Question

In: Statistics and Probability

Lets say Y=a+bX1+cX2+dX3+.......................up to n.....is the regression model (1) What would be the X1,X2,X3........Xn predictors for...

Lets say

Y=a+bX1+cX2+dX3+.......................up to n.....is the regression model

(1) What would be the X1,X2,X3........Xn predictors for house price in your neighborhood. Discuss from most important predictor to least important predictor.

(2) Similarly, What would be the predictors of your salary? Discuss in order.

Solutions

Expert Solution

(1)

For house price in our neighborhood, the predictors X1, X2, X3,..., Xn would be as follows arranged from most important predictor to least important predictor:

X1 = Size of the house

(increase of the facilities such as number of bed rooms, number of bath rooms, car porch, garden, play area for children etc.,will make the the house price increase)

X2 = Location of the house

(If the house is located in a convenient place connected with all requirements, house price will increase)

X3 = Demand for houses

(If the demand for houses increases faster than supply, house price goes up)

X4 = Interest rate

(if the interest rates are high, it will make house buying less attractive)

X5 = Economic Growth of the country

(If the economy grows, wages increase and so more people can afford to purchase a house, thus demand will increase and the house price will increase)

(2)

For our salary, the predictors X1, X2, X3,..., Xn would be as follows arranged from most important predictor to least important predictor:

X1 = Worker's capacity

If the worker is very efficient and highly qualified with specialized technical knowledge, his salary will be more.

X2 = Worker's age

If the worker has more experience, his salary will be more.

X3 = Hazardous work

If the work involves hazards, he will be paid more to compensate for the risks involved.

X4 = Profit of the company

If the company makes profits, salary of the workers will be high.

.X5 = Labor Union

(If a strong Labor Union is present in a company, it will exert prssure for high salary by bargaining with the management)

X6 = Cost of Living

(If the Cost of Living in a place is high, correspondingly the salary will also be high to equal the Consumer Price Index.

X7 = Government Legislation

(If the Government imposes conditions on minimum wage policy of the workers, their salary will increase.



Related Solutions

Lets say Y=a+bX1+cX2+dX3+.......................up to n.....is the regression model (1) What would be the X1,X2,X3........Xn predictors for...
Lets say Y=a+bX1+cX2+dX3+.......................up to n.....is the regression model (1) What would be the X1,X2,X3........Xn predictors for house price in your neighborhood. Discuss from most important predictor to least important predictor. (2) Similarly, What would be the predictors of your salary? Discuss in order.
Let X1, X2, X3, . . . be independently random variables such that Xn ∼ Bin(n,...
Let X1, X2, X3, . . . be independently random variables such that Xn ∼ Bin(n, 0.5) for n ≥ 1. Let N ∼ Geo(0.5) and assume it is independent of X1, X2, . . .. Further define T = XN . (a) Find E(T) and argue that T is short proper. (b) Find the pgf of T. (c) Use the pgf of T in (b) to find P(T = n) for n ≥ 0. (d) Use the pgf of...
In a multiple linear regression model with 2 predictors (X1and X2),                               &n
In a multiple linear regression model with 2 predictors (X1and X2),                                TRUE     or     FALSE In a multiple linear regression model with 2 predictors (X1and X2), then SSR(X1)+SSR(X2|X1) = SSTO–SSE(X1,X2)   TRUE     or    FALSE In a multiple linear regression model with 2 predictors (X1and X2), if X1and X2are uncorrelated, SSR(X1) = SSR(X1|X2).       TRUE     or    FALSE In a multiple linear regression model with 2 predictors (X1and X2), SSR(X1) + SSR(X2|X1) = SSR(X2) + SSR(X1|X2).       TRUE     or    FALSE In simple linear regression, then (X’X)-1is  2x2.    TRUE    or     FALSE In simple linear regression, the hat-matrix is 2x2.    TRUE    or     FALSE
Suppose we have a random sample of n observations {x1, x2, x3,…xn}. Consider the following estimator...
Suppose we have a random sample of n observations {x1, x2, x3,…xn}. Consider the following estimator of µx, the population mean. Z = 12x1 + 14x2 + 18x3 +…+ 12n-1xn−1 + 12nxn Verify that for a finite sample size, Z is a biased estimator. Recall that Bias(Z) = E(Z) − µx. Write down a formula for Bias(Z) as a function of n and µx. Is Z asymptotically unbiased? Explain. Use the fact that for 0 < r < 1, limn→∞i=1nri...
Let X1,X2,X3 be i.i.d. N(0,1) random variables. Suppose Y1 = X1 + X2 + X3, Y2...
Let X1,X2,X3 be i.i.d. N(0,1) random variables. Suppose Y1 = X1 + X2 + X3, Y2 = X1 −X2, Y3 =X1 −X3. Find the joint pdf of Y = (Y1,Y2,Y3)′ using : Multivariate normal distribution properties.
Let X = ( X1, X2, X3, ,,,, Xn ) is iid, f(x, a, b) =...
Let X = ( X1, X2, X3, ,,,, Xn ) is iid, f(x, a, b) = 1/ab * (x/a)^{(1-b)/b} 0 <= x <= a ,,,,, b < 1 then, find a two dimensional sufficient statistic for (a, b)
Let X1, X2, X3 be independent having N(0,1). Let Y1=(X1-X2)/√2, Y2=(X1+X2-2*X3)/√6, Y3=(X1+X2+X3)/√3. Find the joint pdf...
Let X1, X2, X3 be independent having N(0,1). Let Y1=(X1-X2)/√2, Y2=(X1+X2-2*X3)/√6, Y3=(X1+X2+X3)/√3. Find the joint pdf of Y1, Y2, Y3, and the marginal pdfs.
A regression model of the form y = beta0 + beta1 x1 + beta2 x2 +...
A regression model of the form y = beta0 + beta1 x1 + beta2 x2 + beta3 x3 + E was built using 20 observations. Partially completed regression output tables are provided below. What are the values of A, B, and C? Table 1 Statistic Value R-Square A Adjusted R-Square B Standard Error (RMSE) C n 20 Table 2 Source DF SS MS F P-Value Regression D 175 H J K Error E G I Total F 250 A regression...
Does the input requirement set V (y) = {(x1, x2, x3) | x1 + min {x2,...
Does the input requirement set V (y) = {(x1, x2, x3) | x1 + min {x2, x3} ≥ 3y, xi ≥ 0 ∀ i = 1, 2, 3} corresponds to a regular (closed and non-empty) input requirement set? Does the technology satisfies free disposal? Is the technology convex?
(1) z=ln(x^2+y^2), y=e^x. find ∂z/∂x and dz/dx. (2) f(x1, x2, x3) = x1^2*x2+3sqrt(x3), x1 = sqrt(x3),...
(1) z=ln(x^2+y^2), y=e^x. find ∂z/∂x and dz/dx. (2) f(x1, x2, x3) = x1^2*x2+3sqrt(x3), x1 = sqrt(x3), x2 = lnx3. find ∂f/∂x3, and df/dx3.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT