Recall:correlation - a measure of the strength and direction of the linear relationship between two variables.
causation - an action, occurrence, or event ______.
Example - heavier cars get the worst gas mileage
The idea is to understand that because there is a correlation (a strong relationship between two variables), that doesn't mean that the one causes the other.
Example: During the months of March and April, the weekly weight increases of a puppy in New York were collected. For the same time frame, the retail price increases of snowshoes in Alaska were collected. The data was examined and was found to have a very strong linear correlation.
So, this must mean that the weight increase of a puppy in New York is causing snowshoe prices in Alaska to increase, right?
“After I washed my car, it rained. Therefore washing my car causes rain.”
“When I got in the bath tub, the phone rang. Therefore getting in the bath will lead to the phone ringing.”
“We won our baseball game when I was wearing these socks, so it must be the lucky socks that caused our win.”
For example, eating breakfast has long been correlated with success in school for elementary school children. It would be easy to conclude that eating breakfast causes students to be better learners. It turns out, however, that those who don’t eat breakfast are also more likely to be absent or tardy — and it is absenteeism that is playing a significant role in their poor performance. When researchers retested the breakfast theory, they found that, independent of other factors, breakfast only helps undernourished children perform better.
The takeaways:
Just because two things ______doesn’t necessarily mean that ______
______.
Looking for correlations is one of the most frequently used techniques in science because it provide us with hypotheses we can test to find the true cause of what we’re investigating.
Recall:correlation - a measure of the strength and direction of the linear relationship between two variables.
causation - an action, occurrence, or event ______.
Example - heavier cars get the worst gas mileage
The idea is to understand that because there is a correlation (a strong relationship between two variables), that doesn't mean that the one causes the other.
Example: During the months of March and April, the weekly weight increases of a puppy in New York were collected. For the same time frame, the retail price increases of snowshoes in Alaska were collected. The data was examined and was found to have a very strong linear correlation.
So, this must mean that the weight increase of a puppy in New York is causing snowshoe prices in Alaska to increase, right?
“After I washed my car, it rained. Therefore washing my car causes rain.”
“When I got in the bath tub, the phone rang. Therefore getting in the bath will lead to the phone ringing.”
“We won our baseball game when I was wearing these socks, so it must be the lucky socks that caused our win.”
For example, eating breakfast has long been correlated with success in school for elementary school children. It would be easy to conclude that eating breakfast causes students to be better learners. It turns out, however, that those who don’t eat breakfast are also more likely to be absent or tardy — and it is absenteeism that is playing a significant role in their poor performance. When researchers retested the breakfast theory, they found that, independent of other factors, breakfast only helps undernourished children perform better.
The takeaways:
Just because two things ______doesn’t necessarily mean that ______
______.
Looking for correlations is one of the most frequently used techniques in science because it provide us with hypotheses we can test to find the true cause of what we’re investigating.
A survey of 50 vehicles in each of 10 cities was taken to measure the average noise pollution and the percentage of vehicles with silencers in their vehicles. The results are shown below.
1) The data represents
which type of
correlation?
A. Positive B. Negative
C. Weak D. Zero
2) Which statement can be concluded about the data?
a. There is causation between the number of vehicleswith silencers and the average noise pollution.
b. There is no correlation between the number of vehicleswith silencers and the average noise pollution.
c. More noise pollution temperatures causes people touse more silencers.
d. There is a correlation but not necessarily causation
between the number of vehicles with silencers and theaverage noise pollution.
3) David noticed that more silencers are used because of more noise pollution. This is incorrect for what reason?
A. The data shows no correlation.
B. The relationship could be related to outside factors, such as the cost of using silencers.
C. There is causation but no correlation between the variables.
D. The graph shows that lesser noise pollution cause few silencers to be used.
A survey of 50 vehicles in each of 10 cities was taken to measure the average noise pollution and the percentage of vehicles with silencers in their vehicles. The results are shown below.
1) The data represents
which type of
correlation?
A. Positive B. Negative
C. Weak D. Zero
2) Which statement can be concluded about the data?
a. There is causation between the number of vehicleswith silencers and the average noise pollution.
b. There is no correlation between the number of vehicleswith silencers and the average noise pollution.
c. More noise pollution temperatures causes people touse more silencers.
d. There is a correlation but not necessarily causation
between the number of vehicles with silencers and theaverage noise pollution.
3) David noticed that more silencers are used because of more noise pollution. This is incorrect for what reason?
A. The data shows no correlation.
B. The relationship could be related to outside factors, such as the cost of using silencers.
C. There is causation but no correlation between the variables.
D. The graph shows that lesser noise pollution cause few silencers to be used.