1. a) What is the probability for a male to want bowling to the total number of students? b) Given that a student is female, what is the probability that she will want a skating party? c) If the sample size is increased to 1000, how many students would prefer dancing?

2. Linear Regression

3. Correlation Correlation describes the relationship between two variables. EX: How much you study verse how well you do on a test. We can say that these two variables have a relationship, therefore they are correlated.

4. Positive Correlation POSITIVE Correlation: as one variable increases, the other variable increases. Ex: The more you study, the higher your grade on a test

5. Negative Correlation Negative Correlation: as one variable increases, the other variable decreases. Ex: The more you sleep in class, the lower your grade is.

6. No Correlation No Correlation: When two variables are not related at all. Ex: How tall you are, and how high your grade is in English class.

7. Correlation Describe the following as POSITIVE, NEGATIVE or NO Correlation. 1. Money you spend, Money in your savings 2. Hours you work, how much you get paid 1. Amount of Music you listen to, How much you eat.

8. RECALL: Linear Regression To enter the data: STAT  EDIT L 1 is independent variable; L 2 is dependent variable Calculator Steps: STAT  CALC 4: LinReg  VARS  Y-VARS ENTER 3 Times

9. Correlation Coefficient Correlation Coefficient: (r) is a number that tells how well a line of best fit, fits the data. - Closer to 1 or -1, the stronger the relationship - If r is close to 1, then positive correlation -If r is close to -1, then negative correlation -If r is closer to 0, then no correlation R only applies for LINEAR MODELS!!!!

10. Turning on Correlation Coefficient Calculator commands: 2 nd  Zero  DiagnosticOn  Enter

11. Find the Linear Model for the Following. \ Look at the r value, what does it say about the data?

12. Find the Linear Model for the Following. Look at the r value, what does it say about the data?

13. Find the Linear Model for the Following. Look at the r value, what does it say about the data? Theater tickets sales on successive nights.