Business Analytics: Transforming Data for Better Decisions

1. BAN 280 Chapter 1 Introduction to Statistics

2. BAN stands for Business ANalytics. • Scientific process of transforming data into insight for making better decisions. (INFORMS) • Used for data-driven or fact-based decision making Business Analytics Defined

3. Three developments spurred recent explosive growth in the use of analytical methods in business applications: • Technological advancesin computing algorithms • Data generation from personal electronic devices, web pages, POS, wireless devices produce incredible amounts of data for businesses. • Technological advances in speed and storage capacity of computers Introduction

4. Descriptive analytics:Encompasses the set of techniques that describes what has happened in the past. • Descriptive statistics • Data Summary • Data Visualization (data dashboards) • Basic what-if spreadsheet models. A Categorization of Analytical Methods and Models

5. Predictive analytics:Consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another based on probabilities. • Regression • Time Series Analysis • Data Mining • Decision Trees • Artificial Neural Networks • Simulation A Categorization of Analytical Methods and Models

6. Prescriptive Analytics:Indicates a best course of action to take based on known parameters and to a lesser extent probabilities • Optimization Models • Simulation Models • Decision Models A Categorization of Analytical Methods and Models

7. The Spectrum of Business Analytics

8. What is a definition for statistics? The field of Statistics is concerned with the collection, presentation, and analysis of data in order to assist a manager in the decisions making process. • What is the “story” of the data? Why have a class in Statistics?

9. Two Main Branches of Statistics Inferential Infer or make conclusions from an analysis of the data Descriptive • Describe the data • Central Tendency • Dispersion • Distribution

10. Statistical Terminology • Population – the collection of ALL entities possessing some characteristic we are interested in. • Sample – some subset of a Population • Population Parameter – a summary measure of some characteristic we are interested in for all entities in a population. • sample statistic – a summary measure computed from a sample and used to estimate a Parameter from the Population where the sample was derived from.

11. TYPES OF Variables QUALITATIVE • Qualities • Characteristics that are not measurable with an interval or ratio number scale QUANTITATIVE • Data which is numerical in nature • Can use mathematical functions like add, subtract, etc.

12. QUALITATIVE NOMINAL • Data classified into categories with no order implied • What color are your eyes? • What is your Occupation? • Accountant • Economist • Manager • Teacher • Unemployed (Student) ORDINAL • Categorical data with ordering implied • How was the movie last night? • Excellent • Very Good • Good • Fair • Poor • Rate your Professor • 1 • 2 • 3 • 4 • 5

13. QUANTITATIVE (Continuous) • Interval Scale • Numerical but no Zero (ie: tempature, change in employment, etc.) • The distant between consecutive values of the interval scale DOES have meaning • You can perform math operations on interval variables • Ratio Scale • Numerical with a meaningful Zero • Weight • Age • Height • Time

14. Customer Surveys Historical Company Records Competitor Data Manufacturing and Sales Data (internal) MIS issues? IT issues? OPS issues? Sources of Data

15. Time Series Data is data collected through time. • Stock prices are an example of time series data. Tomorrow’s starting price for a stock depends on the ending price of that stock today. Stock prices “move” over time so it is important to factor in this effect. • Cross Sectional Data does not have a “time” component • Data collected on a variable at a single point in time. For example you might be interested in doing a study of comparative housing prices for the 8 major cities in June 2000. Types of Data

16. First step in any analysis is to examine the data • Arrays • Listing the data in ascending or descending order. • Useful in identifying common or outlying values • Tables • Summarizing the data into categories • Useful for visualizing important characteristics of the data • Frequency Distributions • Graphical Representations • Pie and Bar Charts • Histograms Examining the Data

17. Central Tendency • Mean • Median • Mode • Dispersion • Range • Mean Absolute Deviation • Standard Deviation Descriptive Measures

18. Why sample? • Cost and time advantages • Population size - Census too cumbersome • Destructive sampling Selecting a sample

19. Definition • Each member of the population has an equally likely chance of being selected. • sampling with replacement • Basis of most statistical inference simple random sampling (srs)

20. Sampling error • Error caused because no sample is exactly representative of population • Chance differences that occur when a sample is selected • Non sampling error • Error caused by human. Errors in Collecting Data

21. Population Parameters are computed from a census of the entire Population and are used to describe some characteristic about the Population you are interested in (X). Population µx sx Parameters

22. Population Parameters are computed from a census of the entire Population and are used to describe some characteristic about the Population you are interested in (X). Population µx sx Parameters A sample is a subset of a larger Population sample sx sample statistics are computed from sample data and used to estimate Population Parameters statistics