A multiple linear regression model of the following form is fitted to a data set Yi = β0 + β1xi1 + β2xi2 + εi , εi ∼ N(0, σ2 )i. i.d. The model is fitted using SAS and the following output is obtained: Sum of mean Source DF Squares Square F Value Pr> F Model 2 28.119 14.059 ? 0.0039 Error 3 0.715 ? Corrected Total 5 28.833 Parameter Standard Variable Estimate Error t Value P r > |t| Intercept 2.105 0.422 4.99 0.0155 x1 1.242 0.119 10.42 0.0019 x2 -0.195 0.439 ? ? (a) Find the sample size. (b) Find mean squared errors (MSE). (c) Find R2 . (d) Find the value for the F-test statistic. (e) What hypotheses the above F test for? Given α = 0.05, what conclusion will you have? (f) One wants to test H0 : β2 = 0 vs Ha : β2 6= 0. Find the value of test statistic and make your conclusion at α = .05.

8

step-by-step explanation:

this is a subtraction problem.

10 - 2 = 8

it will cost dollar $540

the living room is 3 which means if we divide 325/3 then we get $108

therefore if 1part of the living room cost $108 and 3 costed $325

then total 5part of the room will cost 5*108 which gives $540

a 4/6 = 9/12   not proportional

step-by-step explanation:

to find which are not proportional, we can use cross products

if it is true, they are proportional.   if it is not true, they are not proportional.

a 4/6 = 9/12

4* 12 = 6*9

48 = 54

not this is not proportional  

b 2/3 = 12/18  

2* 18 = 3 * 12

36 = 36

this is proportional

c 5/4 = 10/8  

5*8 = 4*10

40 = 40

this is proportional

d 7/5 = 35/25

7*25 = 5 * 35

175 = 175

is proportional

yeah i know the problem you need. i am taking it right now. the answer is step 3

~mickey