1. Slide 13-1 Copyright © 2005 Pearson Education, Inc. SEVENTH EDITION and EXPANDED SEVENTH EDITION

2. Copyright © 2005 Pearson Education, Inc. Chapter 13 Statistics

3. Copyright © 2005 Pearson Education, Inc. 13.1 Sampling Techniques

4. Slide 13-4 Copyright © 2005 Pearson Education, Inc. Statistics Statistics is the art and science of gathering, analyzing, and making inferences from numerical information (data) obtained in an experiment. Statistics are divided into two main braches.  Descriptive statistics is concerned with the collection, organization, and analysis of data.  Inferential statistics is concerned with the making of generalizations or predictions of the data collected.

5. Slide 13-5 Copyright © 2005 Pearson Education, Inc. Statisticians A statistician’s interest lies in drawing conclusions about possible outcomes through observations of only a few particular events.  The population consists of all items or people of interest.  The sample includes some of the items in the population. When a statistician draws a conclusion from a sample, there is always the possibility that the conclusion is incorrect.

6. Slide 13-6 Copyright © 2005 Pearson Education, Inc. Types of Sampling A random sampling occurs if a sample is drawn in such a way that each time an item is selected, each item has an equal chance of being drawn. When a sample is obtained by drawing every nth item on a list or production line, the sample is a systematic sample. A cluster sample is referred to as an area sample because it is applied on a geographical basis.

7. Slide 13-7 Copyright © 2005 Pearson Education, Inc. Types of Sampling continued Stratified sampling involves dividing the population by characteristics such as gender, race, religion, or income. Convenience sampling uses data that is easily obtained and can be extremely biased.

8. Slide 13-8 Copyright © 2005 Pearson Education, Inc. Example: Identifying Sampling Techniques A raffle ticket is drawn by a blindfolded person at a festival to win a grand prize. Students at an elementary are classified according to their present grade level. Then, a random sample of three students from each grade are chosen to represent their class. Every sixth car on highway is stopped for a vehicle inspection.

9. Slide 13-9 Copyright © 2005 Pearson Education, Inc. Example: Identifying Sampling Techniques continued Voters are classified based on their polling location. A random sample of four polling locations are selected. All the voters from the precinct are included in the sample. The first 20 people entering a water park are asked if they are wearing sunscreen. Solution: a) Random d) Cluster b) Stratified e) Convenience c) Systematic

10. Copyright © 2005 Pearson Education, Inc. 13.2 The Misuses of Statistics

11. Slide 13-11 Copyright © 2005 Pearson Education, Inc. Misuses of Statistics Many individuals, businesses, and advertising firms misuse statistics to their own advantage. When examining statistical information consider the following:  Was the sample used to gather the statistical data unbiased and of sufficient size?  Is the statistical statement ambiguous, could it be interpreted in more than one way?

12. Slide 13-12 Copyright © 2005 Pearson Education, Inc. Example: Misleading Statistics An advertisement says, “Fly Speedway Airlines and Save 20%”.  Here there is not enough information given.  The “Save 20%” could be off the original ticket price, the ticket price when you buy two tickets or of another airline’s ticket price. A helped wanted ad read,” Salesperson wanted for Ryan’s Furniture Store. Average Salary: $32,000.”  The word “average” can be very misleading.  If most of the salespeople earn $20,000 to $25,000 and the owner earns $76,000, this “average salary” is not a fair representation.

13. Slide 13-13 Copyright © 2005 Pearson Education, Inc. Charts and Graphs Charts and graphs can also be misleading.  Even though the data is displayed correctly, adjusting the vertical scale of a graph can give a different impression.  A circle graph can be misleading if the sum of the parts of the graphs do not add up to 100%.

14. Slide 13-14 Copyright © 2005 Pearson Education, Inc. Example: Misleading Graphs While each graph presents identical information, the vertical scales have been altered.

15. Copyright © 2005 Pearson Education, Inc. 13.3 Frequency Distributions

16. Slide 13-16 Copyright © 2005 Pearson Education, Inc. Example The number of pets per family is recorded for 30 families surveyed. Construct a frequency distribution of the following data: 443333 2 2 1 0 22222 21111 11111 00000

17. Slide 13-17 Copyright © 2005 Pearson Education, Inc. Solution 24 43 82 101 60 FrequencyNumber of Pets 443333 2 2 1 0 22222 21111 11111 00000

18. Slide 13-18 Copyright © 2005 Pearson Education, Inc. Rules for Data Grouped by Classes The classes should be of the same “width.” The classes should not overlap. Each piece of data should belong to only one class.

19. Slide 13-19 Copyright © 2005 Pearson Education, Inc. Definitions Midpoint of a class is found by adding the lower and upper class limits and dividing the sum by 2.

20. Slide 13-20 Copyright © 2005 Pearson Education, Inc. Example The following set of data represents the distance, in miles, 15 randomly selected second grade students live from school. Construct a frequency distribution with the first class 0  2. 0.27.41.59.64.8 1.35.70.85.90.5 8.73.89.75.36.8

21. Slide 13-21 Copyright © 2005 Pearson Education, Inc. Solution First, rearrange the data from lowest to highest. 9.79.68.7 7.46.85.9 5.75.34.8 3.81.51.3 0.80.50.2 15 38.4 -10.4 26.3 - 8.3 44.2 - 6.2 12.1 - 4.1 5 0 - 2 Frequency# of miles from school

22. Copyright © 2005 Pearson Education, Inc. 13.4 Statistical Graphs

23. Slide 13-23 Copyright © 2005 Pearson Education, Inc. Circle Graphs Circle graphs (also known as pie charts) are often used to compare parts of one or more components of the whole to the whole.

24. Slide 13-24 Copyright © 2005 Pearson Education, Inc. Example According to a recent hospital survey of 200 patients the following table indicates how often hospitals used four different kinds of painkillers. Use the information to construct a circle graph illustrating the percent each painkiller was used. 200 24Other 16Acetaminophen 104Ibuprofen 56Aspirin

25. Slide 13-25 Copyright © 2005 Pearson Education, Inc. Solution Determine the measure of the corresponding central angle. 200 24 16 104 56 Number of Patients 100% Percent of Total 360  0.12  360 = 43.2  0.08  360 = 28.8  0.52  360 = 187.2  0.28  360 = 100.8  Measure of Central Angle Total Other Acetaminophen Ibuprofen Aspirin Painkiller

26. Slide 13-26 Copyright © 2005 Pearson Education, Inc. Solution continued Use a protractor to construct a circle graph and label it properly.

27. Slide 13-27 Copyright © 2005 Pearson Education, Inc. Histogram A histogram is a graph with observed values on its horizontal scale and frequencies on it vertical scale. Example: Construct a histogram of the frequency distribution. 24 43 82 101 60 Frequency# of pets

28. Slide 13-28 Copyright © 2005 Pearson Education, Inc. Solution 24 43 82 101 60 Frequency# of pets

29. Slide 13-29 Copyright © 2005 Pearson Education, Inc. Frequency Polygon

30. Slide 13-30 Copyright © 2005 Pearson Education, Inc. Stem-and-Leaf Display A stem-and-leaf display is a tool that organizes and groups the data while allowing us to see the actual values that make up the data. The left group of digits is called the stem. The right group of digits is called the leaf.

31. Slide 13-31 Copyright © 2005 Pearson Education, Inc. Example The table below indicates the number of miles 20 workers have to drive to work. construct a stem-and-leaf display. 914352612 1621432717 41532125 12831812

32. Slide 13-32 Copyright © 2005 Pearson Education, Inc. Solution Data 914352612 1621432717 41532125 12831812 34 53 115672 222456781 334890

33. Copyright © 2005 Pearson Education, Inc. 13.5 Measures of Central Tendency

34. Slide 13-34 Copyright © 2005 Pearson Education, Inc. Definitions An average is a number that is representative of a group of data. The arithmetic mean, or simply the mean is symbolized by or by the Greek letter mu, .

35. Slide 13-35 Copyright © 2005 Pearson Education, Inc. Mean The mean, is the sum of the data divided by the number of pieces of data. The formula for calculating the mean is where represents the sum of all the data and n represents the number of pieces of data.

36. Slide 13-36 Copyright © 2005 Pearson Education, Inc. Example-find the mean Find the mean amount of money parents spent on new school supplies and clothes if 5 parents randomly surveyed replied as follows: $327 $465 $672 $150 $230

37. Slide 13-37 Copyright © 2005 Pearson Education, Inc. Median The median is the value in the middle of a set of ranked data. Example: Determine the mean of $327 $465 $672 $150 $230. Rank the data from smallest to largest. $150 $230 $327 $465 $672 middle value (median)

38. Slide 13-38 Copyright © 2005 Pearson Education, Inc. Example: Median (even data) Determine the median of the following set of data: 8, 15, 9, 3, 4, 7, 11, 12, 6, 4. Rank the data: 3 4 4 6 7 8 9 11 12 15 There are 10 pieces of data so the median will lie halfway between the two middle pieces the 7 and 8. The median is (7 + 8)/2 = 7.5 3 4 4 6 9 11 12 15 7 8

39. Slide 13-39 Copyright © 2005 Pearson Education, Inc. Mode The mode is the piece of data that occurs most frequently. Example: Determine the mode of the data set: 3, 4, 4, 6, 7, 8, 9, 11, 12, 15. The mode is 4 since is occurs twice and the other values only occur once.

40. Slide 13-40 Copyright © 2005 Pearson Education, Inc. Midrange The midrange is the value halfway between the lowest (L) and highest (H) values in a set of data. Example: Find the midrange of the data set $327, $465, $672, $150, $230.

41. Slide 13-41 Copyright © 2005 Pearson Education, Inc. Example The weights of eight Labrador retrievers rounded to the nearest pound are 85, 92, 88, 75, 94, 88, 84, and 101. Determine the  a) mean b) median  c) mode d) midrange  e) rank the measures of central tendency from lowest to highest.

42. Slide 13-42 Copyright © 2005 Pearson Education, Inc. Example--dog weights 85, 92, 88, 75, 94, 88, 84, 101 Mean Median-rank the data  75, 84, 85, 88, 88, 92, 94, 101  The median is 88.

43. Slide 13-43 Copyright © 2005 Pearson Education, Inc. Example--dog weights 85, 92, 88, 75, 94, 88, 84, 101 Mode-the number that occurs most frequently. The mode is 88. Midrange = (L + H)/2 = (75 + 101)/2 = 88 Rank the measures 88.375, 88, 88, 88

44. Slide 13-44 Copyright © 2005 Pearson Education, Inc. Measures of Position Measures of position are often used to make comparisons. Two measures of position are percentiles and quartiles.

45. Slide 13-45 Copyright © 2005 Pearson Education, Inc. To Find the Quartiles of a Set of Data Order the data from smallest to largest. Find the median, or 2 nd quartile, of the set of data. If there are an odd number of pieces of data, the median is the middle value. If there are an even number of pieces of data, the median will be halfway between the two middle pieces of data.

46. Slide 13-46 Copyright © 2005 Pearson Education, Inc. To Find the Quartiles of a Set of Data continued The first quartile, Q 1, is the median of the lower half of the data; that is, Q 1, is the median of the data less than Q 2. The third quartile, Q 3, is the median of the upper half of the data; that is, Q 3 is the median of the data greater than Q 2.

47. Slide 13-47 Copyright © 2005 Pearson Education, Inc. Example: Quartiles The weekly grocery bills for 23 families are as follows. Determine Q 1, Q 2, and Q 3. 170210270270280 33080170240270 22522521531050 751601307481 95172190

48. Slide 13-48 Copyright © 2005 Pearson Education, Inc. Example: Quartiles continued Order the data: 50 75 74 80 81 95130 160170170172190210215 225225240270270270280 310330 Q 2 is the median of the entire data set which is 190. Q 1 is the median of the numbers from 50 to 172 which is 95. Q 3 is the median of the numbers from 210 to 330 which is 270.

49. Copyright © 2005 Pearson Education, Inc. 13.6 Measures of Dispersion

50. Slide 13-50 Copyright © 2005 Pearson Education, Inc. Measures of Dispersion Measures of dispersion are used to indicate the spread of the data. The range is the difference between the highest and lowest values; it indicates the total spread of the data.

51. Slide 13-51 Copyright © 2005 Pearson Education, Inc. Example: Range Nine different employees were selected and the amount of their salary was recorded. Find the range of the salaries. $24,000 $32,000 $26,500 $56,000 $48,000 $27,000 $28,500 $34,500 $56,750 Range = $56,750  $24,000 = $32,750

52. Slide 13-52 Copyright © 2005 Pearson Education, Inc. Standard Deviation The standard deviation measures how much the data differ from the mean.

53. Slide 13-53 Copyright © 2005 Pearson Education, Inc. To Find the Standard Deviation of a Set of Data 1. Find the mean of the set of data. 2. Make a chart having three columns:  DataData  Mean(Data  Mean) 2 3. List the data vertically under the column marked Data. 4. Subtract the mean from each piece of data and place the difference in the Data  Mean column.

54. Slide 13-54 Copyright © 2005 Pearson Education, Inc. To Find the Standard Deviation of a Set of Data continued 5.Square the values obtained in the Data  Mean column and record these values in the (Data  Mean) 2 column. 6.Determine the sum of the values in the (Data  Mean) 2 column. 7.Divide the sum obtained in step 6 by n  1, where n is the number of pieces of data. 8.Determine the square root of the number obtained in step 7. This number is the standard deviation of the set of data.

55. Slide 13-55 Copyright © 2005 Pearson Education, Inc. Example Find the standard deviation of the following prices of selected washing machines: $280, $217, $665, $684, $939, $299 Find the mean.

56. Slide 13-56 Copyright © 2005 Pearson Education, Inc. Example continued, mean = 514 421,5160 180,625425939 28,900170684 22,801151665 46,225  215 299 54,756  234 280 (  297) 2 = 88,209  297 217 (Data  Mean) 2 Data  Mean Data

57. Slide 13-57 Copyright © 2005 Pearson Education, Inc. Example continued, mean = 514 The standard deviation is $290.35.

58. Copyright © 2005 Pearson Education, Inc. 13.7 The Normal Curve

59. Slide 13-59 Copyright © 2005 Pearson Education, Inc. Types of Distributions Rectangular Distribution J-shaped distribution

60. Slide 13-60 Copyright © 2005 Pearson Education, Inc. Types of Distributions continued Bimodal Skewed to right

61. Slide 13-61 Copyright © 2005 Pearson Education, Inc. Types of Distributions continued Skewed to left Normal

62. Slide 13-62 Copyright © 2005 Pearson Education, Inc. Normal Distribution In a normal distribution, the mean, median, and mode all have the same value. Z-scores determine how far, in terms of standard deviations, a given score is from the mean of the distribution.

63. Slide 13-63 Copyright © 2005 Pearson Education, Inc. Example: z-scores A normal distribution has a mean of 50 and a standard deviation of 5. Find z-scores for the following values. a) 55b) 60c) 43 a) A score of 55 is one standard deviation above the mean.

64. Slide 13-64 Copyright © 2005 Pearson Education, Inc. Example: z-scores continued b) A score of 60 is 2 standard deviations above the mean. c) A score of 43 is 1.4 standard deviations below the mean.

65. Slide 13-65 Copyright © 2005 Pearson Education, Inc. To Find the Percent of Data Between any Two Values 1. Draw a diagram of the normal curve, indicating the area or percent to be determined. 2.Use the formula to convert the given values to z-scores. Indicate these z- scores on the diagram. 3. Look up the percent that corresponds to each z-score in Table 13.

66. Slide 13-66 Copyright © 2005 Pearson Education, Inc. To Find the Percent of Data Between any Two Values continued 4.  a) When finding the percent of data between two z-scores on the opposite side of the mean (when one z-score is positive and the other is negative), you find the sum of the individual percents.  b) When finding the percent of data between two z-scores on the same side of the mean (when both z-scores are positive or both are negative), subtract the smaller percent from the larger percent.

67. Slide 13-67 Copyright © 2005 Pearson Education, Inc. To Find the Percent of Data Between any Two Values continued  c) When finding the percent of data to the right of a positive z-score or to the left of a negative z- score, subtract the percent of data between ) and z from 50%.  d) When finding the percent of data to the left of a positive z-score or to the right of a negative z- score, add the percent of data between 0 and z to 50%.

68. Slide 13-68 Copyright © 2005 Pearson Education, Inc. Example Assume that the waiting times for customers at a popular restaurant before being seated for lunch at a popular restaurant before being seated for lunch are normally distributed with a mean of 12 minutes and a standard deviation of 3 min. a) Find the percent of customers who wait for at least 12 minutes before being seated. b) Find the percent of customers who wait between 9 and 18 minutes before being seated. c) Find the percent of customers who wait at least 17 minutes before being seated. d) Find the percent of customers who wait less than 8 minutes before being seated.

69. Slide 13-69 Copyright © 2005 Pearson Education, Inc. Solution wait for at least 12 minutes Since 12 minutes is the mean, half, or 50% of customers wait at least 12 min before being seated. between 9 and 18 minutes Use table 13.7 page 801. 34.1% + 47.7% = 81.8%

70. Slide 13-70 Copyright © 2005 Pearson Education, Inc. Solution continued at least 17 min Use table 13.7 page 801. 45.3% is between the mean and 1.67. 50%  45.3% = 4.7% Thus, 4.7% of customers wait at least 17 minutes. less than 8 min Use table 13.7 page 801. 40.8% is between the mean and  1.33. 50%  40.8% = 9.2% Thus, 9.2% of customers wait less than 8 minutes.

71. Copyright © 2005 Pearson Education, Inc. 13.8 Linear Correlation and Regression

72. Slide 13-72 Copyright © 2005 Pearson Education, Inc. Linear Correlation Linear correlation is used to determine whether there is a relationship between two quantities and, if so, how strong the relationship is.  The linear correlation coefficient, r, is a unitless measure that describes the strength of the linear relationship between two variables. If the value is positive, as one variable increases, the other increases. If the value is negative, as one variable increases, the other decreases. The variable, r, will always be a value between –1 and 1 inclusive.

73. Slide 13-73 Copyright © 2005 Pearson Education, Inc. Scatter Diagrams A visual aid used with correlation is the scatter diagram, a plot of points (bivariate data).  The independent variable, x, generally is a quantity that can be controlled.  The dependant variable, y, is the other variable. The value of r is a measure of how far a set of points varies from a straight line.  The greater the spread, the weaker the correlation and the closer the r value is to 0.

74. Slide 13-74 Copyright © 2005 Pearson Education, Inc. Correlation

75. Slide 13-75 Copyright © 2005 Pearson Education, Inc. Correlation

76. Slide 13-76 Copyright © 2005 Pearson Education, Inc. Linear Correlation Coefficient The formula to calculate the correlation coefficient (r) is as follows:

77. Slide 13-77 Copyright © 2005 Pearson Education, Inc. There are five applicants applying for a job as a medical transcriptionist. The following shows the results of the applicants when asked to type a chart. Determine the correlation coefficient between the words per minute typed and the number of mistakes. Example: Words Per Minute versus Mistakes 934Nancy 1041Kendra 1253Phillip 1167George 824Ellen MistakesWords per MinuteApplicant

78. Slide 13-78 Copyright © 2005 Pearson Education, Inc. We will call the words typed per minute, x, and the mistakes, y. List the values of x and y and calculate the necessary sums. Solution 306811156934 xy = 2,281y 2 = 510 x 2 =10,711y = 50x = 219 10 12 11 8 y Mistakes xyy2y2 x2x2 x 41 53 67 24 WPM 4101001681 6361442809 7371214489 19264576

79. Slide 13-79 Copyright © 2005 Pearson Education, Inc. Solution continued The n in the formula represents the number of pieces of data. Here n = 5.

80. Slide 13-80 Copyright © 2005 Pearson Education, Inc. Solution continued Since 0.86 is fairly close to 1, there is a fairly strong positive correlation. This result implies that the more words typed per minute, the more mistakes made.

81. Slide 13-81 Copyright © 2005 Pearson Education, Inc. Linear Regression Linear regression is the process of determining the linear relationship between two variables. The line of best fit (line of regression or the least square line) is the line such that the sum of the vertical distances from the line to the data points is a minimum.

82. Slide 13-82 Copyright © 2005 Pearson Education, Inc. The Line of Best Fit Equation:

83. Slide 13-83 Copyright © 2005 Pearson Education, Inc. Example Use the data in the previous example to find the equation of the line that relates the number of words per minute and the number of mistakes made while typing a chart. Graph the equation of the line of best fit on a scatter diagram that illustrates the set of bivariate points.

84. Slide 13-84 Copyright © 2005 Pearson Education, Inc. Solution From the previous results, we know that Now we find the y-intercept, b. Therefore the line of best fit is y = 0.081x + 6.452

85. Slide 13-85 Copyright © 2005 Pearson Education, Inc. Solution continued To graph y = 0.081x + 6.452, plot at least two points and draw the graph. 8.88230 8.07220 7.26210 yx

86. Slide 13-86 Copyright © 2005 Pearson Education, Inc. Solution continued