Answer:
Step-by-step explanation:
For the random variable 
we define the possible values for this variable on this case ![[1,2,3,4,5]](https://tex.z-dn.net/?f=%20%5B1%2C2%2C3%2C4%2C5%5D)
. We know that we have 2 defective transistors so then we have 5C2 (where C means combinatory) ways to select or permute the transistors in order to detect the first defective:
We want the first detective transistor on the ath place, so then the first a-1 places are non defective transistors, so then we can define the probability for the random variable 
like this:
For the distribution of 
we need to take in count that we are finding a conditional distribution. given 
, for this case we see that ![N_2 \in [1,2,...,5-a]](https://tex.z-dn.net/?f=%20N_2%20%5Cin%20%5B1%2C2%2C...%2C5-a%5D)
, so then exist 
ways to reorder the remaining transistors. And if we want b additional steps to obtain a second defective transistor we have the following probability defined:
And if we want to find the joint probability we just need to do this:
And if we multiply the probabilities founded we got:
which of the two landscapes to hire each landscapes charger an hour rate plus a fee for each job the result of the equator into 20 $20 for 5 hours both landscape charge the same hour rate but not the same fee per job give me your answer
The residual value comes out to be 2.94 cm and height is 157.06 cm
<u>Explanation:</u>
The regression equation is calculated at the first step.
height = 105.08 plus 2.599 multiply foot length
At foot length = 20cm, height = 105.08 plus 2.599 multiply 20
= 157.06 cm
Residual = Actual minus predicted value = 160 minus 157.06
=2.94 cm
B) The residual standard deviation generally gives a sense of the goodness of fit of goodness of regression equation on our data. The magnitude tells us that how much will be predicted values from model will vary from actual values. the linear model is justified.
Answer:
7.25 each or 7.00 if rounded
Step-by-step explanation:
