Business Statistics: A First Course (5th Edition) Chapter 3 - Numerical Descriptive Measures PDF

Business Statistics: A First Course Fifth Edition Chapter 3 Numerical Descriptive Measures B...

Business Statistics: A First Course Fifth Edition Chapter 3 Numerical Descriptive Measures Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-1  Learning Objectives In this chapter, you learn: ◼ To describe the properties of central tendency, variation, and shape in numerical data ◼ To calculate descriptive summary measures for a population ◼ To construct and interpret a boxplot ◼ To calculate the covariance and the coefficient of correlation Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-2  Summary Definitions ▪ The central tendency is the extent to which all the data values group around a typical or central value. ▪ The variation is the amount of dispersion, or scattering, of values ▪ The shape is the pattern of the distribution of values from the lowest value to the highest value. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-3  Measures of Central Tendency: The Mean ◼ The arithmetic mean (often just called “mean”) is the most common measure of central tendency Pronounced x-bar The ith value ◼ For a sample of size n: n X i X1 + X 2 +  + Xn X= i=1 = n n Sample size Observed values Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-4  Example: ◼ Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-5  Mean for grouped data ◼ Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-6  Example: ◼ The following table gives the frequency distribution of the number of orders received each day during the past 50 days at the office of a mail-order company. Number of orders Number of Days 10-12 4 13-15 12 16-18 20 19-21 14 ◼ Calculate the mean. Ans: 16.64 order Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-7  Measures of Central Tendency: The Mean (continued) ◼ The most common measure of central tendency ◼ Mean = sum of values divided by the number of values ◼ Affected by extreme values (outliers) 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Mean = 3 Mean = 4 1 + 2 + 3 + 4 + 5 15 1 + 2 + 3 + 4 + 10 20 = =3 = =4 5 5 5 5 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-8  Measures of Central Tendency: The Median ◼ In an ordered array, the median is the “middle” number (50% above, 50% below) 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Median = 3 Median = 3 ◼ Not affected by extreme values Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-9  Measures of Central Tendency: Locating the Median ◼ The location of the median when the values are in numerical order (smallest to largest): n +1 Median position = position in the ordered data 2 ◼ If the number of values is odd, the median is the middle number ◼ If the number of values is even, the median is the average of the two middle numbers Note that n + 1 is not the value of the median, only the position of 2 the median in the ranked data Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-10  Measures of Central Tendency: The Mode ◼ Value that occurs most often ◼ Not affected by extreme values ◼ Used for either numerical or categorical data ◼ There may be no mode ◼ There may be several modes – multimodal, two modes - bimodal 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 1 2 3 4 5 6 No Mode Mode = 9 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-11  Measures of Central Tendency: Review Example House Prices: ▪ Mean: ($3,000,000/5) $2,000,000 = $600,000 $500,000 $300,000 ▪ Median: middle value of ranked $100,000 data $100,000 = $300,000 Sum $3,000,000 ▪ Mode: most frequent value = $100,000 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-12  Measures of Central Tendency: Which Measure to Choose? ▪ The mean is generally used, unless extreme values (outliers) exist. ▪ The median is often used, since the median is not sensitive to extreme values. For example, median home prices may be reported for a region; it is less sensitive to outliers. ▪ In some situations it makes sense to report both the mean and the median. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-13  Measures of Central Tendency: Summary Central Tendency Arithmetic Median Mode Mean n X i X= i=1 n Middle value Most in the ordered frequently array observed value Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-14  Measures of Variation Variation Range Variance Standard Coefficient Deviation of Variation ◼ Measures of variation give information on the spread or variability or dispersion of the data values. Same center, different variation Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-15  Measures of Variation: The Range ▪ Simplest measure of variation ▪ Difference between the largest and the smallest values: Range = Xlargest – Xsmallest Example: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Range = 13 - 1 = 12 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-16  Measures of Variation: Why The Range Can Be Misleading ▪ Ignores the way in which data are distributed 7 8 9 10 11 12 7 8 9 10 11 12 Range = 12 - 7 = 5 Range = 12 - 7 = 5 ▪ Sensitive to outliers 1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,5 Range = 5 - 1 = 4 1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,120 Range = 120 - 1 = 119 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-17  Measures of Variation: The Variance ◼ Average (approximately) of squared deviations of values from the mean n ◼ Sample variance:  (X − X)i 2 S = 2 i=1 n -1 Where X = arithmetic mean n = sample size Xi = ith value of the variable X Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-18  Measures of Variation: The Standard Deviation ◼ Most commonly used measure of variation ◼ Shows variation about the mean ◼ Is the square root of the variance ◼ Has the same units as the original data n ◼ Sample standard deviation:  (X − X) i 2 S= i=1 n -1 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-19  ◼ Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-20  Measures of Variation: The Standard Deviation Steps for Computing Standard Deviation 1. Compute the difference between each value and the mean. 2. Square each difference. 3. Add the squared differences. 4. Divide this total by n-1 to get the sample variance. 5. Take the square root of the sample variance to get the sample standard deviation. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-21  Measures of Variation: Sample Standard Deviation: Calculation Example Sample Data (Xi) : 10 12 14 15 17 18 18 24 n=8 Mean = X = 16 (10 − X)2 + (12 − X)2 + (14 − X )2 +  + (24 − X )2 S= n −1 (10 − 16) 2 + (12 − 16) 2 + (14 − 16) 2 +  + (24 − 16) 2 = 8 −1 130 A measure of the “average” = = 4.3095 7 scatter around the mean Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. (e.g., Coffeedrink_lecture3) Chap 3-22  Measures of Variation: Comparing Standard Deviations Data A Mean = 15.5 11 12 13 14 15 16 17 18 19 20 21 S = 3.338 Data B Mean = 15.5 11 12 13 14 15 16 17 18 19 20 S = 0.926 21 Data C Mean = 15.5 S = 4.570 11 12 13 14 15 16 17 18 19 20 21 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-23  Measures of Variation: Comparing Standard Deviations Smaller standard deviation Larger standard deviation Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-24  Standard deviation for grouped data ◼ Number of orders Number of Days 10-12 4 13-15 12 16-18 20 19-21 14 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-25  Measures of Variation: Summary Characteristics ▪ The more the data are spread out, the greater the range, variance, and standard deviation. ▪ The more the data are concentrated, the smaller the range, variance, and standard deviation. ▪ If the values are all the same (no variation), all these measures will be zero. ▪ None of these measures are ever negative. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-26  Measures of Variation: The Coefficient of Variation ◼ Measures relative variation ◼ Always in percentage (%) ◼ Shows variation relative to mean ◼ Can be used to compare the variability of two or more sets of data measured in different units  S  CV =    100%   X  Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-27  Measures of Variation: Comparing Coefficients of Variation ◼ Stock A: ◼ Average price last year = $50 ◼ Standard deviation = $5 S $5   CV A =    100% =  100% = 10% X $50 Both stocks have the same ◼ Stock B: standard ◼ Average price last year = $100 deviation, but stock B is less ◼ Standard deviation = $5 variable relative to its price S $5 CVB =    100% =  100% = 5% X $100 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-28  Locating Extreme Outliers: Z-Score ▪ To compute the Z-score of a data value, subtract the mean and divide by the standard deviation. ▪ The Z-score is the number of standard deviations a data value is from the mean. ▪ A data value is considered an extreme outlier if its Z- score is less than -2.0 or greater than +2.0. ▪ The larger the absolute value of the Z-score, the farther the data value is from the mean. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-29  Locating Extreme Outliers: Z-Score X−X Z= S where X represents the data value X is the sample mean S is the sample standard deviation Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-30  Locating Extreme Outliers: Z-Score ▪ Suppose the mean math SAT score is 490, with a standard deviation of 100. ▪ Compute the Z-score for a test score of 620. X − X 620 − 490 130 Z= = = = 1.3 S 100 100 A score of 620 is 1.3 standard deviations above the mean and would not be considered an outlier. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-31  Example: ◼ Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-32  ◼ The results show that the height of 76.2in. is 2.60 standard deviations above the mean height, and the weight of 237.1lb is 2.45 standard deviations above the mean weight. Because the height is more standard deviations above the mean, it is the more extreme value. The height of 76.2in. is more extreme than the weight of 237.1lb Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-33  Shape of a Distribution ◼ Describes how data are distributed ◼ Measures of shape ◼ Symmetric or skewed Left-Skewed Symmetric Right-Skewed Mean < Median Mean = Median Median < Mean Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-34  Skewness Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-35  General Descriptive Stats Using Microsoft Excel 1. Select Data Analysis. 2. Select Descriptive Statistics and click OK. (e.g., Description_Lecture3) Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-36  General Descriptive Stats Using Microsoft Excel 4. Enter the cell range. 5. Check the Summary Statistics box. 6. Click OK Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-37  Excel output Microsoft Excel descriptive statistics output, using the house price data: House Prices: $2,000,000 500,000 300,000 100,000 100,000 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-38  Numerical Descriptive Measures for a Population ▪ Descriptive statistics discussed previously described a sample, not the population. ▪ Summary measures describing a population, called parameters, are denoted with Greek letters. ▪ Important population parameters are the population mean, variance, and standard deviation. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-39  Numerical Descriptive Measures for a Population: The mean µ ◼ The population mean is the sum of the values in the population divided by the population size, N N X i X1 + X 2 +  + XN = i=1 = N N Where μ = population mean N = population size Xi = ith value of the variable X Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-40  Numerical Descriptive Measures For A Population: The Variance σ2 ◼ Average of squared deviations of values from the mean N ◼ Population variance:  (X − μ) i 2 σ2 = i=1 N Where μ = population mean N = population size Xi = ith value of the variable X Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-41  Numerical Descriptive Measures For A Population: The Standard Deviation σ ◼ Most commonly used measure of variation ◼ Shows variation about the mean ◼ Is the square root of the population variance ◼ Has the same units as the original data N ◼ Population standard deviation:  i (X − μ) 2 σ= i=1 N Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-42 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-43  Example: ◼ Following are the 2009 earnings (in thousands of dollars) before taxes for all six employees of a small company. 88.50 108.40 65.50 52.50 79.80 54.60 ◼ Calculate the standard deviation for these data. Answer: 19.721 thousands Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-44  Sample statistics versus population parameters Measure Population Sample Parameter Statistic Mean  X Variance 2 S2 Standard  S Deviation Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-45  The Empirical Rule ◼ The empirical rule approximates the variation of data in a bell-shaped distribution ◼ Approximately 68% of the data in a bell shaped distribution is within 1 standard deviation of the mean or μ  1σ 68% μ μ  1σ Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-46  The Empirical Rule ◼ Approximately 95% of the data in a bell-shaped distribution lies within two standard deviations of the mean, or µ ± 2σ ◼ Approximately 99.7% of the data in a bell-shaped distribution lies within three standard deviations of the mean, or µ ± 3σ 95% 99.7% μ  2σ μ  3σ Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-47  Using the Empirical Rule ▪ Suppose that the variable Math SAT scores is bell- shaped with a mean of 500 and a standard deviation of 90. Then, ▪ 68% of all test takers scored between 410 and 590 (500 ± 90). ▪ 95% of all test takers scored between 320 and 680 (500 ± 180). ▪ 99.7% of all test takers scored between 230 and 770 (500 ± 270). Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-48  Chebyshev Rule ◼ Regardless of how the data are distributed, at least (1 - 1/k2) x 100% of the values will fall within k standard deviations of the mean (for k > 1) ◼ Examples: At least within (1 - 1/22) x 100% = 75% …........ k=2 (μ ± 2σ) (1 - 1/32) x 100% = 89% ………. k=3 (μ ± 3σ) Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-49  Using the Chebyshev Rule ▪ Suppose that the variable Math SAT scores has a mean of 500 and a standard deviation of 90. Then, ▪ At least 75% of all test takers scored between 320 and 680 (500 ± 180). ▪ At least 89% of all test takers scored between 230 and 770 (500 ± 270). Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-50  Quartile Measures ◼ Quartiles split the ranked data into 4 segments with an equal number of values per segment 25% 25% 25% 25% Q1 Q2 Q3 ◼ The first quartile, Q1, is the value for which 25% of the observations are smaller and 75% are larger ◼ Q2 is the same as the median (50% of the observations are smaller and 50% are larger) ◼ Only 25% of the observations are greater than the third quartile Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-51  Quartile Measures: Locating Quartiles Find a quartile by determining the value in the appropriate position in the ranked data, where First quartile position: Q1 = (n+1)/4 ranked value Second quartile position: Q2 = (n+1)/2 ranked value Third quartile position: Q3 = 3(n+1)/4 ranked value where n is the number of observed values Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-52  Quartile Measures: Calculation Rules ◼ When calculating the ranked position use the following rules ◼ If the result is a whole number then it is the ranked position to use ◼ If the result is a fractional half (e.g. 2.5, 7.5, 8.5, etc.) then average the two corresponding data values. ◼ If the result is not a whole number or a fractional half then round the result to the nearest integer to find the ranked position. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-53  Quartile Measures: Locating Quartiles Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22 (n = 9) Q1 is in the (9+1)/4 = 2.5 position of the ranked data so use the value half way between the 2nd and 3rd values, so Q1 = 12.5 Q1 and Q3 are measures of non-central location Q2 = median, is a measure of central tendency Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-54  Quartile Measures Calculating The Quartiles: Example Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22 (n = 9) Q1 is in the (9+1)/4 = 2.5 position of the ranked data, so Q1 = (12+13)/2 = 12.5 Q2 is in the (9+1)/2 = 5th position of the ranked data, so Q2 = median = 16 Q3 is in the 3(9+1)/4 = 7.5 position of the ranked data, so Q3 = (18+21)/2 = 19.5 Q1 and Q3 are measures of non-central location Q2 = median, is a measure of central tendency Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-55  Quartile Measures: The Interquartile Range (IQR) ◼ The IQR is Q3 – Q1 and measures the spread in the middle 50% of the data ◼ The IQR is also called the midspread because it covers the middle 50% of the data ◼ The IQR is a measure of variability that is not influenced by outliers or extreme values ◼ Measures like Q1, Q3, and IQR that are not influenced by outliers are called resistant measures Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-56  Calculating The Interquartile Range Example: Median X X Q1 Q3 maximum minimum (Q2) 25% 25% 25% 25% 12 30 45 57 70 Interquartile range = 57 – 30 = 27 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-57  The Five Number Summary The five numbers that help describe the center, spread and shape of data are: ▪ Xsmallest ▪ First Quartile (Q1) ▪ Median (Q2) ▪ Third Quartile (Q3) ▪ Xlargest Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-58  Relationships among the five-number summary and distribution shape Left-Skewed Symmetric Right-Skewed Median – Xsmallest Median – Xsmallest Median – Xsmallest > ≈ < Xlargest – Median Xlargest – Median Xlargest – Median Q1 – Xsmallest Q1 – Xsmallest Q1 – Xsmallest > ≈ < Xlargest – Q3 Xlargest – Q3 Xlargest – Q3 Median – Q1 Median – Q1 Median – Q1 > ≈ < Q3 – Median Q3 – Median Q3 – Median Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-59  Five Number Summary and The Boxplot ◼ The Boxplot: A Graphical display of the data based on the five-number summary: Xsmallest -- Q1 -- Median -- Q3 -- Xlargest Example: 25% of data 25% 25% 25% of data of data of data Xsmallest Q1 Median Q3 Xlargest Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-60  Five Number Summary: Shape of Boxplots ◼ If data are symmetric around the median then the box and central line are centered between the endpoints Xsmallest Q1 Median Q3 Xlargest ◼ A Boxplot can be shown in either a vertical or horizontal orientation Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-61  Distribution Shape and The Boxplot Left-Skewed Symmetric Right-Skewed Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-62  Boxplot Example ◼ Below is a Boxplot for the following data: Xsmallest Q1 Q2 Q3 Xlargest 0 2 2 2 3 3 4 5 5 9 27 00 22 33 55 27 27 ◼ The data are right skewed, as the plot depicts Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-63  Boxplot example showing an outlier The boxplot below of the same data shows the outlier value of 27 plotted separately A value is considered an outlier if it is more than 1.5 times the interquartile range below Q1 or above Q3 Example Boxplot Showing An Outlier 0 5 10 15 20 25 30 Sample Data Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-64  The Covariance ◼ The covariance measures the strength of the linear relationship between two numerical variables (X & Y) ◼ The sample covariance: n  ( X − X)( Y − Y ) i i cov ( X , Y ) = i=1 n −1 ◼ Only concerned with the strength of the relationship ◼ No causal effect is implied Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-65  Interpreting Covariance ◼ Covariance between two variables: cov(X,Y) > 0 X and Y tend to move in the same direction cov(X,Y) < 0 X and Y tend to move in opposite directions cov(X,Y) = 0 X and Y are independent ◼ The covariance has a major flaw: ◼ It is not possible to determine the relative strength of the relationship from the size of the covariance Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-66  Coefficient of Correlation ◼ Measures the relative strength of the linear relationship between two numerical variables ◼ Sample coefficient of correlation: (e.g., cost of living_lecture3) cov (X , Y) r= SX SY wheren  (X − X)(Y − Y) n n i i  (X − X) i 2  (Y − Y ) i 2 cov (X , Y) = i=1 SX = i=1 SY = i=1 n −1 n −1 n −1 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-67  Features of the Coefficient of Correlation ◼ The population coefficient of correlation is referred as ρ. ◼ The sample coefficient of correlation is referred to as r. ◼ Either ρ or r have the following features: ◼ Unit free ◼ Ranges between –1 and 1 ◼ The closer to –1, the stronger the negative linear relationship ◼ The closer to 1, the stronger the positive linear relationship ◼ The closer to 0, the weaker the linear relationship Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-68  Scatter Plots of Sample Data with Various Coefficients of Correlation Y Y X X r = -1 r = -.6 Y Y Y X X X r = +1 r = +.3 r=0 Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-69  The Coefficient of Correlation Using Microsoft Excel 1. Select Data Analysis 2. Choose Correlation from the selection menu 3. Click OK... Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-70  The Coefficient of Correlation Using Microsoft Excel 4. Input data range and select appropriate options 5. Click OK to get output Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-71  Interpreting the Coefficient of Correlation Using Microsoft Excel ▪ r =.733 Scatter Plot of Test Scores 100 ▪ There is a relatively 95 Test #2 Score strong positive linear 90 relationship between test 85 score #1 and test score 80 #2. 75 70 70 75 80 85 90 95 100 Test #1 Score ▪ Students who scored high on the first test tended to score high on second test. Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-72  Pitfalls in Numerical Descriptive Measures ◼ Data analysis is objective ◼ Should report the summary measures that best describe and communicate the important aspects of the data set ◼ Data interpretation is subjective ◼ Should be done in fair, neutral and clear manner Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-73  Ethical Considerations Numerical descriptive measures: ◼ Should document both good and bad results ◼ Should be presented in a fair, objective and neutral manner ◼ Should not use inappropriate summary measures to distort facts Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-74  Chapter Summary ◼ Described measures of central tendency ◼ Mean, median, mode ◼ Described measures of variation ◼ Range, interquartile range, variance and standard deviation, coefficient of variation, Z-scores ◼ Illustrated shape of distribution ◼ Symmetric, skewed ◼ Described data using the 5-number summary ◼ Boxplots Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-75  Chapter Summary (continued) ◼ Discussed covariance and correlation coefficient ◼ Addressed pitfalls in numerical descriptive measures and ethical considerations Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-76