P = 2w + 2l where w is the width, and l is the length. We know the length (l) is 3 inches longer than it's width (or l = w+3). Substitute w +3 into Perimeter equation. P = 2w + 2(w+3).
Answer: -25
Step-by-step explanation:
14-43+4
Answer:
0.36
Step-by-step explanation:
all you have to do is multiply them
Answer:
x= log120/7log3
Step-by-step explanation:
7x=log3 120
7x= log120/log3/7
x= log120/7log3
Answer:
The OLS estimator of the slope <em>β</em>₁ is 1.22.
Step-by-step explanation:
The OLS regression equation to estimate the relationship between people's weight (<em>W</em>) and the number of times they eat out in a month (<em>EO</em>) is:
![W=\beta_{0}+\beta_{1} EO_{i}+u_{i}](https://tex.z-dn.net/?f=W%3D%5Cbeta_%7B0%7D%2B%5Cbeta_%7B1%7D%20EO_%7Bi%7D%2Bu_%7Bi%7D)
The information provided is:
![Cov (W, EO)=4.94\\V(EO)=4.04\\E(W)=43.82\\E(EO)=2.46](https://tex.z-dn.net/?f=Cov%20%28W%2C%20EO%29%3D4.94%5C%5CV%28EO%29%3D4.04%5C%5CE%28W%29%3D43.82%5C%5CE%28EO%29%3D2.46)
The formula to compute the OLS estimator of slope coefficient <em>β</em>₁ is:
![\hat \beta_{1}=\frac{Cov(W, EO)}{V(EO)}](https://tex.z-dn.net/?f=%5Chat%20%5Cbeta_%7B1%7D%3D%5Cfrac%7BCov%28W%2C%20EO%29%7D%7BV%28EO%29%7D)
Compute the OLS estimator of slope coefficient <em>β</em>₁ as follows:
![\hat \beta_{1}=\frac{Cov(W, EO)}{V(EO)}=\frac{4.94}{4.04}=1.22277\approx1.22](https://tex.z-dn.net/?f=%5Chat%20%5Cbeta_%7B1%7D%3D%5Cfrac%7BCov%28W%2C%20EO%29%7D%7BV%28EO%29%7D%3D%5Cfrac%7B4.94%7D%7B4.04%7D%3D1.22277%5Capprox1.22)
Thus, the OLS estimator of the slope <em>β</em>₁ is 1.22.