2 Frequency Distributions and Their Graphs § 2.1Frequency Distributions and Their Graphs
3 Frequency Distributions A frequency distribution is a table that shows classes or intervals of data with a count of the number in each class. The frequency f of a class is the number of data points in the class.ClassFrequency, f1 – 445 – 859 – 12313 – 1617 – 202Lower Class LimitsUpper Class LimitsFrequencies
4 Frequency Distributions The class width is the distance between lower (or upper) limits of consecutive classes.ClassFrequency, f1 – 445 – 859 – 12313 – 1617 – 2025 – 1 = 49 – 5 = 413 – 9 = 417 – 13 = 4The class width is 4.The range is the difference between the maximum and minimum data entries.
5 Guidelines for Constructing a Frequency Distribution Decide on the number of classes to include. The number of classes should be between 5 and 20; otherwise, it may be difficult to detect any patterns.Find the class width as follows. Determine the range of the data, divide the range by the number of classes, and round up to the next convenient number. If you obtain a whole number when calculating the class width of a frequency distribution, use the next whole number as the class width to ensure you have enough space in your frequency distribution for all data values.Find the class limits. You can use the minimum entry as the lower limit of the first class. To find the remaining lower limits, add the class width to the lower limit of the preceding class. Then find the upper class limits.Make a tally mark for each data entry in the row of the appropriate class.Count the tally marks to find the total frequency f for each class.
6 Constructing a Frequency Distribution Example:The following data represents the ages of 30 students in a statistics class. Construct a frequency distribution that has five classes.Ages of Students1820212729193032342437382239443346544951Continued.
7 Constructing a Frequency Distribution Example continued:1. The number of classes (5) is stated in the problem.2. The minimum data entry is 18 and maximum entry is 54, so the range is 36. Divide the range by the number of classes to find the class width.365Class width == 7.2Round up to 8.Continued.
8 Constructing a Frequency Distribution Example continued:3. The minimum data entry of 18 may be used for the lower limit of the first class. To find the lower class limits of the remaining classes, add the width (8) to each lower limit.The lower class limits are 18, 26, 34, 42, and 50.The upper class limits are 25, 33, 41, 49, and 57.4. Make a tally mark for each data entry in the appropriate class.5. The number of tally marks for a class is the frequency for that class.Continued.
9 Constructing a Frequency Distribution Example continued:Number of studentsAgesAges of StudentsTallyFrequency, fClass18 – 251326 – 33834 – 41442 – 493Check that the sum equals the number in the sample.50 – 572
10 MidpointThe midpoint of a class is the sum of the lower and upper limits of the class divided by two. The midpoint is sometimes called the class mark.(Lower class limit) + (Upper class limit)2Midpoint =Frequency, fClassMidpoint41 – 42.5Midpoint =
11 MidpointExample:Find the midpoints for the “Ages of Students” frequency distribution.50 – 5742 – 4934 – 4126 – 3323481318 – 25Frequency, fClassMidpointAges of Students= 4321.543 2 = 21.529.537.545.553.5
12 Relative FrequencyThe relative frequency of a class is the portion or percentage of the data that falls in that class. To find the relative frequency of a class, divide the frequency f by the sample size n.Class frequencySample sizeRelative frequency =ClassFrequency, fRelative Frequency1 – 440.222Relative frequency
13 Relative Frequency Example: Find the relative frequencies for the “Ages of Students” frequency distribution.50 – 5723481342 – 4934 – 4126 – 3318 – 25Frequency, fClassRelative FrequencyPortion of students0.4330.2670.1330.10.067
14 Cumulative FrequencyThe cumulative frequency of a class is the sum of the frequency for that class and all the previous classes.50 – 5723481342 – 4934 – 4126 – 3318 – 25Frequency, fClassCumulative FrequencyAges of Students13+21+25+28Total number of students+30
15 Frequency HistogramA frequency histogram is a bar graph that represents the frequency distribution of a data set.The horizontal scale is quantitative and measures the data values.The vertical scale measures the frequencies of the classes.Consecutive bars must touch.Class boundaries are the numbers that separate the classes without forming gaps between them.The horizontal scale of a histogram can be marked with either the class boundaries or the midpoints.
16 Class Boundaries Example: Find the class boundaries for the “Ages of Students” frequency distribution.Class Boundaries50 – 5723481342 – 4934 – 4126 – 3318 – 25Frequency, fClassAges of StudentsThe distance from the upper limit of the first class to the lower limit of the second class is 1.17.5 25.525.5 33.533.5 41.541.5 49.5Half this distance is 0.5.49.5 57.5
17 Frequency Histogram f Example: Draw a frequency histogram for the “Ages of Students” frequency distribution. Use the class boundaries.Ages of Students108642Age (in years)f121417.525.533.541.549.557.5138432Broken axis
18 Frequency PolygonA frequency polygon is a line graph that emphasizes the continuous change in frequencies.Broken axisAges of Students108642Age (in years)f121413.521.529.537.545.553.561.5Line is extended to the x-axis.Midpoints
19 Relative Frequency Histogram A relative frequency histogram has the same shape and the same horizontal scale as the corresponding frequency histogram.0.40.30.20.10.5Ages of StudentsAge (in years)Relative frequency(portion of students)17.525.533.541.549.557.50.4330.2670.1330.10.067
20 Cumulative Frequency Graph A cumulative frequency graph or ogive, is a line graph that displays the cumulative frequency of each class at its upper class boundary.17.5Age (in years)Ages of Students241812630Cumulative frequency(portion of students)25.533.541.549.557.5The graph ends at the upper boundary of the last class.
21 More Graphs and Displays § 2.2More Graphs and Displays
22 Stem-and-Leaf PlotIn a stem-and-leaf plot, each number is separated into a stem (usually the entry’s leftmost digits) and a leaf (usually the rightmost digit). This is an example of exploratory data analysis.Example:The following data represents the ages of 30 students in a statistics class. Display the data in a stem-and-leaf plot.Ages of Students1820212729193032342437382239443346544951Continued.
23 Stem-and-Leaf Plot Ages of Students Key: 1|8 = 18 1 8 8 8 9 9 9 2 3 45Most of the values lie between 20 and 39.4 6 91 4This graph allows us to see the shape of the data as well as the actual values.
24 Stem-and-Leaf Plot Example: Construct a stem-and-leaf plot that has two lines for each stem.Ages of StudentsKey: 1|8 = 18123457 9 9From this graph, we can conclude that more than 50% of the data lie between 20 and 34.7 8 946 91 4
25 Dot PlotIn a dot plot, each data entry is plotted, using a point, above a horizontal axis.Example:Use a dot plot to display the ages of the 30 students in the statistics class.Ages of Students1820212729193032342437382239443346544951Continued.
26 Dot Plot Ages of Students 151824454821513054394233362757From this graph, we can conclude that most of the values lie between 18 and 32.
27 Pie ChartA pie chart is a circle that is divided into sectors that represent categories. The area of each sector is proportional to the frequency of each category.Accidental Deaths in the USA in 2002TypeFrequencyMotor Vehicle43,500Falls12,200Poison6,400Drowning4,600Fire4,200Ingestion of Food/Object2,900Firearms1,400(Source: US Dept. of Transportation)Continued.
28 Pie ChartTo create a pie chart for the data, find the relative frequency (percent) of each category.TypeFrequencyRelative FrequencyMotor Vehicle43,5000.578Falls12,2000.162Poison6,4000.085Drowning4,6000.061Fire4,2000.056Ingestion of Food/Object2,9000.039Firearms1,4000.019n = 75,200Continued.
29 Pie ChartNext, find the central angle. To find the central angle, multiply the relative frequency by 360°.TypeFrequencyRelative FrequencyAngleMotor Vehicle43,5000.578208.2°Falls12,2000.16258.4°Poison6,4000.08530.6°Drowning4,6000.06122.0°Fire4,2000.05620.1°Ingestion of Food/Object2,9000.03913.9°Firearms1,4000.0196.7°Continued.
30 Pie Chart Ingestion 3.9% Firearms 1.9% Fire 5.6% Drowning 6.1% Poison 8.5%Motor vehicles57.8%Falls16.2%
31 Pareto ChartA Pareto chart is a vertical bar graph is which the height of each bar represents the frequency. The bars are placed in order of decreasing height, with the tallest bar to the left.Accidental Deaths in the USA in 2002TypeFrequencyMotor Vehicle43,500Falls12,200Poison6,400Drowning4,600Fire4,200Ingestion of Food/Object2,900Firearms1,400(Source: US Dept. of Transportation)Continued.
32 Ingestion of Food/Object Pareto ChartAccidental Deaths4500040000350003000025000200001500010000Poison5000Motor VehiclesFallsPoisonDrowningFireIngestion of Food/ObjectFirearms
33 Scatter PlotWhen each entry in one data set corresponds to an entry in another data set, the sets are called paired data sets.In a scatter plot, the ordered pairs are graphed as points in a coordinate plane. The scatter plot is used to show the relationship between two quantitative variables.The following scatter plot represents the relationship between the number of absences from a class during the semester and the final grade.Continued.
34 Scatter Plot Final grade (y) Absences (x) 825121596y78929058437481Finalgrade(y)246810121416405060708090Absences (x)100From the scatter plot, you can see that as the number of absences increases, the final grade tends to decrease.
35 Times Series ChartA data set that is composed of quantitative data entries taken at regular intervals over a period of time is a time series. A time series chart is used to graph a time series.Example:The following table lists the number of minutes Robert used on his cell phone for the last six months.MonthMinutesJanuary236February242March188April175May199June135Construct a time series chart for the number of minutes used.Continued.
36 Times Series Chart Robert’s Cell Phone Usage Month Minutes 200 150 100 250MinutesMonthJanFebMarAprMayJune
37 Measures of Central Tendency § 2.3Measures of Central Tendency
38 MeanA measure of central tendency is a value that represents a typical, or central, entry of a data set. The three most commonly used measures of central tendency are the mean, the median, and the mode.The mean of a data set is the sum of the data entries divided by the number of entries.Population mean:Sample mean:“mu”“x-bar”
39 MeanExample:The following are the ages of all seven employees of a small company:Calculate the population mean.Add the ages and divide by 7.The mean age of the employees is 49 years.
40 MedianThe median of a data set is the value that lies in the middle of the data when the data set is ordered. If the data set has an odd number of entries, the median is the middle data entry. If the data set has an even number of entries, the median is the mean of the two middle data entries.Example:Calculate the median age of the seven employees.To find the median, sort the data.The median age of the employees is 53 years.
41 ModeThe mode of a data set is the data entry that occurs with the greatest frequency. If no entry is repeated, the data set has no mode. If two entries occur with the same greatest frequency, each entry is a mode and the data set is called bimodal.Example:Find the mode of the ages of the seven employees.The mode is 57 because it occurs the most times.An outlier is a data entry that is far removed from the other entries in the data set.
42 Comparing the Mean, Median and Mode Example:A 29-year-old employee joins the company and the ages of the employees are now:Recalculate the mean, the median, and the mode. Which measure of central tendency was affected when this new age was added?Mean = 46.5The mean takes every value into account, but is affected by the outlier.Median = 48.5The median and mode are not influenced by extreme values.Mode = 57
43 Weighted MeanA weighted mean is the mean of a data set whose entries have varying weights. A weighted mean is given bywhere w is the weight of each entry x.Example:Grades in a statistics class are weighted as follows:Tests are worth 50% of the grade, homework is worth 30% of the grade and the final is worth 20% of the grade. A student receives a total of 80 points on tests, 100 points on homework, and 85 points on his final. What is his current grade?Continued.
44 Weighted Mean Begin by organizing the data in a table. Source Score, x Weight, wxwTests800.5040Homework1000.3030Final850.2017The student’s current grade is 87%.
45 Mean of a Frequency Distribution The mean of a frequency distribution for a sample is approximated bywhere x and f are the midpoints and frequencies of the classes.Example:The following frequency distribution represents the ages of 30 students in a statistics class. Find the mean of the frequency distribution.Continued.
46 Mean of a Frequency Distribution Class midpointClassxf(x · f )18 – 2521.513279.526 – 3329.58236.034 – 4137.54150.042 – 4945.53136.550 – 5753.52107.0n = 30Σ = 909.0The mean age of the students is 30.3 years.
47 Shapes of Distributions A frequency distribution is symmetric when a vertical line can be drawn through the middle of a graph of the distribution and the resulting halves are approximately the mirror images.A frequency distribution is uniform (or rectangular) when all entries, or classes, in the distribution have equal frequencies. A uniform distribution is also symmetric.A frequency distribution is skewed if the “tail” of the graph elongates more to one side than to the other. A distribution is skewed left (negatively skewed) if its tail extends to the left. A distribution is skewed right (positively skewed) if its tail extends to the right.
48 Symmetric Distribution 10 Annual Incomes15,00020,00022,00024,00025,00026,00028,00030,00035,000Income54321f$25000mean = median = mode= $25,000
49 Skewed Left Distribution 10 Annual Incomes20,00022,00024,00025,00026,00028,00030,00035,000Income54321f$25000mean = $23,500median = mode = $25,000Mean < Median
50 Skewed Right Distribution 10 Annual Incomes15,00020,00022,00024,00025,00026,00028,00030,0001,000,000Income54321f$25000mean = $121,500median = mode = $25,000Mean > Median
51 Summary of Shapes of Distributions SymmetricUniformMean = MedianSkewed rightSkewed leftMean > MedianMean < Median
53 RangeThe range of a data set is the difference between the maximum and minimum date entries in the set.Range = (Maximum data entry) – (Minimum data entry)Example:The following data are the closing prices for a certain stock on ten successive Fridays. Find the range.Stock565758616367The range is 67 – 56 = 11.
54 DeviationThe deviation of an entry x in a population data set is the difference between the entry and the mean μ of the data set.Deviation of x = x – μExample:The following data are the closing prices for a certain stock on five successive Fridays. Find the deviation of each price.6763615856StockxDeviationx – μ56 – 61 = – 558 – 61 = – 361 – 61 = 063 – 61 = 2The mean stock price isμ = 305/5 = 61.67 – 61 = 6Σx = 305Σ(x – μ) = 0
55 Variance and Standard Deviation The population variance of a population data set of N entries isPopulation variance =“sigma squared”The population standard deviation of a population data set of N entries is the square root of the population variance.Population standard deviation =“sigma”
56 Finding the Population Standard Deviation GuidelinesIn Words In SymbolsFind the mean of the population data set.Find the deviation of each entry.Square each deviation.Add to get the sum of squares.Divide by N to get the population variance.Find the square root of the variance to get the population standard deviation.
57 Finding the Sample Standard Deviation GuidelinesIn Words In SymbolsFind the mean of the sample data set.Find the deviation of each entry.Square each deviation.Add to get the sum of squares.Divide by n – 1 to get the sample variance.Find the square root of the variance to get the sample standard deviation.
58 Finding the Population Standard Deviation Example:The following data are the closing prices for a certain stock on five successive Fridays. The population mean is 61. Find the population standard deviation.Always positive!StockxDeviationx – μSquared(x – μ)256– 52558– 3961632467636Σx = 305Σ(x – μ) = 0Σ(x – μ)2 = 74SS2 = Σ(x – μ)2 = 743.85σ $3.85
59 Interpreting Standard Deviation When interpreting standard deviation, remember that is a measure of the typical amount an entry deviates from the mean. The more the entries are spread out, the greater the standard deviation.108642Data valueFrequency1214= 4s = 1.18108642Data valueFrequency1214= 4s = 0
60 Empirical Rule (68-95-99.7%) Empirical Rule For data with a (symmetric) bell-shaped distribution, the standard deviation has the following characteristics.About 68% of the data lie within one standard deviation of the mean.About 95% of the data lie within two standard deviations of the mean.About 99.7% of the data lie within three standard deviation of the mean.
61 Empirical Rule (68-95-99.7%) 34% 34% 2.35% 2.35% 13.5% 13.5% –4 –3 –2 99.7% within 3 standard deviations95% within 2 standard deviations68% within 1 standard deviation–4–3–2–1123434%34%2.35%2.35%13.5%13.5%
62 Using the Empirical Rule Example:The mean value of homes on a street is $125 thousand with a standard deviation of $5 thousand. The data set has a bell shaped distribution. Estimate the percent of homes between $120 and $130 thousand.12513013512014014511511010568%μ – σμμ + σ68% of the houses have a value between $120 and $130 thousand.
63 Chebychev’s TheoremThe Empirical Rule is only used for symmetric distributions.Chebychev’s Theorem can be used for any distribution, regardless of the shape.
64 Chebychev’s TheoremThe portion of any data set lying within k standard deviations (k > 1) of the mean is at leastFor k = 2: In any data set, at least or 75%, of the data lie within 2 standard deviations of the mean.For k = 3: In any data set, at least or 88.9%, of the data lie within 3 standard deviations of the mean.
65 Using Chebychev’s Theorem Example:The mean time in a women’s 400-meter dash is 52.4 seconds with a standard deviation of 2.2 sec. At least 75% of the women’s times will fall between what two values?2 standard deviations52.454.656.85950.24845.8At least 75% of the women’s 400-meter dash times will fall between 48 and 56.8 seconds.
66 Standard Deviation for Grouped Data Sample standard deviation =where n = Σf is the number of entries in the data set, and x is the data value or the midpoint of an interval.Example:The following frequency distribution represents the ages of 30 students in a statistics class. The mean age of the students is 30.3 years. Find the standard deviation of the frequency distribution.Continued.
67 Standard Deviation for Grouped Data The mean age of the students is 30.3 years.Classxfx –(x – )2(x – )2f18 – 2521.513– 8.877.4426 – 3329.58– 0.80.645.1234 – 4137.547.251.84207.3642 – 4945.5315.2231.04693.1250 – 5753.5223.2538.24n = 30The standard deviation of the ages is 10.2 years.
69 QuartilesThe three quartiles, Q1, Q2, and Q3, approximately divide an ordered data set into four equal parts.Median502510075Q3Q2Q1Q1 is the median of the data below Q2.Q3 is the median of the data above Q2.
70 Finding Quartiles Example: The quiz scores for 15 students is listed below. Find the first, second and third quartiles of the scores.Order the data.Lower halfUpper halfQ1Q2Q3About one fourth of the students scores 37 or less; about one half score 43 or less; and about three fourths score 48 or less.
71 Interquartile RangeThe interquartile range (IQR) of a data set is the difference between the third and first quartiles.Interquartile range (IQR) = Q3 – Q1.Example:The quartiles for 15 quiz scores are listed below. Find the interquartile range.Q1 = 37Q2 = 43Q3 = 48(IQR) = Q3 – Q1The quiz scores in the middle portion of the data set vary by at most 11 points.= 48 – 37= 11
72 Box and Whisker PlotA box-and-whisker plot is an exploratory data analysis tool that highlights the important features of a data set.The five-number summary is used to draw the graph.The minimum entryQ1Q2 (median)Q3The maximum entryExample:Use the data from the 15 quiz scores to draw a box-and-whisker plot.Continued.
73 Box and Whisker Plot Five-number summary The minimum entry Q1 Q2 (median)Q3The maximum entry2837434855Quiz Scores28374348554044485236322856
74 Percentiles and Deciles Fractiles are numbers that partition, or divide, an ordered data set.Percentiles divide an ordered data set into 100 parts. There are 99 percentiles: P1, P2, P3…P99.Deciles divide an ordered data set into 10 parts. There are 9 deciles: D1, D2, D3…D9.A test score at the 80th percentile (P80), indicates that the test score is greater than 80% of all other test scores and less than or equal to 20% of the scores.
75 Standard ScoresThe standard score or z-score, represents the number of standard deviations that a data value, x, falls from the mean, μ.Example:The test scores for all statistics finals at Union College have a mean of 78 and standard deviation of 7. Find the z-score fora.) a test score of 85,b.) a test score of 70,c.) a test score of 78.Continued.
76 Standard Scores Example continued: a.) μ = 78, σ = 7, x = 85 This score is 1 standard deviation higher than the mean.b.) μ = 78, σ = 7, x = 70This score is 1.14 standard deviations lower than the mean.c.) μ = 78, σ = 7, x = 78This score is the same as the mean.
77 Relative Z-Scores Example: John received a 75 on a test whose class mean was 73.2 with a standard deviation of Samantha received a 68.6 on a test whose class mean was 65 with a standard deviation of Which student had the better test score?John’s z-scoreSamantha’s z-scoreJohn’s score was 0.4 standard deviations higher than the mean, while Samantha’s score was 0.92 standard deviations higher than the mean. Samantha’s test score was better than John’s.