I’m trying to study for my Statistics course and I need some help to understand this question.
Peer 1 (William):
The two variables I choose to test to see if they are correlated are price of the car and horsepower. I chose these two variables because I suspect that cars with more horsepower cost more to make. That being said, I believe the relationship will be a strong positive correlation. The horsepower is the independent variable and the price of the car is the dependent variable. This is because the price of the car should be dependent upon the amount of horsepower.
When I entered in the information to find the correlations between price and horsepower i got 0.9348. R-squared equals 87.39%. After running the regressions test I received .866395 for R squared. Not sure why the numbers were different, could someone help me out with this?
An r squared value of 87.39% means a strong positive correlation. This proves that the two numbers are related to each other. When one number increases the other also increases.
Peer 2 (Joni):
I chose the mileage and the price for the two variables. I feel that they are correlated because the higher the mileage a vehicle has the lower the price will be. As the mileage increases, the price decreases. I suspect that the relationship would be a negative one because the vehicles chosen were not of the same type so there is a huge gap in the price. The price of the car depends on how much miles it has on it. To test my assumption of the relationship, I use the formula:
=CORREL(price of the 10 cars, mileage of 10 cars) which equals to -0.52906. My assumption was correct in that the mileage and price had a negative relationship.
Next, I will conduct a regression test in data analysis tool pack. The r squared value is 0.279907151 or 27.99% which tells me that it is a low correlation.
p-value = 0.115840399>.05 therefore the mileage is not a significant to price of the vehicle.