Measures of Variation

Total Page:16

File Type:pdf, Size:1020Kb

Measures of Variation Lesson 10 Measures of Variation SURVEYS Jamie asked her classmates how many Glasses of Water Consumed Main Idea glasses of water they drink on a typical day. × × Find the measures of 1. What is the median of the data set? × × × × × × × × variation of a set of 2. Organize the data into two groups: the top × × × × × × data. half and the bottom half. How many data 012345 values are in each group? New Vocabulary 3. measures of variation What is the median of each group? range 4. Find the difference between the two numbers from Exercise 3. quartile lower quartile upper quartile Measures of variation are used to describe the distribution of the data. interquartile range The range is the difference between the greatest and least data values. outlier Quartiles are values that divide the data set into four equal parts. Math Online LQ median UQ glencoe.com 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 4, 5, 5, 5 ⎧ ⎨ ⎩ ⎧ ⎨ ⎩ The median of the lower The median of the upper 6.SP.3, 6.SP.5c half of a set of data is half of a set of data is the lower quartile or the upper quartile or LQ; in this case, 1. UQ; in this case, 3.5. One fourth of the data lie below the lower quartile and one fourth of the data lie above the upper quartile. So, one half of the data lie between the lower quartile and upper quartile. This is called the interquartile range. Measures of Variation Upper and Lower Quartiles The upper and lower quartiles are the medians of the upper half and lower half of a set of data, respectively. 14, 18, 19, 20, 24, 29, 31 lower quartile median upper quartile Interquartile Range The range of the middle half of the data. It is the difference between the upper quartile and the lower quartile; in this case, 29 - 18, or 11. The interquartile range is 29 - 18, or 11. Range The difference between the greatest and least data values; in this case, 31 - 14, or 17. Lesson 10 Measures of Variation 39 Find Measures of Variation SPEED Find the measures Animal Speeds of variation for the data. Animal Speed (mph) Range 70 - 1 or 69 mph cheetah 70 lion 50 Quartiles cat 30 elephant 25 Order the numbers from mouse 8 least to greatest. spider 1 lower half median upper half ⎩ ⎨ ⎧ ⎩ ⎨ ⎧ 1 8 25 30 50 70 ⎩ ⎧ ⎨ 25 + 30 LQ _ = 27.5 UQ 2 Interquartile Range 50 - 8 or 42 UQ - LQ The range is 69, the median is 27.5, the lower quartile is 8, the Interquartile Range If the upper quartile is 50, and the interquartile range is 42. interquartile range is low, the middle data are Basketball Scores grouped closely together. a. SPORTS Determine the measures 64 61 67 59 60 of variation for the data in the table. 58 57 71 56 62 An outlier is a data value that is either much greater or much less than the median. If a data value is more than 1.5 times the value of the interquartile range beyond the quartiles, it is an outlier. Find Outliers ELECTIONS The ages of candidates in an election are 23, 48, 49, 55, 57, 63, and 72. Name any outliers in the data. Find the interquartile range. 63 - 48 = 15 Multiply the interquartile range by 1.5. 15 × 1.5 = 22.5 Subtract 22.5 from the lower quartile and add 22.5 to the upper quartile. 48 - 22.5 = 25.5 63 + 22.5 = 85.5 The limits for the outliers are between 25.5 and 85.5. The only age beyond this is 23. So, it is the only outlier. b. BRIDGES The lengths, in feet, of various bridges are 88, 251, 275, 354, and 1,121. Name any outliers in the data set. Analyze Data SCIENCE The table shows a set of scores on a Room Room science test in two different classrooms. Compare A B and contrast their measures of variation. 72 63 Find the measures of variation for both rooms. 100 93 67 79 Room A Room B 84 83 Range 100 - 65 = 35 98 - 63 = 35 65 98 Median 80 81 78 87 87 + 92 87 + 93 UQ _ = 89.5 _ = 90 92 73 2 2 87 81 67 + 72 65 + 73 LQ _ = 69.5 _ = 69 80 65 2 2 Interquartile 89.5 - 69.5 = 20 90 - 69 = 21 Range Both classrooms have a range of 35, but Room B has an interquartile range of 21 while Room A’s interquartile range is 20. There are slight differences in the medians as well as the upper and lower quartiles. Antelope, Augusta, Month MT ME c. WEATHER Temperatures for the January 21 28 first half of the year are given for Antelope, Montana, and February 30 32 Augusta, Maine. Compare and March 42 41 contrast the measures of April 58 53 variation of the two cities. May 70 66 June 79 75 Examples 1 and 2 1. WIND SPEED The average wind Wind Speed speeds for several cities in Speed Pennsylvania are given in the table. Pennsylvania City (mph) a. Find the range of the data. Allentown 8.9 b. Find the median and the lower and Erie 11.0 upper quartiles. Harrisburg 7. 5 Middletown 7. 7 c. Find the interquartile range. Philadelphia 9.5 d. Identify any outliers in the data. Pittsburgh 9.0 Williamsport 7. 6 Example 3 2. TREES The heights of several types of palm trees, in feet, are 40, 25, 15, 22, 50, and 30. The heights of several types of pine trees, in feet, are 60, 75, 45, 80, 75, and 70. Compare and contrast the measures of variation of both kinds of trees. Examples 1 and 2 3. GOLF COURSES The table shows the number Number of Golf Courses of golf courses in various states. California 1,117 a. Find the range of the data. Florida 1,465 b. Find the median and the lower and upper Georgia 513 quartiles. Iowa 437 Michigan 1,038 c. Find the interquartile range. New York 954 d. Name any outliers in the data. North Carolina 650 Ohio 893 South Carolina 456 Texas 1,018 4. INTERNET The table shows the countries Millions of Internet Users with the most Internet users. China 99.8 a. Find the range of the data. Germany 41.88 b. Find the median and the lower and upper India 36.97 quartiles. Japan 78.05 c. Find the interquartile range. South Korea 31.67 d. Name any outliers in the data. United Kingdom 33.11 United States 185.55 Example 3 5. EXERCISE The table shows the number Minutes of Exercise of minutes of exercise for each person. Week 1 Week 2 Compare and contrast the measures of Tanika 45 30 variation for both weeks. Tasha 40 55 Tyrone 45 35 Uniqua 55 60 Videl 60 45 Wesley 90 75 6. FOOTBALL The table Penalties By NFL Teams shows the top teams NFC AFC in the National Football Dallas Cowboys 104 New England Patriots 78 Conference (NFC) and Arizona Cardinals 137 Indianapolis Colts 67 the American Football Green Bay Packers 113 Jacksonville Jaguars 76 Conference (AFC). New Orleans Saints 68 San Diego Chargers 94 a. Which conference had New York Giants 77 Cleveland Browns 114 a greater range of Seattle Seahawks 59 Pittsburgh Steelers 80 penalties? Minnesota Vikings 86 Houston Texans 82 b. Find the measures of variation for each conference. c. Compare and contrast the measures of variation for each conference. For each data set, find the median, the lower and upper quartiles, and the interquartile range. 7. daily attendance at the water park: 346, 250, 433, 369, 422, 298 8. texts per day: 24, 53, 38, 12, 31, 19, 26 9. cost of admission: $13.95, $24.59, $19.99, $29.98, $23.95, $28.99 B 10. SCIENCE The table shows the number of Known Moons of Planets known moons for each planet in our solar Mercury 0 Jupiter 63 system. Use the measures of variation to Venus 0 Saturn 34 describe the data. Earth 1 Uranus 27 Mars 2 Neptune 13 11. EXERCISE Lucy and Dena are training for a bike race and recorded their mileage for a week. Find the measures of variation of each person’s mileage. Which measures of variation show the girls’ similarities in their training? the differences? Explain. Monday Tuesday Wednesday Thursday Friday Saturday Sunday Lucy 7 mi 3 mi 5 mi 8 mi 6 mi 10 mi 9 mi Dena 6 mi 4 mi 6 mi 8 mi 11 mi 9 mi 7 mi C 12. FIND THE ERROR Hiroshi was finding the measures of variation of the following set of data: 89, 93, 99, 110, 128, 135, 144, 152, and 159. Find his mistake and correct it. median = 128 lower quartile = 99 upper quartile = 144 interquartile range = 45 range = 70 13. OPEN ENDED Create a list of data with at least six numbers that has an interquartile range of 15 and two outliers. 14. WRITE MATH Explain why the median is not affected by very high or very low values in the data. Lesson 10 Measures of Variation 43 TestT Practice 15. The number of games won by 10 chess 17. The normal monthly rainfall in inches players is given. for a city are given in the table. 13, 15, 2, 7, 5, 9, 11, 10, 12, 11 Jan Feb Mar Apr May June 0.65 1.39 0.63 2.16 2.82 4.21 Which of the following statements is July Aug Sept Oct Nov Dec NOT supported by these data? 3.22 1.20 9.31 11.25 0.70 0.80 A.
Recommended publications
  • Lesson 7: Measuring Variability for Skewed Distributions (Interquartile Range)
    NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 7 M2 ALGEBRA I Lesson 7: Measuring Variability for Skewed Distributions (Interquartile Range) Student Outcomes . Students explain why a median is a better description of a typical value for a skewed distribution. Students calculate the 5-number summary of a data set. Students construct a box plot based on the 5-number summary and calculate the interquartile range (IQR). Students interpret the IQR as a description of variability in the data. Students identify outliers in a data distribution. Lesson Notes Distributions that are not symmetrical pose some challenges in students’ thinking about center and variability. The observation that the distribution is not symmetrical is straightforward. The difficult part is to select a measure of center and a measure of variability around that center. In Lesson 3, students learned that, because the mean can be affected by unusual values in the data set, the median is a better description of a typical data value for a skewed distribution. This lesson addresses what measure of variability is appropriate for a skewed data distribution. Students construct a box plot of the data using the 5-number summary and describe variability using the interquartile range. Classwork Exploratory Challenge 1/Exercises 1–3 (10 minutes): Skewed Data and Their Measure of Center Verbally introduce the data set as described in the introductory paragraph and dot plot shown below. Exploratory Challenge 1/Exercises 1–3: Skewed Data and Their Measure of Center Consider the following scenario. A television game show, Fact or Fiction, was cancelled after nine shows. Many people watched the nine shows and were rather upset when it was taken off the air.
    [Show full text]
  • Normal Probability Distribution
    MATH2114 Week 8, Thursday The Normal Distribution Slides Adopted & modified from “Business Statistics: A First Course” 7th Ed, by Levine, Szabat and Stephan, 2016 Pearson Education Inc. Objective/Goals To compute probabilities from the normal distribution How to use the normal distribution to solve practical problems To use the normal probability plot to determine whether a set of data is approximately normally distributed Continuous Probability Distributions A continuous variable is a variable that can assume any value on a continuum (can assume an uncountable number of values) thickness of an item time required to complete a task temperature of a solution height, in inches These can potentially take on any value depending only on the ability to precisely and accurately measure The Normal Distribution Bell Shaped f(X) Symmetrical Mean=Median=Mode σ Location is determined by X the mean, μ μ Spread is determined by the standard deviation, σ Mean The random variable has an = Median infinite theoretical range: = Mode + to The Normal Distribution Density Function The formula for the normal probability density function is 2 1 (X μ) 1 f(X) e 2 2π Where e = the mathematical constant approximated by 2.71828 π = the mathematical constant approximated by 3.14159 μ = the population mean σ = the population standard deviation X = any value of the continuous variable The Normal Distribution Density Function By varying 휇 and 휎, we obtain different normal distributions Image taken from Wikipedia, released under public domain The
    [Show full text]
  • 2018 Statistics Final Exam Academic Success Programme Columbia University Instructor: Mark England
    2018 Statistics Final Exam Academic Success Programme Columbia University Instructor: Mark England Name: UNI: You have two hours to finish this exam. § Calculators are necessary for this exam. § Always show your working and thought process throughout so that even if you don’t get the answer totally correct I can give you as many marks as possible. § It has been a pleasure teaching you all. Try your best and good luck! Page 1 Question 1 True or False for the following statements? Warning, in this question (and only this question) you will get minus points for wrong answers, so it is not worth guessing randomly. For this question you do not need to explain your answer. a) Correlation values can only be between 0 and 1. False. Correlation values are between -1 and 1. b) The Student’s t distribution gives a lower percentage of extreme events occurring compared to a normal distribution. False. The student’s t distribution gives a higher percentage of extreme events. c) The units of the standard deviation of � are the same as the units of �. True. d) For a normal distribution, roughly 95% of the data is contained within one standard deviation of the mean. False. It is within two standard deviations of the mean e) For a left skewed distribution, the mean is larger than the median. False. This is a right skewed distribution. f) If an event � is independent of �, then � (�) = �(�|�) is always true. True. g) For a given confidence level, increasing the sample size of a survey should increase the margin of error.
    [Show full text]
  • L-Moments Based Assessment of a Mixture Model for Frequency Analysis of Rainfall Extremes
    Advances in Geosciences, 2, 331–334, 2006 SRef-ID: 1680-7359/adgeo/2006-2-331 Advances in European Geosciences Union Geosciences © 2006 Author(s). This work is licensed under a Creative Commons License. L-moments based assessment of a mixture model for frequency analysis of rainfall extremes V. Tartaglia, E. Caporali, E. Cavigli, and A. Moro Dipartimento di Ingegneria Civile, Universita` degli Studi di Firenze, Italy Received: 2 December 2004 – Revised: 2 October 2005 – Accepted: 3 October 2005 – Published: 23 January 2006 Abstract. In the framework of the regional analysis of hy- events in Tuscany (central Italy), a statistical methodology drological extreme events, a probabilistic mixture model, for parameter estimation of a probabilistic mixture model is given by a convex combination of two Gumbel distributions, here developed. The use of a probabilistic mixture model in is proposed for rainfall extremes modelling. The papers con- the regional analysis of rainfall extreme events, comes from cerns with statistical methodology for parameter estimation. the hypothesis that rainfall phenomenon arises from two in- With this aim, theoretical expressions of the L-moments of dependent populations (Becchi et al., 2000; Caporali et al., the mixture model are here defined. The expressions have 2002). The first population describes the basic components been tested on the time series of the annual maximum height corresponding to the more frequent events of low magnitude, of daily rainfall recorded in Tuscany (central Italy). controlled by the local morpho-climatic conditions. The sec- ond population characterizes the outlier component of more intense and rare events, which is here considered due to the aggregation at the large scale of meteorological phenomena.
    [Show full text]
  • Estimation of Quantiles and the Interquartile Range in Complex Surveys Carol Ann Francisco Iowa State University
    Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1987 Estimation of quantiles and the interquartile range in complex surveys Carol Ann Francisco Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Statistics and Probability Commons Recommended Citation Francisco, Carol Ann, "Estimation of quantiles and the interquartile range in complex surveys " (1987). Retrospective Theses and Dissertations. 11680. https://lib.dr.iastate.edu/rtd/11680 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS While the most advanced technology has been used to photograph and reproduce this manuscript, the quality of the reproduction is heavily dependent upon the quality of the material submitted. For example: • Manuscript pages may have indistinct print. In such cases, the best available copy has been filmed. • Manuscripts may not always be complete. In such cases, a note will indicate that it is not possible to obtain missing pages. • Copyrighted material may have been removed from the manuscript. In such cases, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, and charts) are photographed by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each oversize page is also filmed as one exposure and is available, for an additional charge, as a standard 35mm slide or as a 17"x 23" black and white photographic print.
    [Show full text]
  • Hand-Book on STATISTICAL DISTRIBUTIONS for Experimentalists
    Internal Report SUF–PFY/96–01 Stockholm, 11 December 1996 1st revision, 31 October 1998 last modification 10 September 2007 Hand-book on STATISTICAL DISTRIBUTIONS for experimentalists by Christian Walck Particle Physics Group Fysikum University of Stockholm (e-mail: [email protected]) Contents 1 Introduction 1 1.1 Random Number Generation .............................. 1 2 Probability Density Functions 3 2.1 Introduction ........................................ 3 2.2 Moments ......................................... 3 2.2.1 Errors of Moments ................................ 4 2.3 Characteristic Function ................................. 4 2.4 Probability Generating Function ............................ 5 2.5 Cumulants ......................................... 6 2.6 Random Number Generation .............................. 7 2.6.1 Cumulative Technique .............................. 7 2.6.2 Accept-Reject technique ............................. 7 2.6.3 Composition Techniques ............................. 8 2.7 Multivariate Distributions ................................ 9 2.7.1 Multivariate Moments .............................. 9 2.7.2 Errors of Bivariate Moments .......................... 9 2.7.3 Joint Characteristic Function .......................... 10 2.7.4 Random Number Generation .......................... 11 3 Bernoulli Distribution 12 3.1 Introduction ........................................ 12 3.2 Relation to Other Distributions ............................. 12 4 Beta distribution 13 4.1 Introduction .......................................
    [Show full text]
  • Descriptive Statistics
    NCSS Statistical Software NCSS.com Chapter 200 Descriptive Statistics Introduction This procedure summarizes variables both statistically and graphically. Information about the location (center), spread (variability), and distribution is provided. The procedure provides a large variety of statistical information about a single variable. Kinds of Research Questions The use of this module for a single variable is generally appropriate for one of four purposes: numerical summary, data screening, outlier identification (which sometimes is incorporated into data screening), and distributional shape. We will briefly discuss each of these now. Numerical Descriptors The numerical descriptors of a sample are called statistics. These statistics may be categorized as location, spread, shape indicators, percentiles, and interval estimates. Location or Central Tendency One of the first impressions that we like to get from a variable is its general location. You might think of this as the center of the variable on the number line. The average (mean) is a common measure of location. When investigating the center of a variable, the main descriptors are the mean, median, mode, and the trimmed mean. Other averages, such as the geometric and harmonic mean, have specialized uses. We will now briefly compare these measures. If the data come from the normal distribution, the mean, median, mode, and the trimmed mean are all equal. If the mean and median are very different, most likely there are outliers in the data or the distribution is skewed. If this is the case, the median is probably a better measure of location. The mean is very sensitive to extreme values and can be seriously contaminated by just one observation.
    [Show full text]
  • Measures of Variability
    CHAPTER 4 Measures of Variability Chapter 3 introduced descriptive statistics, the purpose of which is to numerically describe or summarize data for a Chapter Outline variable. As we learned in Chapter 3, a set of data is often 4.1 An Example From the described in terms of the most typical, common, or frequently Research: How Many “Sometimes” occurring score by using a measure of central tendency such in an “Always”? as the mode, median, or mean. Measures of central ten- 4.2 Understanding Variability dency numerically describe two aspects of a distribution of scores, modality and symmetry, based ondistribute what the scores 4.3 The Range have in common with each other. However, researchers • Strengths and weaknesses of also describe the amount of differences among the scores of a the range variable in analyzing and reportingor their data. This chapter 4.4 The Interquartile Range introduces measures of variability, which are designed to rep- • Strengths and weaknesses of resent the amount of differences in a distribution of data. In the interquartile range this chapter, we will present and evaluate different measures 4.5 The Variance (s2) of variability in terms of their relative strengths and weak- • Definitional formula for the nesses. As in the previous chapters, our presentation will variance depend heavily on the findings and analysis from a published • Computational formula for the research study.post, variance • Why not use the absolute value of the deviation in 4.1 An Example From the calculating the variance? Research: How Many • Why divide by N – 1 rather than “Sometimes” in an “Always”? N in calculating the variance? 4.6 The Standard Deviation (s) copy,Throughout our daily lives, we are constantly asked to fill out • Definitional formula for the questionnaires and surveys.
    [Show full text]
  • Measures of Spread
    community project encouraging academics to share statistics support resources All stcp resources are released under a Creative Commons licence stcp-gilchristsamuels-6 The following resources are associated: Descriptive Statistics – Measures of Middle Value Descriptive Statistics – Measures of Spread Research question type: Most What kind of variables: Nominal, ordinal and interval/scale Common Applications: Most quantitative studies Descriptive statistics are a way of summarising your data into a few values, often for comparative purposes. Commonly they are presented in tables, although appropriate charts are also used. They include measures of middle values (or central tendency), and measures of spread (or dispersion) of your data. The following is a rough guide to appropriate measures for level of data: Data level Middle value (central tendency) Dispersion (spread) Nominal Mode Ordinal Mode, median Range, interquartile range (IQR) Interval/scale Median, mean Variance, standard deviation Range The range is defined as the difference between the maximum and the minimum values of a set of data. Example Find the range for: 2, 6, 3, 9, 5, 6, 2, 6. The maximum value of the data is 9, and the minimum value is 2. Hence the range is 9 - 2 = 7. Interquartile range (IQR) Quartiles split a dataset into four quarters when the values are written in ascending order. !!! The lower quartile is 25% of the way through a data set. This is the th value, where n is the ! number of data values. (Note: Quarter values are found by starting with the lower value and adding either ¼ or ¾ of the difference. For example, the 2¼th value is the 2nd value plus ¼ of the 3rd value minus the 2nd).
    [Show full text]
  • Chapter 4 – Analyzing Skewed Quantitative Data Introduction: in Chapter 3, We Focused on Analyzing Bell Shaped (Normal) Data, but Many Data Sets Are Not Bell Shaped
    Chapter 4 – Analyzing Skewed Quantitative Data Introduction: In chapter 3, we focused on analyzing bell shaped (normal) data, but many data sets are not bell shaped. How do we analyze quantitative data when it is not bell shaped? The main issue is that the mean and standard deviations are not accurate and should not be used in the analysis. Then what statistics should we use? We will be introducing a new kind of graph that is specially designed for analyzing skewed data. It is called the “box and whisker plot” or “box plot” for short. When data sets are not bell shaped, we will focus on the median, quartiles, interquartile range and boxplots to measure center and spread. Quartiles are more accurate because they are based on the order of the numbers instead of distances and so are not as effected by the skewed shape and extremely unusual values. 143 Section 4A – Review of Shapes and Centers, Creating Histograms and Dot Plots with Technology Let us start by reviewing how to create dot plots and histograms with technology and determine the shape of a quantitative data set. Here are the directions for making dot plots and histograms in Statcato. Making a dot plot in Statcato: Graph => Dot plot => Pick a column => OK Making a histogram in Statcato: Graph => Histogram => Pick a column => Chose number of bins => OK Example 1 Use the women’s weight data (in pounds) from the Health data and Statcato to create a dot plot, histogram and boxplot. What is the shape of the data set? Plugging in the women’s weight data into Statcato, we can use the directions above to create the following graphs.
    [Show full text]
  • Outing the Outliers – Tails of the Unexpected
    Presented at the 2016 International Training Symposium: www.iceaaonline.com/bristol2016 Outing the Outliers – Tails of the Unexpected Outing the Outliers – Tails of the Unexpected Cynics would say that we can prove anything we want with statistics as it is all down to … a word (or two) from the Wise? interpretation and misinterpretation. To some extent this is true, but in truth it is all down to “The great tragedy of Science: the slaying making a judgement call based on the balance of of a beautiful hypothesis by an ugly fact” probabilities. Thomas Henry Huxley (1825-1895) British Biologist The problem with using random samples in estimating is that for a small sample size, the values could be on the “extreme” side, relatively speaking – like throwing double six or double one with a pair of dice, and nothing else for three of four turns. The more random samples we have the less likely (statistically speaking) we are to have all extreme values. So, more is better if we can get it, but sometimes it is a question of “We would if we could, but we can’t so we don’t!” Now we could argue that Estimators don’t use random values (because it sounds like we’re just guessing); we base our estimates on the “actuals” we have collected for similar tasks or activities. However, in the context of estimating, any “actual” data is in effect random because the circumstances that created those “actuals” were all influenced by a myriad of random factors. Anyone who has ever looked at the “actuals” for a repetitive task will know that there are variations in those values.
    [Show full text]
  • Problem Max. Points Your Points 1-10 10 11 10 12 3 13 4 14 18 15 8 16 7 17 14 Total 75
    M 140 Test 1 B Name__________________ (1 point) SHOW YOUR WORK FOR FULL CREDIT! Problem Max. Points Your Points 1-10 10 11 10 12 3 13 4 14 18 15 8 16 7 17 14 Total 75 Multiple choice questions (1 point each) For questions 1-3 consider the following: The distribution of scores on a certain statistics test is strongly skewed to the right. 1. How would the mean and the median compare for this distribution? a) mean < median b) mean = median c) mean > median d) Can’t tell without more information When a distribution skewed to the right, the “tail” is on the right. Since the tail “pulls” the mean, the mean is greater than the median. 2. Which set of measures of center and spread are more appropriate for the distribution of scores? a) Mean and standard deviation b) Median and interquartile range c) Mean and interquartile range d) Median and standard deviation For skewed distributions the median and the interquartile range are the appropriate measures of center and spread because they are resistant to outliers. 3. What does this suggest about the difficulty of the test? a) It was an easy test b) It was a hard test c) It wasn’t too hard or too easy d) It is impossible to tell If the distribution is skewed to the right, then most of the values are to the left, toward the lower values. Thus, more students got lower scores, which suggests that the test was hard. 4. Which one of the following variables is NOT categorical? a) whether or not an individual has glasses b) weight of an elephant c) religion d) social security number All the other variables are categorical—the average doesn’t make sense for those.
    [Show full text]