Week 1 September 2-6 - Oakland University

Week 1 September 2-6 - Oakland University

Week 1 September 1-5 Six Mini-Lectures QMM 510 Fall 2014 Chapter 0 Getting Started ML 1.1 self-introductions (Moodle mini-biographies) course format, syllabus, projects grading, communication goals: short run vs long run You can watch the instructors introductory welcome video for MBA students (posted on Moodle) 0-2

Chapter 0 Getting Started Textbook David P. Doane and Lori E. Seward, Applied Statistics in Business and Economics, 4th edition (McGraw-Hill, 2013), ISBN 0077931505. This is an omnibus ISBN that includes several components (textbook, Connect access, MegaStat download). All four components are essential because this is an online course. The Oakland University campus book center (248-370-2404) has this package ISBN in stock (and can ship to you if necessary). 0-3 Chapter 0 Getting Started Online Resources Homework, testing, and grading will utilize McGraw-Hill's

Connect Plus. The Online Learning Center (OLC) has downloadable data sets for exercises and examples, as well as Big Data Sets, PowerPoint slides, self-graded practice quizzes, and step-by-step guided examples. The instructor will post mini-lectures on Moodle. 0-4 Chapter 0 Getting Started Course Organization Unless otherwise indicated, online quizzes, exercises, and written projects are due by midnight on Monday of the week shown in the syllabus. Use e-mail ([email protected]) or call me (cell 248-766-7605) Note: Instructor is in the Pacific time zone (please use judgment when calling). Post questions on Moodle forum. 0-5

Chapter 0 Getting Started Grading Students will complete several written projects (50% weight, graded by instructor) and several Connect assignments with online feedback (50% weight). Basically, you will submit one assignment (Connect or Project) per week except for weeks 9 and 13. Grades will be posted on Moodle. 0-6 Chapter 0 Getting Started Homework using Connect C-1 Chapters 2-3 (Sep 8) C-5 Chapter 8 (Oct 20) C-2 Chapter 4 (Sep 15) C-6 Chapter 9-10 (Nov 3) C-3 Chapters 5-6 (Sep 29) C-4 Chapter 7 (Oct 6)

C-7 Chapter 15 (Nov 10) C-8 Chapter 12 (Nov 17) Note: Connect assignments allow three attempts. Online feedback increases with each attempt. Assignments will be auto-submitted on due date. Your score will be the average of all three attempts, so it pays to try hard on each attempt. You may complete them in advance (they are accessible anytime up to due date). Be sure to save your work when you exit Connect. 0-7 Chapter 0 Getting Started Projects P-1 Describing a sample (Sep 22) P-2 Making forecasts (Oct 13) P-3 Regression modeling (Dec 3) Note: For each project, submit a concise (5-10 page) report (not a

spreadsheet or PowerPoint) using Microsoft Word or equivalent that answers the questions posed along with your own comments and interpretations. Strive for effective writing (see textbook Appendix I). Creativity and initiative will be rewarded. In projects done with partners or teams, submit only one report. 0-8 Chapter 0 Goals: Short Run / Long Run Short Run Complete weekly assignments successfully Improve Excel and report-writing skills Balance this course against other responsibilities Enjoy learning and want to learn more Long Run Succeed in other MBA classes that use statistics Develop confidence and lose fear of quant methods Use resources to learn on your own (web, textbook)

0-9 Chapter 0 Resources Available ML 1.2 textbook, e-book OLC (http://www.mhhe.com/doane4e) Connect (http://connect.mcgraw-hill.com/class/d_doane_qmm_510__fall_2014) Moodle (https://moodle.oakland.edu/) MegaStat (http://www.mhhe.com/megastat) LearningStats (http://www.mhhe.com/doane4e) 0-10 Chapter 0 Resources Available

Textbook, e-book Basically, we will cover the first 14 chapters Within chapters some topics get less weight Focus on what you need for assignments Not covered

in this class 0-11 Chapter 0 Resources Available Pre-paid registration code is required to use Connect Plus Connect Plus (http://connect.mcgraw-hill.com/class/d_doane_qmm_510_-_fall_2014) E-book: In addition to textbook, you have an e-book Premium content: ScreenCam videos on Excel and MegaStat

0-12 Chapter 0 Resources Available Connect Plus (http://connect.mcgraw-hill.com/class/d_doane_qmm_510_-_fall_2014) OLC (http://www.mhhe.com/doane4e) A pre-paid registration code is required to use Connect Plus and premium content The OLC is available to anyone (without premium content)

Premium content: 5-minute tutorials on Excel and MegaStat 0-13 Connect Plus (http://connect.mcgraw-hill.com/class/d_doane_qmm_510_-_fall_2014) Chapter 0 Resources Available A pre-paid registration code is required to use Connect Plus and premium content ScreenCam tutorials on Excel statistics by

Professor Doane (4 videos, 5 min each) if you need it Chapter 0 Resources Available OLC (http://www.mhhe.com/doane4e) No registration code required to use OLC Course: Big Data Sets, LearningStats, etc Click on a chapter: Quizzes, PowerPoints for that chapter 0-15

Chapter 0 Resources Available MegaStat (http://www.mhhe.com/megastat) Click to download: Prepaid with code (with ISBN 0077931505) 0-16 Chapter 0 Resources Available MegaStat (http://www.mhhe.com/megastat) Drop-down menu: Adds statistical capability to Excel Add-Ins tab: Click on this tab to see MegaStat

drop-down menu 0-17 Chapter 0 Resources Available OLC (http://www.mhhe.com/doane4e) Files are zipped: Download one chapter at a time LearningStats is a supplement nice but not part of the textbook (demos, spreadsheets, slides) Appendix A F Tables (346.0K) Appendix I Business Reports (1011.0K) Unit 01 Overview of Statistics (5925.0K)

Unit 02 Data Collection (815.0K) Unit 03 Data Presentation (9572.0K) Unit 04 Describing Data (3337.0K) Unit 05 Probability (478.0K) Unit 06 Discrete Distributions (550.0K) Unit 07 Continuous Distributions (1409.0K) Unit 08 Estimation (2103.0K) Unit 09 Hypothesis Tests I (1135.0K) Unit 10 Hypothesis Tests II (420.0K) Unit 11 ANOVA (192.0K) Unit 12 Simple Regression (2245.0K) Unit 13 Multiple Regression (2756.0K) Unit 14 Time Series I (1519.0K) Unit 15 Chi Square Tests (627.0K) Unit 16 Nonparametric Tests (1385.0K) Unit 17 Quality Management (1329.0K) Unit 18 Simulation (1460.0K) 0-18 Chapter 1

Challenges for MBAs ML 1.3 1.1 What is Statistics? 1.2 Why Study Statistics? 1.3 Uses of Statistics 1.4 Statistical Challenges 1.5 Critical Thinking 0-19 Chapter 1 What is Statistics? Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data. A statistic is a single measure (number) used to summarize a sample data set; for example, the average height of students in a university.

1-20 Chapter 1 Big Data, Big Tools Data mining, neural tools, simulation, spreadsheet modeling, etc Costly software Specialized expertise required Huge databases (millions of records, complex file structure, sparse or

missing data, proprietary concerns, privacy issues) 1-21 Chapter 1 Uses of Statistics Descriptive statistics the collection, organization, presentation, and summary of data. Inferential statistics generalizing from a sample to a population, estimating unknown parameters, drawing conclusions, making decisions. 1-22 Chapter 1 Why Study Statistics

Statistical knowledge gives a company a competitive advantage against organizations that cannot understand their internal or external market data. Mastery of basic statistics gives an individual manager a competitive advantage as one works ones way through the promotion process, or when one moves to a new employer. 1-23 Chapter 1 The Ideal Data Analyst Is technically current (e.g., software-wise).

Communicates well. Is proactive. Has a broad outlook. Is flexible. Focuses on the main problem.

Meets deadlines Knows his/her limitations and is willing to ask for help. Can deal with imperfect information. Has professional integrity. 1-24 Chapter 1 Business Ethics Treat customers in a fair and honest manner.

Comply with laws that prohibit discrimination. Ensure that products and services meet safety regulations. Stand behind warranties. Advertise in a factual and informative manner. Encourage employees to ask questions and voice concerns.

Accurately report information to management. 1-25 Chapter 1 Upholding Ethical Standards Know and follow accepted procedures. Maintain data integrity. Carry out accurate calculations.

Report procedures faithfully. Protect confidential information. Cite sources. Acknowledge sources of financial support. 1-26 Chapter 1

Critical Thinking Pitfall 1: Big Conclusions from a Small Sample Pitfall 2: Conclusions from Nonrandom Samples Pitfall 3: Conclusions From Rare Events Pitfall 4: Poor Survey Methods Pitfall 5: Assuming a Causal Link Pitfall 6: Generalization from Groups Pitfall 7: Unconscious Bias Pitfall 8: Significance versus Importance 1-27 Chapter 1 Using Consultants Hire consultants at the beginning of the project, when your team lacks certain skills or when an unbiased or informed view is needed. 1-28

Chapter 2 Collecting Data ML 1.4 Chapter Contents 2.1 Definitions 2.2 Level of Measurement 2.3 Sampling Concepts 2.4 Sampling Methods 2.5 Data Sources 2.6 Surveys 2-29 Chapter 2 Definitions

Observation: a single member of a collection of items that we want to study, such as a person, firm, or region. Variable: a characteristic of the subject or individual, such as an employees income or an invoice amount Data Set: consists of all the values of all of the variables for all of the observations we have chosen to observe. 2-30 Chapter 2 Time Series vs Cross-Sectional Data Time Series Data

Each observation in the sample represents a different equally spaced point in time (e.g., years, months, days). Periodicity may be annual, quarterly, monthly, weekly, daily, hourly, etc. We are interested in trends and patterns over time (e.g., personal bankruptcies from 1980 to 2008). 2-31 Chapter 2 Time Series vs Cross-Sectional Data Cross Sectional Data Each observation represents a different individual unit (e.g., person) at

the same point in time (e.g., monthly VISA balances). We are interested in: - variation among observations or - relationships. We can combine the two data types to get pooled cross-sectional and time series data. 2-32 Chapter 2 Data Types Caution: Ambiguity is introduced when continuous data are rounded to whole numbers so they seem discrete (e.g., round your weight from

166.4 to 166). When the range is large, it is usually best to treat integers as continuous(Figure data.2.1) 2-33 Chapter 2 Level of Measurement 2-34 Chapter 2 Level of Measurement Level of Measurement Characteristics Example

Nominal Categories only Eye color (blue, brown, green, etc.) Ordinal Rank has meaning. No clear meaning to distance Exercise frequency (often, rarely, never) Interval Distance has meaning Temperature (57o Celsius)

Ratio Meaningful zero exists Accounts payable ($21.7 million) 2-35 Chapter 2 Level of Measurement Nominal Measurement Nominal data merely identify a category. Nominal data can be coded numerically (e.g., 1 = Apple, 2 = Toshiba, 3 = Dell, 4 = HP, 5 = Other). Only mathematical operation allowed is counting (e.g., frequencies) or calculating percent in each category. Ordinal Measurement

Ordinal data codes can be ranked (e.g., 1 = Frequently, 2 = Sometimes, 3 = Rarely, 4 = Never). 2-36 Chapter 2 Level of Measurement Ordinal Measurement Distance between codes is not meaningful (e.g., distance between 1 and 2, or between 2 and 3, or between 3 and 4 lacks meaning). Many useful statistical tests exist for ordinal data, especially in social science, marketing and human resource research. Interval Measurement Data can not only be ranked, but also have meaningful intervals between scale points (e.g., difference between 60F and 70F is same as difference between 20F and 30F). 2-37

Chapter 2 Level of Measurement Interval Measurement Intervals between numbers represent distances, so math operations can be performed (e.g., take the average). Zero point of interval scales is arbitrary, so ratios are not meaningful (e.g., 60F is not twice as warm as 30F). Ratio Measurement Ratio data have all properties of nominal, ordinal, and interval data types and also a meaningful zero. Because of this zero point, ratios of data values are meaningful (e.g., $20 million profit is twice as much as $10 million).

Zero does not have to be observable; it is a reference point. 2-38 Chapter 2 Likert Scales A special case of interval data frequently used in survey research. The coarseness of a Likert scale refers to the number of scale points (typically 5 or 7). Responses are often coded as numbers (e.g., 1, 2, 3, 4, 5) but technically are ordinal measurements. Researchers generally treat Likert scales as interval data (no true zero) so they can calculate the mean and standard deviation. 2-39 Chapter 2 Level of Measurement Use the following procedure to recognize data types:

Question If Yes Q1. Is there a meaningful zero point? Ratio data (statistical operations are allowed) Q2. Are intervals between scale points meaningful? Interval data (common statistics allowed, e.g., means and standard deviations) Q3. Do scale points represent rankings? Ordinal data (restricted to certain types of nonparametric statistical tests)

Q4. Are there discrete categories? Nominal data (only counting allowed, e.g., finding the mode) 2-40 Chapter 2 Changing Data By Recoding In order to simplify data or when exact data magnitude is of little interest, ratio data can be recoded downward into ordinal or nominal measurements (but not conversely). For example, recode systolic blood pressure as normal (under 130), elevated (130 to 140), or high (over 140). Or recode your income (a ratio measurement) as ordinal (low, medium, high) by specifying cutoff points. The above recoded data are ordinal (ranking is preserved), but intervals are unequal and some information is lost.

2-41 Chapter 2 Sample or Census? A sample involves looking only at some items selected from the population. A census is an examination of all items in a defined population. Why sample instead of census? Cost, time, budget constraints. Accuracy may be better in a sample (training, etc). For example, the United States Census cannot survey every person in the population (mobility, un-documented workers, budget constraints, incomplete responses, etc). 2-42 Chapter 2

Sampling Concepts Situations Where A Sample or Census May Be Preferred Sample Census Infinite population Small population Destructive testing Large sample size Timely results Database exists Accuracy

Legal requirements Cost Sensitive information 2-43 Chapter 2 Parameters and Statistics Statistics are computed from a sample of n items, chosen from a population of N items. Statistics can be used as estimates of parameters found in the population. Specific symbols are used to represent population parameters and sample statistics. Example: If you use the symbol s, the statistician assumes that you are referring to a sample standard deviation, whereas would denote a population standard deviation.

2-44 Chapter 2 Parameters and Statistics Rule of Thumb: A population may be treated as infinite when N is at least 20 times n (i.e., when N/n 20 or equivalently if n/N < .05). 2-45 Chapter 2 Sampling Methods Random Sampling Simple random sample Use random numbers to select items from a list (e.g., VISA cardholders).

Systematic sample Select every kth item from a list or sequence (e.g., restaurant customers). Stratified sample Select randomly within defined strata (e.g., by age, occupation, gender). Cluster sample Like stratified sampling except strata are geographical areas (e.g., zip codes). 2-46 Chapter 2 Sampling Methods Non-random Sampling Judgment sample

Use expert knowledge to choose typical items (e.g., which employees to interview). Convenience sample Use a sample that happens to be available (e.g., ask co-worker opinions at lunch). Focus groups In-depth dialog with a representative panel of individuals (e.g., iPod users). 2-47 Chapter 2 Sampling Methods With or Without Replacement

If we allow duplicates when sampling, then we are sampling with replacement. Duplicates are unlikely when n is much smaller than large N. If we do not allow duplicates when sampling, then we are sampling without replacement. 2-48 Chapter 2 Sampling Methods Computer Methods Excel - Option A Enter the Excel function =RANDBETWEEN(1,875) into 10 spreadsheet cells. Press F9 to get a new sample. Excel - Option B Enter the function =INT(1+875*RAND()) into 10 spreadsheet cells. Press F9 to get a new sample.

Internet The website www.random.org will give you many kinds of excellent random numbers (integers, decimals, etc). Minitab Use Minitabs Random Data menu with the Integer option. These are pseudo-random generators because even the best algorithms eventually repeat themselves. 2-49 Chapter 2 Sampling Methods Row Column Data Arrays When the data are arranged in a rectangular array, an item can be chosen at random by selecting a row and column.

For example, in the 4 x 3 array, select a random column between 1 and 3 and a random row between 1 and 4. This way, each item has an equal chance of being selected. 2-50 Chapter 2 Sampling Methods Randomizing a List In Excel, use function =RAND() beside each row to create a column of random numbers between 0 and 1. Copy and paste these numbers into the same column using Paste Special > Values in order to paste only values and not the formulas. Sort the spreadsheet on the random number column. Demonstration: CEO compensation (362 CEOs). 2-51

Chapter 2 Sampling Methods Randomizing a List of 362 CEOs Before: CEOs are arranged in descending order of compensation. After: Sorted on RAND() column. The first k CEOs are a random sample. Rank 1 2 3

4 5 6 7 8 9 10 11 Name Terry S Semel Barry Diller William W McGuire Howard Solomon George David Lew Frankfort Edwin M Crawford Ray R Irani Angelo R Mozilo Richard D Fairbank Richard M Kovacevich

Rand() Rank Name 0.0015203 254 Gary L Bloom 0.0060530 173 Edmond J English 0.0074301 350 William V Hickey 0.0087558 202 William Clay Ford Jr 0.0093715 169 David N Farr 0.0140494 305 Carl E Jones Jr 0.0153532 309 James S Tisch 0.0161077 81 James E Rogers 0.0210922 184 Luke R Corbett 0.0222110 242 John B Hess Company Yahoo IAC/InterActiveCorp UnitedHealth Group Forest Labs United Technologies Coach Caremark Rx

Occidental Petroleum Countrywide Financial Capital One Financial Wells Fargo Total Comp ($thou) 230,554 156,168 124,774 92,116 88,712 86,481 77,864 64,136 56,956 56,660 53,083 Company Veritas Software TJX Cos

Sealed Air Ford Motor Emerson Electric Regions Financial Loews Cinergy Kerr-McGee Amerada Hess Total Comp ($thou) 3,492 6,938 1,049 5,603 7,154 2,471 2,380 14,574 6,435 3,912

2-52 Chapter 2 Sampling Methods Systematic Sampling Sample by choosing every kth item from a list, starting from a randomly chosen entry on the list. For example, starting at item 2, we sample every 4 items to obtain a sample of n = 20 items from a list of N = 78 items. Note that N/n = 78/20 4 (periodicity). 2-53 Chapter 2 Sampling Methods Stratified Sampling

Requires prior information about the population. Applicable when the population can be divided into relatively homogeneous subgroups of known size (strata). A simple random sample of the desired size is taken within each stratum. 2-54 Chapter 2 Sampling Methods Cluster Sample Strata consist of geographical regions. One-stage cluster sampling sample consists of all elements in each of k randomly chosen subregions (clusters). Two-stage cluster sampling, first choose k subregions (clusters), then choose a random sample of elements within each cluster. 2-55 Chapter 2

Sampling Methods Cluster Sample Here is an example of 4 elements sampled from each of 3 randomly chosen clusters (twostage cluster sampling). 2-56 Chapter 2 Sampling Methods Judgment Sample A non-probability sampling method that relies on the expertise of the sampler to choose items that are representative of the population. Can be affected by subconscious bias (i.e., non-randomness the choice).

in 2-57 Chapter 2 Sampling Methods Convenience Sample Take advantage of whatever sample is available at that moment. A quick way to sample. Focus Groups A panel of individuals chosen to be representative of a wider population, formed for open-ended discussion and idea gathering. 2-58 Chapter 3 Describing Data Visually

ML 1.5 3.1 Stem-and-Leaf Displays and Dot Plots 3.2 Frequency Distributions and Histograms 3.3 Excel Charts So many 3.4 Line Charts topics, so little time 3.5 Bar Charts 3.6 Pie Charts 3.7 Scatter Plots 3.8 Tables 3.9 Deceptive Graphs 3-59 Chapter 3 Describing Data For univariate data (a set of n observations on one variable)

the statistician would consider the following: 3-60 Chapter 3 Visualizing Data Look and Think Look at the data and visualize how they were collected and measured. Maybe the data values were rounded off? Sorting (Example: Price/Earnings Ratios) Sort the data. Without fancy calculations, you can see the range, and get an idea of typical values. Note that these surely are rounded (price/earnings would not be exactly an integer). 3-61 Chapter 3

Stem-and-Leaf To visualize small integer data sets we can use a stem-and-leaf plot. It is basically a frequency tally, except that we write digits instead of tally marks. For two-digit integer data, the stem is the tens digit of the data, and the leaf is the ones digit. For the 44 P/E ratios, the stem-and-leaf plot is: Use equally spaced stems (even if some stems are empty). The stem-and-leaf can reveal center (24 P/E ratios were in the 1019 stem) as well as variability (the range is from 7 to 59) and shape (right-skewed, mode in the 2nd stem). In this illustration, the leaf digits have been sorted, although this is not necessary. An advantage of the stem-and-leaf is that we can retrieve the raw data. For example, the data values in the fourth stem are 31, 37, 37, 38. Caution Teachers like it, but you rarely see this display in business because it only works for simple integer data (at least, without heroic modifications). 3-62 Chapter 3 Dot Plot Dot plots - are easy to understand.

- reveal center, variability, and shape of the distribution. Steps in Making a Dot Plot 1. Make a scale that covers the data range. 2. Mark the axes and label them. 3. Plot each data value as a dot above the scale at its approximate location. Note: If more than one data value lies at about the same axis location, the dots are stacked vertically. 3-63 Chapter 3 Dot Plot: Example The range is from 7 to 59.

All but a few data values lie between 10 and 25. A typical middle data value would be around 17 or 18. The data are not symmetric due to a few large P/E ratios. Caution: Dot plots work best for integers and small samples. Avoid dot plots if n is large or if you have decimal data. 3-64 Chapter 3 Frequency Distributions Bins and Bin Limits A frequency distribution is a table formed by classifying n data values into k classes (bins). Bin limits define the values to be included in each bin. Widths must all be the same except when we have open-ended bins.

Frequencies are the number of observations within each bin. Often expressed as relative frequencies (frequency divided by the total) or percentages (relative frequency times 100). 3-65 Chapter 3 How Many Bins? What is the ideal number of bins (k) to classify n data values? Herbert Sturges proposed adding bins at a declining rate as n increases: k = 1 + log2(n) or k = 1 + 3.3log10(n) The Excel formula for k is =1+log(n)/log(2). Add one bin when n doubles. This is

only a guideline. Use more or fewer bins to make nice bin limits. 3-66 Chapter 3 Example: n = 44 P/E ratios: Sturges suggests: k = 1 + 3.3log10(n) k = 1 + 3.3log10(44) k = 1 + 3.3(1.64345) k = 6.42 so 6 or 7 bins seems reasonable 3-67 A histogram is a bar chart whose Y-axis shows the frequency within each bin, and whose X-axis ticks show end points of each bin. Consider 3 histograms for the P/E ratio data with different bin widths. In what ways do they differ? In what ways are they similar? .

3-68 Chapter 3 Histograms Chapter 3 Shape Prototype distribution shapes 3-69 A frequency polygon connects midpoints of the histogram intervals, with extra intervals at the beginning and end so that the line will touch the Xaxis. Attractive when you need to compare data sets (since more than one polygon can be plotted on the same scale).

An ogive is a line graph of the cumulative frequencies. It is useful for finding percentiles or in comparing the shape of the sample with a known benchmark such as the normal distribution. Examples for P/E Data Using 6 Bins 3-70 Chapter 3 Frequency Polygons and Ogives Chapter 3 Frequency Polygons and Ogives Examples Using 11 Bins 3-71 Chapter 3

Scatter Plots Scatter plots can convey patterns in (x, y) data pairs that would not be apparent from a table. 3-72 Chapter 3 Scatter Plots Example: Miles per gallon vs weight for 93 cars. 3-73 Chapter 3 Effective Tables Tips for effective tables: 1. Keep the table simple, consistent with its purpose.

2. 3. 4. 5. 6. Put summary tables in the main body of the written report. Put detailed tables in an appendix (or insert a hyperlink). Display the data to be compared in columns rather than rows. For presentation, round off to three or four significant digits. Physical table layout should guide the eye toward the comparison you wish to emphasize. Row and column headings should be simple yet descriptive. Within a column, use a consistent number of decimal digits. 7. 8. 3-74 Chapter 3

Line Charts Log Scales Arithmetic scale distances on the Y-axis are proportional to the magnitude of the variable being displayed. Logarithmic scale (ratio scale) equal distances represent equal ratios. Use a log scale for the vertical axis when data vary over a wide range, say, by more than an order of magnitude. This will reveal more detail for smaller data values. 3-75 Chapter 3

Line Charts Log Scales A log scale is useful for time series data that might be expected to grow at a compound annual percentage rate (e.g., GDP, the national debt, or your future income). It reveals whether the quantity is growing at an increasing percent (concave upward), or constant percent (straight line), or declining percent (concave downward) both growing at a constant percent? 3-76 Chapter 3 Deceptive Graphs Error 1: Dramatic Title and Distracting Pictures Error 2: Elastic Graph Proportions Error 3: Dramatic Title and Distracting Pictures Error 4: 3D and Novelty Graphs

Error 5: Rotated Graphs Error 6: Unclear Definitions or Scales Error 7: Vague Sources Error 8: Complex Graphs Error 9: Gratuitous Effects Error 10: Estimated Data Error 11: Area Trick 3-77 Chapter 3 Deceptive Graphs Error 1: Nonzero Origin A nonzero origin will exaggerate the trend. Deceptive Objective 3-78 Chapter 3

Deceptive Graphs Error 4: 3-D and Novelty Graphs 3D is acceptable (e.g., 3D column) but harder to read data values. Avoid novelty charts (e.g., pyramid). They distort the data. 3-79 Chapter 3 Deceptive Graphs Error 5: 3-D and Rotated Graphs Trends may appear to dwindle into the distance or loom towards you. Harder to read data values. Label each data value if there is room. 3-80

Chapter 3 Deceptive Graphs Error 8: Complex Graphs Keep your main objective in mind. Break graph into smaller parts if necessary. Use clear labels and descriptive titles. 3-81 Assignments ML 1.6 Connect C-1 (covers chapters 2-3)

You get three tries Connect gives you feedback Printable if you wish Deadline is midnight each Monday Project P-1 (data, tasks, questions) Review instructions Look at the data Your task is to write a nice, readable report (not a spreadsheet) Paste Excel graphs and tables into your Word document Length is up to you

0-82 Projects: General Instructions General Instructions For each team project, submit a short (5-10 page) report (using Microsoft Word or equivalent) that answers the questions posed. Strive for effective writing (see textbook Appendix I). Creativity and initiative will be rewarded. Avoid careless spelling and grammar. Paste graphs and computer tables or output into your written report. It may be easier to format tables in Excel and then use Paste Special > Picture to avoid weird formatting and permit sizing within Word. Allocate tasks among team members as you see fit, but all should review and proofread the report (submit only one report). 0-83 Project P-1 Random teams are assigned on Moodle (submit only one report). Data: Download from Moodle or from the instructors web page. Your team is assigned one crime category (but you can change it if you wish). Copy the city names and the chosen crime data column to a new spreadsheet. Delete lines (if any) with missing data. Analysis: (a) Sort the observations (with city names). (b) List the top 10 and bottom 10 data values (with city

names). (c) For the entire data set, calculate the mean and median. What do they tell you about center? Would the mode be helpful for this type of data? Explain. (d) Calculate the standard deviation. (e) Calculate the standardized z-value for each observation. (f) Are there outliers or unusual data values (see p. 137)? Discuss. (g) Use MegaStat (or Minitab or Excel) to make a histogram. Describe its shape. (h) Calculate the quartiles. Make a boxplot and describe it. (i) Make a scatter plot of your kind of crime versus a different type of crime. What does it show? (j) Ambitious students: Sort the database in random order (see bottom of page 36) using Excels function =RAND(). Copy and paste the first few sorted lines into your report to illustrate your sorting method. Comment on anything unusual (or interesting things that you might find on the web). Watch the video walkthrough using Voting, North Carolina Births, and CEO compensation as examples (posted on Moodle) 0-84 Project P-1 your 2010 data will look like this (2005 and 2000 are also available) Crime Rates in U.S. Metropolitan Areas, 2010 (n = 365) Violent Crimes Per 100,000 Metropolitan Statistical Area

All Violent Murder Rape Robbery Abilene, TX M.S.A. 423.0 3.1 48.9 72.7 Akron, OH M.S.A. 304.7 3.7 40.9 105.1 Albany, GA M.S.A. 566.0 8.7 24.9 150.4 Albany-Schenectady-Troy, NY M.S.A. 310.4 1.5

21.0 98.5 Albuquerque, NM M.S.A. 670.4 5.8 44.8 124.3 Alexandria, LA M.S.A. 638.0 5.8 23.1 132.3 Allentown-Bethlehem-Easton, PA-NJ M.S.A. 228.2 3.5 20.3 93.6 Altoona, PA M.S.A. 243.6 0.8 38.0

49.8 Amarillo, TX M.S.A. 513.1 5.7 40.8 98.9 Ames, IA M.S.A. 299.5 1.1 41.7 12.4 Anchorage, AK M.S.A. 812.9 4.2 85.9 148.5 Anderson, IN M.S.A. 205.8 2.3 33.4 70.6

Anderson, SC M.S.A. 586.0 5.3 36.4 75.9 Ann Arbor, MI M.S.A. 338.5 1.4 43.2 69.8 Appleton, WI M.S.A. 155.8 0.0 21.4 13.8 Asheville, NC M.S.A. 229.7 1.9 21.8 59.9 Athens-Clarke County, GA M.S.A.

374.9 4.2 19.6 70.5 Atlanta-Sandy Springs-Marietta, GA M.S.A. 413.8 6.1 20.9 149.7 Atlantic City-Hammonton, NJ M.S.A. 529.8 8.0 18.9 245.5 Augusta-Richmond County, GA-SC M.S.A. 412.9 10.2 37.4 156.6 Austin-Round Rock-San Marcos, TX M.S.A. 327.9

3.4 24.7 84.0 Bakersfield-Delano, CA M.S.A. 593.0 9.0 19.9 148.4 Baltimore-Towson, MD M.S.A. 685.3 10.3 23.6 214.4 Bangor, ME M.S.A. 68.4 2.0 12.6 27.2 Barnstable Town, MA M.S.A. 434.6 0.5

36.1 57.6 Battle Creek, MI M.S.A. 697.6 4.5 75.3 109.6 Bay City, MI M.S.A. 335.2 0.9 78.1 50.8 Beaumont-Port Arthur, TX M.S.A. 498.3 5.6 37.7 157.9 Bellingham, WA M.S.A. 267.0 2.5 44.7

50.6 Bend, OR M.S.A.2 304.9 4.3 29.0 30.9 Assault 298.3 155.0 382.1 189.4 495.6 476.7 110.9 155.0 367.8 244.4 574.4 99.5 468.4

224.0 120.5 146.1 280.5 237.1 257.5 208.7 215.8 415.7 437.0 26.6 340.3 508.3 205.2 297.0 169.1 240.7 Property Crimes Per 100,000 All Property Burglary

Larceny Car Theft 3617.3 1009.0 2459.8 148.5 3185.6 947.7 2074.5 163.3 4512.6 1417.8 2803.4 291.4 2693.6 512.1 2076.2 105.4 3896.1 920.6 2586.2

389.4 4592.9 1203.3 3176.3 213.3 2298.0 432.2 1758.1 107.7 1811.7 425.4 1318.2 68.0 4812.7 1137.2 3390.5 285.0 2528.1 478.6 1966.1 83.3

3506.3 416.1 2813.4 276.8 3353.8 848.1 2294.6 211.1 4707.8 1297.6 3041.7 368.4 2713.7 659.7 1879.5 174.4 2136.7 378.5 1708.2 50.0 2454.9

749.6 1534.9 170.3 3843.7 1018.0 2588.1 237.5 3462.6 957.0 2135.7 370.0 3550.3 741.5 2685.7 123.1 4815.3 1355.1 3037.7 422.5 3792.0 754.3

2866.9 170.8 3713.1 1148.0 1931.6 633.6 3090.7 649.5 2135.5 305.7 3098.2 573.3 2429.3 95.7 2972.8 1116.6 1764.7 91.5 3703.5 1145.6 2411.1

146.8 2472.4 610.1 1776.6 85.7 3865.3 1156.9 2488.4 220.1 3197.8 694.2 2372.7 130.8 2973.7 497.5 2360.2 116.0 Definitions Violent crime Murder and nonnegligent manslaughter

Forcible rape Robbery Aggravated assault Property crime Burglary Larceny-theft Motor vehicle theft 0-85 Example: CEO Compensation sorting is a good first step 0-86 Example: CEO Compensation Highlight all data (including the headings) and use Custom Sort 0-87

Example: CEO Compensation now you can clearly see the high and low data values (and comment on any weird data values) 0-88 Example: CEO Compensation use MegaStats Descriptive Statistics to get your basic stats along with a nice boxplot 0-89 Example: CEO Compensation severely skewed use MegaStats Frequency Distributions to get a frequency table, histogram, etc annotated by user normal if logs used?

0-90 Example: CEO Compensation standardize the sorted list by subtracting the mean from each x value and then dividing by the standard deviation (or use =STANDARDIZE function) 0-91 Example: CEO Compensation after standardizing the sorted list, unusual z values can be seen 0-92 Example: CEO Compensation to randomize the list, paste values of =RAND() beside data and custom sort on =RAND() 0-93

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