Answer:
a) ρ (X,Y) = 0.125
b) The linear relationship is weak because ρ (X,Y) is almost 0
c) There is a positive linear relationship because ρ (X,Y) > 0
Step-by-step explanation:
The Pearson correlation coefficient is calculate as :
ρ (X,Y) = (σXY) / (σX)(σY)
-1 ≤ ρ ≤ 1
When ρ = 0 there is no linear relationship between the variables. This does not mean that the variables are independent.
When ρ = 1 the linear relationship is perfect positive. ρ = 1 means a direct relation between X and Y : When X increases also Y increases in constant proportion. When Y increases also X increases in constant proportion.
When ρ = -1 the linear relationship is perfect negative. ρ = -1 means a inverse relation between X and Y : When X increases Y decreases in constant proportion. When Y increases X decreases in constant proportion.
In our exercise ρ (X,Y) = 13.44 / (15.57)(6.90) = 0.125
a) ρ (X,Y) = 0.125
b) The linear relationship is weak because ρ (X,Y) is almost 0
c) There is a positive linear relationship because ρ (X,Y) > 0
Answer:
m - n - 4
Step-by-step explanation:
9m + 4n - 1 - 6m - (n + 3) - (2m + 4n)
= m - n - 4
A correlation, strong or moderate, does not indicate causation.
A negative correlation means that when a variable increase the other decrease.
Discovering lurking variables is no that easy, you are supposed to understand the data and the variables that can affect the process.
The fact that the correlation is moderate makes is difficult to tell if the relation is meaninful to indicate a lurking variable.
What could affet the purcahses of a computer inversely than it affects the purchases of microwaves. It could be a temporary thing or a situation of a particular region.
You should not tell that the correlation is most likely a coincidence because while there is a moderate correlation you have to explore until finding the lurking variables or that the correlation dissapears.
So the right answer is the first option. The correlation is most likely due to a lurking variable.