What do you learn about Salesperson age after performing each of the correlation and regression analyses in the prior questions? Although a correlation by itself may not tell you the whole story, it's at least enough to establish causality A correlation analysis is usually enough to tell you what you need to know Even if a correlation is substantial, you don't know for sure until you measure it in a regression model with several other relevant variables If the correlation is negative, the relationship is not likely to be significant

The Salesperson age after performing each of the correlation and regression analyses in the prior questions shows that:

Even if a correlation is substantial, you don't know for sure until you measure it in a regression model with several other relevant variables.

Step-by-step explanation:

The simple difference between correlation and causation is that a correlation measures the linear relationship between two variables, while causation is a statistical measure that establishes that one event or variable is the cause of another event or variable.  With correlation established, one needs to study the strength of the relationship through a regression analysis before concluding on how one variable affects another variable.

answer: we have from thales theorem that because the 2 triangles are similar, jm/jk=jn/jl. we have that jn=x, jl=x+24 and that jm=7 and jk=28. substituting, we get:

x/(x+24)=7/28.

hence, x/(x+24)=1/4. this leads to 4x=x+24, 3x=24, x=8. we have solved for x=jn and this is equal to 8.

step-by-step explanation:

answer:

step-by-step explanation:

here, the total dishes = 10

dishes having meat = 3 ( chicken, meatloaf and hamburger )

thus, the probability of having meat on the chosen dishes = number of dishes having meat / total dishes

=

therefore, the probability of not having meat = 1- the probability of having meat on the chosen dishes

=

=

=

⇒ option b is correct.