Question

Assume Y= salary, X1 = years of education and X2 = years of experience. Write the...

Assume Y= salary, X1 = years of education and X2 = years of experience. Write the multivariate regression equation showing that everyone will make $30K regardless of education or experience, will make $1K less for every year of education, and $5K more for every year of experience.

Homework Answers

Answer #1

We have dependent variable = Y(salary)

and independent variables are X1(years of education) and X2(years of experience)

It is given that everyone will make $30K regardless of education or experience, so intercept of equation is 30,000 or 30k

It is given that everyoone will make $1K less for every year of education, so negative slope for X1 or -1000 for X1

It is given that everyoone will make $5K more for every year of experience, so positive slope for X2 or 5000 for X2

We know that the general equation is given as

Y = A + BX1 + CX2

where A is intercept = 30,0000, B is slope constant for X1 = -1000 and C is slope constant for X2 = 5000

setting the given values, we get

Salary = 30,000 -1,000(Years of education) + 5,000(years of experience)

This is the required multivariate regression equation

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Let us consider the salary (y), the years of experience(X1) and the years of schooling (X2)...
Let us consider the salary (y), the years of experience(X1) and the years of schooling (X2) for 50 employees of the Georgia Pacific Company in Crossett, Arkansas. Our goal is to build a model that will satisfy all of our assumptions, so that we can use years of experience and years of schooling to predict salaries. The data is presented below. X1 X2 Y X1 X2 Y X1 X2 Y 7 12 26075 21 12 43268 28 16 99139 28...
Consider the following estimated regression model relating annual salary to years of education and work experience....
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=10,815.11+2563.46(Education)+897.49(Experience) . Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree (10 years of education). How much more money would we expect the employee with a master's degree to make?
Consider the following estimated regression model relating annual salary to years of education and work experience....
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=11,756.80+2723.3(Education)+1092.64(Experience) Suppose an employee with 11 years of education has been with the company for 2 years (note that education years are the number of years after  8th grade). According to this model, what is his estimated annual salary?
At a large company, the salaries (y, in thousands of dollars) and years of experience (x)...
At a large company, the salaries (y, in thousands of dollars) and years of experience (x) of six randomly chosen engineers are: x (years) 6 7 9 10 13 15 y (salary) 44 41 43 45 51 49 Compute the least squares regression equation and use that equation to predict the salary (in thousands of dollars) of a new employee with 8 years of experience,
Individual Bettendorf Salary Experience (X1) Education (X2) Sex (X3) 1 53600 5.5 4.0 F 2 52500...
Individual Bettendorf Salary Experience (X1) Education (X2) Sex (X3) 1 53600 5.5 4.0 F 2 52500 9.0 4.0 M 3 58900 4.0 5.0 F 4 59000 8.0 4.0 M 5 57500 9.5 5.0 M 6 55500 3.0 4.0 F 7 56000 7.0 3.0 F 8 52700 1.5 4.5 F 9 65000 8.5 5.0 M 10 60000 7.5 6.0 F 11 56000 9.5 2.0 M 12 54900 6.0 2.0 F 13 55000 2.5 4.0 M 14 60500 1.5 4.5 M 1....
Consider the following computer output of a multiple regression analysis relating annual salary to years of...
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7345 R Square 0.5395 Adjusted R Square 0.5195 Standard Error 2134.9715 Observations 49 ANOVA df SS MS F Significance F Regression 2 245,644,973.9500 122,822,486.9750 26.9460 1.8E-08 Residual 46        209,672,760.0092 4,558,103.4785 Total 48 455,317,733.9592 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 14271.51879 2,525.5672 5.6508 0.000000963 9187.8157 19,355.2219 Education (Years) 2351.3035...
Consider the following computer output of a multiple regression analysis relating annual salary to years of...
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7338 R Square 0.5384 Adjusted R Square 0.5183 Standard Error 2139.0907 Observations 49 ANOVA df SS MS F Significance F Regression 2 245,472,093.5833 122,736,046.7917 26.8234 1.9E-08 Residual 46 210,482,624.6208 4,575,709.2309 Total 48 455,954,718.2041 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 14275.75637 2,530.4400 5.6416 0.000000994 9182.2448 19,369.2679 Education (Years) 2350.2675 338.3625...
10. In Exercise 6, we examined the relationship between years of education and hours of television...
10. In Exercise 6, we examined the relationship between years of education and hours of television watched per day. We saw that as education increases, hours of television viewing decreases. The number of children a family has could also affect how much television is viewed per day. Having children may lead to more shared and supervised viewing and thus increases the number of viewing hours. The following SPSS output displays the relationship between television viewing (measured in hours per day)...
Salesperson Years of Experience Annual Sales ($1000s) 1 1 85 2 4 100 3 4 91...
Salesperson Years of Experience Annual Sales ($1000s) 1 1 85 2 4 100 3 4 91 4 4 97 5 6 107 6 9 109 7 10 118 8 11 126 9 10 113 10 14 132 Compute b1 and b0 (to 1 decimal). b1 = b0 = Complete the estimated regression equation (to 1 decimal). y = ... + x According to this model, what is the change in annual sales ($1000s) for every year of experience (to 1...
Please use R or Rstudio for this exercise and show everything, including the R output. Pay...
Please use R or Rstudio for this exercise and show everything, including the R output. Pay attention in everything in Bold, please. " The data in stat5_prob2 contains values of the following four variables for 93 employees of Harris Bank Chicago in 1977: • y : beginning salary in dollars (SALARY) • x1 : years of schooling at the time of hire (EDU) • x2 : number of months of previous work experience (EXPER) • x3 : number of months...