1. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 1 Overview and Descriptive Statistics

2. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 1.1 Populations, Samples, and Processes

3. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Populations and Samples A population is a well-defined collection of objects. When information is available for the entire population we have a census. A subset of the population is a sample.

4. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Data and Observations Univariate data consists of observations on a single variable (multivariate – more than two variables).

5. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Branches of Statistics Descriptive Statistics – summary and description of collected data. Inferential Statistics – generalizing from a sample to a population.

6. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Relationship Between Probability and Inferential Statistics Population Sample Probability Inferential Statistics

7. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 1.2 Pictorial and Tabular Methods in Descriptive Statistics

8. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Stem-and- Leaf Displays 1. Select one or more leading digits for the stem values. The trailing digits become the leaves. 2. List stem values in a vertical column. 3. Record the leaf for every observation. 4. Indicate the units for the stem and leaf on the display.

9. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Stem-and-Leaf Example 9, 10, 15, 22, 9, 15, 16, 24,11 Observed values: 0 9 9 1 1 0 5 5 6 2 2 4 Stem: tens digitLeaf: units digit

10. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Stem-and- Leaf Displays Identify typical value Extent of spread about a value Presence of gaps Extent of symmetry Number and location of peaks Presence of outlying values

11. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Another stem-and-leaf example The decimal point is 1 digit to the right of the | 2 | 55 3 | 05888 4 | 03558888888 5 | 00000000333335555577777 6 | 00033333555588 7 | 0003333335555588 8 | 00000335588 9 | 088

12. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Dotplots 9, 10, 15, 22, 9, 15, 16, 24,11 Observed values: 5 10 15 20 25 Represent data with dots.

13. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Types of Variables A variable is discrete if its set of possible values constitutes a finite set or an infinite sequence. A variable is continuous if its set of possible values consists of an entire interval on a number line.

14. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Histograms Discrete Data Determine the frequency and relative frequency for each value of x. Then mark possible x values on a horizontal scale. Above each value, draw a rectangle whose height is the relative frequency of that value.

15. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Ex. Students from a small college were asked how many charge cards they carry. x is the variable representing the number of cards and the results are below. x#people 012 142 257 324 49 54 62 Rel. Freq 0.08 0.28 0.38 0.16 0.06 0.03 0.01 Frequency Distribution

16. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Histograms xRel. Freq. 00.08 10.28 20.38 30.16 40.06 50.03 60.01 Credit card results:

17. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Histograms Continuous Data: Equal Class Widths Determine the frequency and relative frequency for each class. Then mark the class boundaries on a horizontal measurement axis. Above each class interval, draw a rectangle whose height is the relative frequency.

18. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Histogram example

19. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Histograms Continuous Data: Unequal Widths After determining frequencies and relative frequencies, calculate the height of each rectangle using: The resulting heights are called densities and the vertical scale is the density scale.

20. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Histogram Shapes symmetric unimodalbimodal positively skewednegatively skewed

21. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Histogram example: symmetric, slightly bimodal

22. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 1.3 Measures of Location

23. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. The Mean The average (mean) of the n numbers Population mean:

24. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. The sample median, is the middle value in a set of data that is arranged in ascending order. For an even number of data points the median is the average of the middle two. Median Population median:

25. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Median example In a class of 85 exam scores, the median, is the 43 rd number if the scores are listed in ascending order. (Note: In this case there are 42 above the median and 42 below the median.) 40 41 42 43 44 45 46 57.5 57.5 60.0 60.0 60.0 62.5 62.5

26. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Three Different Shapes for a Population Distribution symmetric positive skew negative skew

27. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Slight positive skew

28. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 1.4 Measures of Variability

29. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Sample Variance Variance is a measure of the spread of the data. The sample variance of the sample x 1, x 2, …x n of n values of X is given by We refer to s 2 as being based on n – 1 degrees of freedom.

30. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Sample variance example First, find sample mean: Next, add up squared deviations from mean: Divide by n-1, where n is the number of observations (in this case, 85):

31. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. The sample standard deviation is the square root of the sample variance: Standard Deviation Standard deviation is a measure of the spread of the data using the same units as the data.

32. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Standard deviation example

33. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Formula for s 2 An alternative expression for the numerator of s 2 is

34. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Formula for s 2 : Shortcut example First, sum the scores: Next, sum the squares: Numerator of variance equals

35. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Properties of s 2 Let x 1, x 2,…,x n be any sample and c be any nonzero constant. where is the sample variance of the x’s and is the sample variance of the y’s.

36. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Upper and Lower Fourths After the n observations in a data set are ordered from smallest to largest, the lower (upper) fourth is the median of the smallest (largest) half of the data, where the median is included in both halves if n is odd. A measure of the spread that is resistant to outliers is the fourth spread f s = upper fourth – lower fourth.

37. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Third and first quartiles After the n observations in a data set are ordered from smallest to largest, the first (third) quartile is the median of the smallest (largest) half of the data, where the median is included in both halves if n is odd. A measure of the spread that is resistant to outliers is the interquartile range or IQR f s = 3 rd quartile – 1 st quartile.

38. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Outliers Any observation farther than 1.5f s from the closest fourth is an outlier. An outlier is extreme if it is more than 3f s from the nearest fourth, and it is mild otherwise.

39. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Boxplots median extreme outliers upper fourth lower fourth mild outliers

40. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Boxplot example

41. Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Another boxplot example