1. Statistics Dealing With Uncertainty

2. Objectives Describe the difference between a sample and a population Learn to use descriptive statistics (data sorting, central tendency, etc.) Learn how to prepare and interpret histograms State what is meant by normal distribution and standard normal distribution. Use Z-tables to compute probability.

3. Statistics “There are lies, d#$& lies, and then there’s statistics.” Mark Twain

4. Statistics is... a standard method for... - collecting, organizing, summarizing, presenting, and analyzing data - drawing conclusions - making decisions based upon the analyses of these data. used extensively by engineers (e.g., quality control)

5. Populations and Samples Population - complete set of all of the possible instances of a particular object e.g., the entire class Sample - subset of the population e.g., a team We use samples to draw conclusions about the parent population.

6. Why use samples? The population may be large all people on earth, all stars in the sky. The population may be dangerous to observe automobile wrecks, explosions, etc. The population may be difficult to measure subatomic particles. Measurement may destroy sample bolt strength

7. Team Exercise: Sample Bias To three significant figures, estimate the average age of the class based upon your team. When would a team not be a representative sample of the class?

8. Measures of Central Tendency If you wish to describe a population (or a sample) with a single number, what do you use? Mean - the arithmetic average Mode - most likely (most common) value. Median - “middle” of the data set.

9. What is the Mean? The mean is the sum of all data values divided by the number of values.

10. Sample Mean Where: is the sample mean x i are the data points n is the sample size

11. Population Mean Where: μ is the population mean x i are the data points N is the total number of observations in the population

12. What is the Mode? mode - the value that occurs the most often in discrete data (or data that have been grouped into discrete intervals) Example, students in this class are most likely to get a grade of B.

13. Mode continued Example of a grade distribution with mean C, mode B

14. What is the Median? Median - for sorted data, the median is the middle value (for an odd number of points) or the average of the two middle values (for an even number of points). useful to characterize data sets with a few extreme values that would distort the mean (e.g., house price,family incomes).

15. What Is the Range? Range - the difference between the lowest and highest values in the set. Example, driving time to Houston is 2 hours +/- 15 minutes. Therefore... Minimum = 105 min Maximum = 135 minutes Range = 30 minutes

16. Standard Deviation Gives a unique and unbiased estimate of the scatter in the data.

17. Standard Deviation Population Sample Deviation Variance =  2 Variance = s 2

18. The Subtle Difference Between  and σ N versus n-1 n-1 is needed to get a better estimate of the population  from the sample s. Note: for large n, the difference is trivial.

19. A Valuable Tool Gauss invented standard deviation circa 1700 to explain the error observed in measured star positions. Today it is used in everything from quality control to measuring financial risk.

20. Team Exercise In your team’s bag of M&M candies, count the number of candies for each color the total number of candies in the bag When you are done counting, have a representative from your team enter your data on the board Using Excel, enter the data gathered by the entire class More

21. Team Exercise (con’t) For each color, and the total number of candies, determine the following: maximummode minimummedian rangestandard deviation meanvariance

22. Individual Exercise: Histograms Flip a coin EXACTLY ten times. Count the number of heads YOU get. Report your result to the instructor who will post all the results on the board Open Excel Using the data from the entire class, create bar graphs showing the number of classmates who get one head, two heads, three heads, etc.

23. Data Distributions The “shape” of the data is described by its frequency histogram. Data that behaves “normally” exhibit a “bell-shaped” curve, or the “normal” distribution. Gauss found that star position errors tended to follow a “normal” distribution.

24. The Normal Distribution The normal distribution is sometimes called the “Gauss” curve. mean x RF Relative Frequency

25. Standard Normal Distribution Define: Then Area = 1.00 z

26. Some handy things to know. 50% of the area lies on each side of the mid-point for any normal curve. A standard normal distribution (SND) has a total area of 1.00. “z-Tables” show the area under the standard normal distribution, and can be used to find the area between any two points on the z-axis.

27. Using Z Tables (Appendix C, p. 624) Question: Find the area between z= -1.0 and z= 2.0 From table, for z = 1.0, area = 0.3413 By symmetry, for z = -1.0, area = 0.3413 From table, for z= 2.0, area = 0.4772 Total area = 0.3413 + 0.4772 = 0.8185 “Tails” area = 1.0 - 0.8185 = 0.1815

28. “Quick and Dirty” Estimates of  and    (lowest + 4*mode + highest)/6 For a standard normal curve, 99.7% of the area is contained within ± 3  from the mean. Define “highest” =  Define “lowest” =  Therefore,   (highest - lowest)/6

29. Example: Drive time to Houston Lowest = 1 h Most likely = 2 h Highest = 4 h (including a flat tire, etc.)  = (1+4*2+4)/6 = 2.16 (2 h 12 min)  = (4 - 1)/6= 0.5 h This technique (Delphi) was used to plan the moon flights.

30. Team Exercise You want to put a scale on your rubber-band car to relate a given scale setting and an expected distance traveled. Design an experiment to establish a scale for your car. More

31. Team Exercise continued. Some Issues to consider: Sample size Range of distances Desired accuracy

32. Review Central tendency mean mode median Scatter range variance standard deviation Normal Distribution