Answer:
The answer to your question is 19.2 feet.
Step-by-step explanation:
With the data given, we can see that we have a right triangle in the middle of the bigger one. Also, we can see that we have four triangles inside the biggest one, and all these triangles have the same size.
Then we can know the length of the three sides of:
BC = DE + DE = 3.2 + 3.2 = 6.4
AB = EF + EF = 4 + 4 = 8
AC = DF + DF = 2.4 + 2.4 = 4.8
Perimeter = BC + AB + AC = 6.4 + 8 + 4.8 = 19.2 feet
Answer:
m=0
Step-by-step explanation:
We simplify the equation to the form, which is simple to understand
93+12m=3(4m-1)+96
Reorder the terms in parentheses
93+12m=+(+12m-3)+96
Remove unnecessary parentheses
+93+12m=+12m-3+96
We move all terms containing m to the left and all other terms to the right.
+12m-12m=-3+96-93
We simplify left and right side of the equation.
m=0
Hope this helps!
Brain-List?
Answer:
B, an = 4n - 9
Step-by-step explanation:
The sequence is for every time the exponent on a goes up one, the number that it equals to goes up by 4. So to find the nth term, we at 4 to n. I hope this helped! :)
Answer:
Hence P values of beta becomes smaller(< 0.0001). and doest affect the mean response
Step-by-step explanation:
Given:
AS Predictor become more highly correlated .
To find:
Descriptive Nature of high correlated Predictor .
Solution:
A predictor is high correlated means:
1)It means that the two variables are strongly related to each other.
2)This is also called as problem of multicollinearity when two variables are
in Regression.
Effects when predictor are highly correlated ;
- <em>The estimated coefficient of one any one variable depends on the other predictor variable in model.</em>
- <em>Estimated coefficient of regression decrease as predictor variables are added.</em>
- <em>Hypothesis test Beta = zero gives different conclusion depending upon variable.</em>
- <em>High correlated of predictor variable does not provide good precision of predication of response in within model.</em>
In short ,Mulitcollinearity does not affect the mean response and new response of the model.
Hence P values of beta becomes smaller(< 0.0001). and doest affect the mean response
Answer:
0.91
Step-by-step explanation:
0.52/4=0.13
0.13*7=0.91