1. Introduction to the Practice of Statistics Fifth Edition Chapter 1: Looking at Data—Distributions Copyright © 2005 by W. H. Freeman and Company Modifications and Additions by M. Leigh Lunsford, 2005- 2006 David S. Moore George P. McCabe

2. Class Website www.IntroStats.blogspot.com Policies and Syllabus doc Software Homework assignments, test announcements, lecture slides, etc. Continually updated - check it regularly!

3. Technology Requirements MegaStat Plugin for Excel (website) Data Sets in Excel Format on CD (CD accompanying text) TI-83

4. The Science of Learning from Data The Collection and Analysis of Data What is Statistics?? Experimental Design Chapter 3 Descriptive Statistics (Data Exploration) Chapters 1, 2 Inferential Statistics Chapters 5 - 8 Probability Chapter 4

5. Chapter 1 - Looking at Data 1.1 Displaying Distributions with Graphs 1.2 Describing Distributions with Numbers 1.3 Density Curves and Normal Distributions

6. Section 1.1 Displaying Distributions with Graphs

7. Data Basics

8. Variable Types

9. An Example (p. 5)

10. Graphs for Categorical Vars. Bar Graphs Pie Charts Educational Level Example (page 7): –A Bar Graph by Hand –A Pie Chart by Hand Homework: Try to do these in Excel!

13. Graphs for Quantitative Data Stemplots (Stem and Leaf Plots) –Generally for small data sets Histograms Time Plots (if applicable) Let’s look at an example to see what types of questions one may ask and how these plots help to visualize the answers!

14. Example 1.7 Page 14 Descriptive and Inferential Stats 1.What percent of the 60 randomly chosen fifth grade students have an IQ score of at least 120? 2.Based on this data, approximately what percent of all fifth grade students have an IQ score of at least 120? 3.What is the average IQ score of the fifth grade students in this sample? 4.Based on this data, what is the average IQ score of all fifth grade students (i.e. the population) from which the sample was drawn? Inferential? Descriptive? 2 and 41 and 3

15. Let’s Make a Stemplot! An Example (Ex. 1.7 p.14) Data in Table 1.3 p. 14 (and on next slide)

17. Stem and Leaf Plot for Example IQ Test Scores for 60 Randomly Chosen 5 th Grade Students Generated Using the Descriptive Statistics Menu on Megastat Stem and Leaf plot foriq stem unit =10 leaf unit =1 FrequencyStem Leaf 38 1 2 9 49 0 4 6 7 1410 0 1 1 1 2 2 2 3 5 6 8 9 9 9 1711 0 0 0 2 2 3 3 4 4 4 5 6 7 7 7 8 8 1112 2 2 3 4 4 4 5 6 7 7 8 913 0 1 3 4 4 6 7 9 9 214 2 5 60

18. Now Let’s Make a Histogram! Use the Same Data in Example 1.7 (Data in Table 1.3) We will start by hand….using class widths of 10 starting at 80… Compare the Stemplot to the Histogram!

19. Histogram for Example iq cumulative lower upper midpointwidth frequencypercent frequencypercent 80<90851035.03 90<100951046.7711.7 100<110105101423.32135.0 110<120115101728.33863.3 120<130125101118.34981.7 130<14013510915.05896.7 140<1501451023.360100.0 60100.0 Compare this Histogram to the Stem & Leaf Plot we Generated Earlier!

20. Recall Our Earlier Question 1 1. What percent of the 60 randomly chosen fifth grade students have an IQ score of at least 120? Numerically? How to Represent Graphically? 18.3%+15%+3.3%=36.6% (11+9+2)/60=.367 or 36.7% Grey Shaded Region corresponds to this 36.6% of data

21. What is different from the histogram we generated in class??

22. Let’s Look at the Distribution we Just Created: Overall Pattern: Shape (modes, tails (skewness), symmetry) Center (mean, median) Spread (range, IQR, standard deviation) Deviations: Outliers Descriptors we will be interested in for data and population distributions.

23. Overall Pattern: Shape, Center, Spread? Deviations: Outliers?

24. Data Analysis – An Interesting Example (p. 9)! 80 Calls

25. Overall Pattern: Shape, Center, Spread? Deviations: Outliers?

27. Moral of this story: making your class widths too small can obscure important features of your data.

28. Time Plots – For Data Collected Over Time… Example: Mississippi River Discharge p.19 (data p. 21)

33. Example – Dealing with Seasonal Variation

35. Trend line Seasonal variation Original data

36. Residuals = original data - trend line - seasonal variation

37. Extra Slides from Homework Problem 1.19 Problem 1.20 Problem 1.21 Problem 1.31 Problem 1.36 Problem 1.37-1.38

38. Problem 1.19, page 30

39. Problem 1.20, page 31

40. Problem 1.21, page 31

41. Problem 1.31, page 36