Business Statistics Lecture Slides PDF

Business Statistics Eleventh Edition Ken Black Chapter 1 Introduction to Statistics and Business Analytics This deck contains equations...

Business Statistics Eleventh Edition Ken Black Chapter 1 Introduction to Statistics and Business Analytics This deck contains equations authored in Math Type. For the full experience, please download the Math Type software plug-in. Copyright ©2023 John Wiley & Sons, Inc. Learning Objectives (1 of 2) 1. List quantitative and graphical examples of statistics within a business context. 2. Define important statistical terms, including population, sample, and parameter, as they relate to descriptive and inferential statistics. 3. Explain the difference between variables, measurement, and data. 4. Compare the four different levels of data: nominal, ordinal, interval, and ratio. Copyright ©2023 John Wiley & Sons, Inc. 2 Learning Objectives (2 of 2) 5. Define important business analytics terms including big data, business analytics, data mining, and data visualization. 6. List the four dimensions of big data and explain the differences between them. 7. Compare and contrast the three categories of business analytics. Copyright ©2023 John Wiley & Sons, Inc. 3 1.1 Key Elements of Statistics Statistics: a science dealing with the collection, analysis, interpretation, and presentation of numerical data Copyright ©2023 John Wiley & Sons, Inc. 4 1.1 Population, Census, Sample Population versus Sample Population: ○ A collection of all persons, objects, or items under study ○ Can be broadly or narrowly defined Census: gathering data from the whole population Sample: gathering data on a subset of the population ○ Should be representative of the whole population ○ Use information about the sample to infer about the population Copyright ©2023 John Wiley & Sons, Inc. 5 1.1 Census Example Consider this population of cars We can collect the color and miles per gallon from all cars; this would be a census Copyright ©2023 John Wiley & Sons, Inc. 6 1.1 Random Sample Example Instead of a census, we could take a random sample of 4 cars Identifier Color MPG RD1 Red 12 RD2 Red 10 RD3 Red 13 RD4 Red 10 RD5 Red 13 BL1 Blue 27 BL2 Blue 24 GR1 Green 35 GR2 Green 35 GY1 Gray 15 GY2 Gray 18 GY3 Gray 17 Copyright ©2023 John Wiley & Sons, Inc. 7 1.1 Random Sample from Population We can select a random sample of 4 cars from the population of 12 cars, and record the color and MPG for those cars Identifier Color MPG RD2 Red 10 RD5 Red 13 GR1 Green 35 GY2 Gray 18 Copyright ©2023 John Wiley & Sons, Inc. 8 1.1 Two Branches of Statistics Descriptive ○ Uses data gathered on a group to describe or reach conclusions about that same group ○ Produces graphical or numerical summaries of data Inferential ○ Gathers data from a sample and uses the statistics generated to reach conclusions about the population from which the sample was taken ○ Sometimes called inductive statistics Copyright ©2023 John Wiley & Sons, Inc. 9 1.1 Statistical Measures Parameter: descriptive measure of the population ○ Usually represented by Greek letters µ denotes population mean denotes population variance σ denotes population standard deviation Statistic: descriptive measure of a sample ○ Usually represented by Roman letters denotes sample mean denotes sample variance s denotes sample standard deviation Copyright ©2023 John Wiley & Sons, Inc. 10 1.1 Inferential Process Copyright ©2023 John Wiley & Sons, Inc. 11 1.1 Variables, Measurements A variable is a characteristic of any entity being studied that is capable of taking on different values ○ E.g., stock price, age of worker, historical cost, market share A measurement is the standard process used to assign numbers to particular attributes of a variable ○ Data are recorded measurements Copyright ©2023 John Wiley & Sons, Inc. 12 1.2 Level of Data Measurement Business analysts must know the level of data measurement of the numbers being analyzed because all data cannot be analyzed the same way Copyright ©2023 John Wiley & Sons, Inc. 13 1.2 Nominal Level of Data Measurement Nominal: used only to classify or categorize ○ No value statement is implied ○ Lowest level of measurement ○ Examples: Profession (doctor, lawyer…) Sex (male, female) Eye color (blue, brown, green…) Location (Zip code) Copyright ©2023 John Wiley & Sons, Inc. 14 1.2 Ordinal Level of Data Measurement Ordinal: ranking or ordering ○ Distances between ranks are not always equal ○ Nominal and ordinal data are nonmetric data or qualitative data because their measurements are imprecise ○ Examples: Ranking mutual funds by risk 50 most-admired companies Copyright ©2023 John Wiley & Sons, Inc. 15 1.2 Interval Level of Data Measurement Interval: numerical data in which the distances between consecutive numbers have meaning ○ Interval data have equal intervals ○ Example: Fahrenheit temperature scale The zero point is a matter of convenience or convention A temperature of 0° does not mean that there is no temperature The amounts of heat between consecutive readings are the same Copyright ©2023 John Wiley & Sons, Inc. 16 1.2 Ratio Level of Data Measurement Ratio: numerical data in which the distances between consecutive numbers have meaning and the zero value represents the absence of the characteristic being studied ○ Highest level of data measurement ○ Interval and ratio data are called metric or quantitative data because their measurements are precise ○ Examples: Volume Weight Kelvin temperature Copyright ©2023 John Wiley & Sons, Inc. 17 1.2 Usage Potential of Levels of Measurement Type of data determines the type of statistical analysis that can be performed ○ Nominal data is the most limited ○ Ratio data is the broadest Parametric statistics require interval or ratio data Nonparametric statistics can be used with any data, but nominal and ordinal data require nonparametric methods Copyright ©2023 John Wiley & Sons, Inc. 18 1.2 Summary of Metric Data, Nonmetric Data Copyright ©2023 John Wiley & Sons, Inc. 19 1.3 Big Data Big Data: a collection of large and complex datasets from different sources that are difficult to process using traditional data management and processing applications Variety: different forms based on sources of data Velocity: the speed with which the data are available and can be processed Veracity: data quality, correctness, and accuracy Volume: ever-increasing size of data and databases Value: sometimes considered a fifth characteristic Copyright ©2023 John Wiley & Sons, Inc. 20  1.3 Business Analytics Business Analytics: the application of processes and techniques that transform raw data into meaningful information to improve decision-making Copyright ©2023 John Wiley & Sons, Inc. 21 1.3 Descriptive Analytics Descriptive Analytics: takes traditional data and describes what has or is happening in a business Simplest and most commonly used category Use to condense big data into smaller, more useful data Also called reporting analytics Used to discover hidden relationships and patterns Data visualization is key Topics include descriptive statistics, frequency distributions, statistical inference, correlation, clustering techniques, data mining, and data visualization Copyright ©2023 John Wiley & Sons, Inc. 22 1.3 Predictive Analytics Predictive Analytics: finds relationships in the data that are not readily apparent with descriptive analytics ○ Patterns or relationships are extrapolated forward in time and the past is used to make predictions about the future ○ Topics include regression, time-series, forecasting, simulation, data mining, statistical modeling, machine learning techniques, decision tree models, and neural networks Copyright ©2023 John Wiley & Sons, Inc. 23 1.3 Prescriptive Analytics Prescriptive Analytics: examines current trends and likely forecasts to make better decisions ○ Takes uncertainty into account, recommends ways to mitigate risks, and tries to foresee the effects of future decisions ○ Uses a set of mathematical techniques that determine optimal decisions given a complex set of objectives, requirements, and constraints ○ Topics include management science or operations research aimed at optimizing performance of a system such as mathematical programming, simulation, and network analysis Copyright ©2023 John Wiley & Sons, Inc. 24 1.3 Data Mining Data Mining: collecting, exploring, and analyzing large volumes of data to uncover hidden patterns to enhance decision- making Used by companies to turn raw data into useful information Copyright ©2023 John Wiley & Sons, Inc. 25 1.3 Data Visualization Data Visualization: the study of the visual representation of data and is employed to convey data or information by imparting it as visual objects displayed in graphics TABLE 1.1: Top Five Government Contractors to the U.S. Treasury Global Vendor Name Dollars Obligated Coins 'N Things Inc. $529,070,983.45 Spectrum Group International Inc. $415,013,005.82 Sunshine Minting Inc. $406,300,921.45 Deloitte LLP $205,655,400.82 Crane & Co. Inc. $173,888,697.30 Copyright ©2023 John Wiley & Sons, Inc. 26 1.3 Visualization: Bubble Chart Copyright ©2023 John Wiley & Sons, Inc. 27 1.3 Data Visualization: Bar Chart Copyright ©2023 John Wiley & Sons, Inc. 28 Copyright Copyright © 2023 John Wiley & Sons, Inc. All rights reserved. Reproduction or translation of this work beyond that permitted in Section 117 of the 1976 United States Act without the express written permission of the copyright owner is unlawful. Request for further information should be addressed to the Permissions Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages, caused by the use of these programs or from the use of the information contained herein. Copyright ©2023 John Wiley & Sons, Inc. 29