Answer:
option 4 (2,-15)
Step-by-step explanation:
For this case we have that by definition, the area of a triangle is given by:
Where:
b: It is the base of the triangle
h: It is the height of the triangle
According to the statement data we have:
Substituting we have:
We divide between 2 on both sides:
We factor by looking for two numbers that, when multiplied, are obtained -88 and when added together, +3 is obtained.
These numbers are +11 and -8.
We have two roots:
We choose the positive value.
Thus, the base of the triangle is:
Answer:
The base of the triangle is 22 units.
Answer:
i SEE NO ONE ANSWERED THIS QUESTION SO IM GONNA ANSWER its 1/2
Step-by-step explanation:
B
This can't work because if there are 60 cars that means there are 240 car wheels and then 25 bikes means 50 bike wheels for a total of 290 wheels, if there was 1 more car then it would be 86 cars and motorcycles with 294 wheels or 2 more bikes would mean 87 cars and motorcycles with 294 wheels
For a set of data: x = (0,1,2,3,4,5,6) and y=(36, 28, 25, 24, 23, 21, 19), is it wise to use a linear regression to extrapolate
melisa1 [442]
Answer:
The problem with this solution is that a regression model is not recommended to extrapolate because we do not know if the linear relation that we calculated for a specific range of x values still holds outside this range.
Step-by-step explanation:
We have a linear regression model, with a range of the independent variable "x" that goes from 0 to 6.
The regression model finds a good fit (r=0.8582).
As it has a good fit, it is proposed to use this model to extrapolate and calculate the value of y for x=50.
It is not recommended to extrapolate a regression model unless we are really sure that the model is still valid within the range within we are extrapolating.
This means that if we have no proof that y has a linear relation in a range of x that includes x, the extrapolation has no validity and can lead to serious errors.
A linear regression model is only suitable for interpolation or extrapolating within the range we are sure that the relation between y and x is linear within a certain acceptable error.
