1. Looking Back! A review of the first 4 chapters

2. Quick Survey…

3. The “Monty Hall” Paradox On “Let’s Make a Deal” a contestant is told that behind one of the 3 doors is a new, 2007 Mazda Miata. Behind the other 2 are lovely goats! You get what you choose! You are asked to select a door. The game show host then reveals a door behind which is one of the goats. Question: Should the contestant change his or her initial choice?

4. Conditional Probability Formula Approach: Useful if you are given the information in the “right form” “Reason it Out” method is often faster depending on information at hand examples…

5. Example 1: 30% of police trainees pass both of their training exams and 65% passed their first exam. How many who passed the 1 st exam also pass the 2 nd exam?

6. Solution – Reason it Out Method For the sake of argument assume 100 trainees took the exam: –30 passed both exams (30%) –65 passed the 1 st exam so 30 these were the ones who also passed the 2 nd exam too –30/65 = the percent who also passed the 2 nd exam –Conditional probability is 0.46

7. Solution – Formula Method Identify what the “conditional” is. Re-state as: Find the probability of passing the 2 nd exam given that you passed the 1 st exam P(A and B) = passing both = 0.30 P(A) = probability of passing 1 st = 0.65 B A

8. Final comments on conditional probabilities… Try to re-phrase the information in the P(B|A) form Clearly identify what information you have – use either the “reasoning” or formula method Question: Is P(A|B) = P(B|A)? Return to main menu

9. Scatter plots and Models Sometimes transforming the explanatory variable can “straighten” a scatter plot You can “straighten” by: –Raising explanatory variable to a powerRaising explanatory variable to a power –Taking the log of bothTaking the log of both –Taking the log of the responding variableTaking the log of the responding variable Regression line provides a model that links an explanatory to a responding variable Return to main menu

10. Example.. In the Physics Lab Students measure the distance and object falls as a function of time… Get Excel spreadhseet of this

11. This can be linearized by plotting distance wrt time 2

12. A Cubic relation… return

13. Why do we need this? Logarithms flatten AND compress data Example…Question 2.105

14. By taking logs of both variable the scatterplot looks like this! return

16. Spread and Central Tendency Means, medians and modes are all different ways to evaluate the central tendency in data Variance and standard deviation measure scatter Interquartile spread and 1.5xIQR help detect outliers Normal distribution is a “key” distribution

17. The “Rule” Normally distributed data has the following critical property: The 68-95-99.7 Rule

18. Z-Scores and the Standard Normal Distribution All normal distributions share the same shape A simple linear transformation can convert any normal distribution to the Standard Form This gives us the concept of the Z-Score The 68-95-99.7 rule applies here and can give us a deeper insight into what a z-score means Converting to Z-scores allows you to use Table A (inside cover of book)

19. Using z-scores… Z If the z-score for a data Point is 1 then this means That 84.13% of the samples In the population are less than The value of this data point How do you interpret a z-score of -1.71?

20. Who’s the Greatest? Ty Cobb batted 0.420 In 1911 N(0.266,0.0371) Ted Williams batted 0.406 in 1941 N(0.267,0.0326) George Brett batted 0.390 in 1980 N(0.261,0.0317) z = 4.15 z = 4.26 z = 4.07 Return to main menu