Thank you :) A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.68b=0.168 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -1.417 1.417 0.702 -0.702 (c) If a country increases its life expectancy, the happiness index will increase decrease (d) If the life expectancy is increased by 0.5 years in a certain country, how much will the happiness index change? Round to two decimal places.(e) Use the regression line to predict the happiness index of a country with a life expectancy of 69 years. Round to two decimal places.
Construct the equation of the regression line. An editing firm compiled the following table which lists the number of pages contained in a piece of technical writing and the cost of proofreading and correcting them (in dollars). Assume there is a significant linear relationship between X and Y and construct the equation of the linear regression line. Number of Pages, x 7 12 4 14 25 30 Cost, y 128 213 75 250 446 540 a.) y^= 17.9(x) + 1.6 b.) y^= 7.1(x) + 15.4 c.) y^= 15.4(x) + 7.1 d.) y^= 1.6(x) +
A frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20 frequent 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: \hat y = 24000 + 10xy^=24000+10x. A person who spent $1000 is predicted to have flown:
A researcher wishes to examine the relationship between years of schooling completed and the number of pregnancies in young women. Her research discovers a linear relationship, and the least squares line is: ˆy=5−4xy^=5-4x where x is the number of years of schooling completed and y is the number of pregnancies. The slope of the regression line can be interpreted in the following way: When amount of schooling increases by one year, the number of pregnancies tends to decrease by 4. When amount of schooling increases by one year, the number of pregnancies tends to increase by 4. When amount of schooling increases by one year, the number of pregnancies tends to increase by 5. When amount of schooling increases by one year, the number of pregnancies tends to decrease by 5.
Students who are going to college have a cost per credit. The chart below is a cost of tuition cost increase over the years. A table of the yearly cost is listed below. A) Use technology (graphing calculator or excel) to determine the least squares regression line for the best fit line for the data given above. List the equation and R^2 Regression factor. B) Use your equation from technology to estimate how much college credits will cost in 2021
A grocery store manager did a study to look at the relationship between the amount of time (in minutes) customers spend in the store and the amount of money (in dollars) they spend. The results of the survey are shown below. Time 12 26 5 28 11 24 20 21 16 Money 37 84 34 84 53 92 72 92 75 The equation of the linear regression line is: ˆyy^ = + xx (Please show your answers to two decimal places) Use the model to predict the amount of money spent by a customer who spends 20 minutes at the store.Dollars spent = (Please round your answer to the nearest whole number.)
We wish to predict the salary for baseball players (y) using the variables RBI (x1) and HR (x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2y^=b0+b1x1+b2x2. HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars. The following is a chart of baseball players' salaries and statistics from 2016. Player Name RBI's HR's Salary (in millions) Adrian Beltre 104 32 18.000 Justin Smoak 34 14 3.900 Jean Segura 64 20 2.600 Justin Upton 87 31 22.125 Brandon Crawford 84 12 6.000 Curtis Granderson 59 30 16.000 Aaron Hill 38 10 12.000 Miquel Cabrera 108 38 28.050 Adrian Gonzalez 90 18 21.857 Jacoby Ellsbury 56 9 21.143 Mark Teixeira 44 15 23.125 Albert Pujols 119 31 25.000 Matt Wieters 66 17 15.800 Logan…

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