Answer:
It is measured using a measure called variance inflation factor (VIF).
Step-by-step explanation:
This is the statement that is true about multi-collinearity. Multi-collinearity is a situation that can sometimes arise when dealing with a multiple regression model. Multicollinearity refers to the occurrence of high intercorrelations among various independent variables. This can often lead to results that are skewed or misleading. The main way in which multi-collinearity can be measured is by using the variance inflation factor (IVF). This allows us to quantify the severity of multi-collinearity.
A) maximum mean weight of passengers = <span>load limit ÷ number of passengers
</span><span>
maximum mean weight of passengers = 3750 </span>÷ 25 = <span>150lb
</span>B) First, find the z-score:
z = (value - mean) / stdev
= (150 - 199) / 41
= -1.20
We need to find P(z > -1.20) = 1 - P(z < -1.20)
Now, look at a standard normal table to find <span>P(z < -1.20) = 0.11507, therefore:
</span>P(z > -1.20) = 1 - <span>0.11507 = 0.8849
Hence, <span>the probability that the mean weight of 25 randomly selected skiers exceeds 150lb is about 88.5%</span> </span>
C) With only 20 passengers, the new maximum mean weight of passengers = 3750 ÷ 20 = <span>187.5lb
Let's repeat the steps of point B)
z = (187.5 - 199) / 41
= -0.29
P(z > -0.29) = 1 - P(z < -0.29) = 1 - 0.3859 = 0.6141
</span>Hence, <span>the probability that the mean weight of 20 randomly selected skiers exceeds 187.5lb is about 61.4%
D) The mean weight of skiers is 199lb, therefore:
number</span> of passengers = <span>load limit ÷ <span>mean weight of passengers
= 3750 </span></span><span>÷ 199
= 18.8
The new capacity of 20 skiers is safer than 25 skiers, but we cannot consider it safe enough, since the maximum capacity should be of 18 skiers.</span>
Answer:
63
Step-by-step explanation:
Step-by-step explanation:
part A
y=nx
part B
y=48×x
y=48x