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Interpreting Correlation

Interpreting Correlation.

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Interpreting Correlation

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  1. Interpreting Correlation

  2. So far we have looked at finding the correlation for different sets of values. We have used the ____________________, __________________, and ____________ looking at scatter plots and contingency tables. Today we will look at what correlations mean and what they tell us. Median-median line method Mayer line method estimation

  3. Math Mark Height (cm) 0 Let’s look at some examples: If there is no correlation, that means there is ___________________ between the variables. no relationship This makes sense because your math mark doesn’t depend on your height, and your height doesn’t depend on your math mark

  4. Weight (lbs) Pasta eaten at dinner (g) 0 If there is a weak/moderate correlation, that means there is_______________ of a relationship between the variables, but it is ________________________. somewhat not very reliable We can see that weight is loosely related to how much pasta you eat at dinner, but we couldn’t make a very accurate prediction using the relationship

  5. Length of foot (cm) Length of hand (cm) 0 If there is a strong correlation, we know that one variable is ______________________ to the other and we can make a ________________________________. strongly related fairly accurate prediction We can see the length of feet and hands on a person are closely related. We can use this information to make a good prediction of what one length will be, when we are given the other length measurement

  6. Amount Paid ($) Hours Worked 0 If there is a perfect correlation, we know one variable is __________________ on the other and we can make a ________________________________. completely dependent prediction that will be exact We can see you will get paid depending on the number of hours you work. We can figure out exactly how much you will get paid if we know how long you worked (and vice versa).

  7. We need to be careful though, because sometimes what we see can be misleading; it is important to look at what the variables are and think about the situation. Here are some examples of reasons a correlation might seem like it means one thing, when really there is something else going on:

  8. It may seem that there is a strong correlation between the number of ice cream cones and air conditioners sold in the summer, but really these depend on a third variable – temperature. Between the ages of 5-10, there is a linear correlation between age and height. However, before and after this time the correlation is not linear. When finding the population density of a type of insect it would be easy to miscount the insects, by missing some or counting some more than once. Strong correlation indicates the existence of a link, but it does not explain the reason or nature of the link. Be careful to examine the situation and use your best judgement.

  9. Homework: p. 256 #1-3 Come to next class ready for review – bring topics that you would like to go over before the test

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