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Identify the key components that must be in place for an organization to get real value from its BI and analytics efforts.

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CHAPTER

9 Business Intelligence and Analytics

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Know?Did Yo u

• MetLife is implementing analytical software to identify medical provider, attorney, and repair shop fraud to aid its special investigations unit (SIU).

• Nearly 20 percent of Medicare patients were readmitted to the hospital within 30 days of their initial discharge, running up an additional $17 billion in healthcare costs. Hospitals are now using BI analytics to identify patients are high risk of readmission—especially now that Medicare has begun reducing payments to hospitals with high readmission rates.

• IBM Watson Analytics services, a cloud-based business analytics tool that offers a variety of tools for uncovering trends hidden in large sets of data, uses baseball statistics on every player in Major League Baseball from AriBall to build predictions of player performance. You can use this service to gain an edge over your fantasy baseball league competitors.

Principles Learning Objectives

• Business intelligence (BI) and analytics are used to support improved decision making.

• Define the terms business intelligence (BI) and analytics.

• Provide several real-world examples of BI and analytics being used to improve decision making.

• Identify the key components that must be in place for an organization to get real value from its BI and analytics efforts.

• There are many BI and analytics techniques and tools that can be used in a wide range of problem- solving situations.

• Identify several BI techniques and discuss how they are used.

• Identify several BI tools.

• Define the term self-service analytics and discuss its pros and cons.

Copyright 2018 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

Why Learn about Business Intelligence (BI) and Analytics? We are living in the age of big data, with new data flooding us from all directions at the incomprehensible speed of nearly a zettabyte (1 trillion gigabytes or a 1 followed by 21 zeros) per year. What is most exciting about this data is not its amount, but rather the fact that we are gaining the tools and understanding to do something truly meaningful with it. Organizations are learning to analyze large amounts of data not only to measure past and current performance but also to make predictions about the future. These forecasts will drive anticipatory actions to improve business strategies, strengthen business operations, and enrich decision making—enabling the organization to become more competitive.

A wide range of business users can derive benefits from access to data, but most of them lack deep information systems or data science skills. Business users need easier and faster ways to discover relevant patterns and insights into data to better support their decision making and to make their companies more agile. Companies that have access to the same kind of data as their competitors but can analyze it sooner to take action faster will outpace their peers. Providing BI tools and making business analytics more understandable and accessible to these users should be a key strategy of organizations.

Members of financial services organizations use BI and analytics to better understand their customers to enhance service, create new and more appealing products, and better manage risk. Marketing managers analyze data related to the Web-surfing habits, past purchases, and even social media activity of existing and potential customers to create highly effective marketing programs that generate consumer interest and increased sales. Health care professionals who are able to improve the patient experience will reap the benefits of maximized reimbursements, lower costs, and higher market share, and they will ultimately deliver higher quality care for patients. Physicians use business analytics to analyze data in an attempt to identify factors that lead to readmission of hospital patients. Human resources managers use analytics to evaluate job candidates and choose those most likely to be successful. They also analyze the impact of raises and changes in employee-benefit packages on employee retention and long-term costs.

Regardless of your field of study in school and your future career, using BI and analytics, will likely be a significant component of your job. As you read this chapter, pay attention to how different organizations use business analytics. This chapter starts by introducing basic concepts related to BI and analytics. Later in the chapter, several BI and analytics tools and strategies are discussed.

As you read this chapter, consider the following:

• What is business intelligence (BI) and analytics, and how can they be used to improve the operations and results of an organization?

• What are some business intelligence and analytics techniques and tools, and how can they be used?

This chapter begins with a definition of business intelligence (BI) and busi- ness analytics and the components necessary for a successful BI and analytics program. The chapter goes on to describe and provide examples of the use of several BI techniques and tools. It ends with a discussion of some of the issues associated with BI and analytics.

What Are Analytics and Business Intelligence?

Business analytics is the extensive use of data and quantitative analysis to sup- port fact-based decision making within organizations. Business analytics can be used to gain a better understanding of current business performance, reveal new business patterns and relationships, explain why certain results occurred, optimize current operations, and forecast future business results.

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Business intelligence (BI) includes a wide range of applications, prac- tices, and technologies for the extraction, transformation, integration, visuali- zation, analysis, interpretation, and presentation of data to support improved decision making. The data used in BI is often pulled from multiple sources and may come from sources internal or external to the organization. Many organizations use this data to build large collections of data called data ware- houses, data marts, and data lakes, for use in BI applications. Users, including employees, customers, and authorized suppliers and business partners, may access the data and BI applications via the Web or through organizational intranets and extranets—often using mobile devices, such as smartphones and tablets. The goal of business intelligence is to get the most value out of information and present the results of analysis in an easy to understand man- ner that the layman can understand.

Often the data used in BI and analytics must be gathered from a variety of sources. Helse Vest, a regional health authority in Norway, has 26,500 employees who serve 1 million people in 50 healthcare facilities, including 10 hospitals. Helse Vest implemented a BI system to meet the requirements of a government-sponsored national patient safety program. The system collects, visualizes, and shares medical data used to identify quality measures and reporting requirements across all care teams and regional hospi- tals. A major challenge for the project was the need for each of the 10 hospitals to combine data from all the facilities within its region for analysis by the pro- gram’s board and hospital managers. Prior to implementing the new system, it took up to 14 days for employees to produce some reports, making it difficult for hospital staff to assess and act on performance data because it was not cur- rent. With the new system, Helse Vest analysts can easily combine data from different sources and create analytical reports in less than one day. Real-time data enables Helse Vest to act on information much more quickly, while the metrics are still valid for the staff, and a quick response to performance data is more likely to lead to significant improvements in patient safety measures.1

Benefits Achieved from BI and Analytics BI and analytics are used to achieve a number of benefits as illustrated by the following examples:

● Detect fraud. MetLife implemented analytical software to help its special investigations unit (SIU) identify medical provider, attorney, and repair shop fraud. Although an accident claim may not have enough data to be flagged as suspicious when it is first filed, as more claim data is added, a claim is continually rescored by the software. After the first six months of using the software, the number of claims under investigation by the SIU increased 16 percent.2

● Improve forecasting. Kroger serves customers in 2,422 supermarkets and 1,950 in-store pharmacies. The company found that by better predicting pharmacy customer demand, it could reduce the number of prescriptions that it was unable to fill because a drug is out of stock. To do so, Kroger developed a sophisticated inventory management system that could provide employees with a visualization of inventory levels, adapt to user feedback, and support “what-if” analysis. Out-of-stock pre- scriptions have been reduced by 1.5 million per year, with a resulting increase in sales of $80 million per year. In addition, by carrying the right drugs in the right quantities, Kroger was able to reduce its overall inven- tory costs by $120 million per year.3

● Increase Sales. DaimlerChrysler and many other auto manufacturers set their suggested retail and wholesale prices for the year, then adjust pricing through seasonal incentives based on the impact of supply and demand. DaimlerChrysler implemented a price-elasticity model to

business intelligence (BI): A wide range of applications, practices, and technologies for the extraction, transformation, integration, visualiza- tion, analysis, interpretation, and presentation of data to support improved decision making.

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optimize the company’s pricing decisions. The system enables managers to evaluate many potential incentives for each combination of vehicle model (e.g., Jeep Grand Cherokee), acquisition method (cash, finance, or lease), and incentive program (cash back, promotional APR, and a combination of cash back and promotional APR). The firm estimates that use of the system has generated additional annual sales of $500 million.4

● Optimize operations. Chevron is one of the world’s leading integrated energy companies. Its refineries work with crude oil that is used to make a wide range of oil products, including gasoline, jet fuel, diesel fuel, lubricants, and specialty products such as additives. With market prices of crude oil and its various products constantly changing, determining which products to refine at a given time is quite complex. Chevron uses an analytical system called Petro to aid analysts in advising the refineries and oil traders on the mix of products to produce, buy, and sell in order to maximize profit.5

● Reduce costs. Coca-Cola Enterprises is the world’s largest bottler and distributor of Coca Cola products. Its delivery fleet of 54,000 trucks is second in size to only to the U.S. Postal Service. Using analytics software, the firm implemented a vehicle-routing optimization system that resulted in savings of $45 million a year from reduced gas consumption and reduction in the number of drivers required.6

The Role of a Data Scientist Data scientists are individuals who combine strong business acumen, a deep understanding of analytics, and a healthy appreciation of the limitations of their data, tools, and techniques to deliver real improvements in decision mak- ing. Data scientists do not simply collect and report on data; they view a situa- tion from many angles, determine what data and tools can help further an understanding of the situation, and then apply the appropriate data and tools. They often work in a team setting with business managers and specialists from the business area being studied, market research and financial analysts, data stewards, information system resources, and experts highly knowledgeable about the company’s competitors, markets, products, and services. The goal of the data scientist is to uncover valuable insights that will influence organiza- tional decisions and help the organization to achieve competitive advantage.

Data scientists are highly inquisitive, continually asking questions, performing “what-if” analyses, and challenging assumptions and existing processes. Successful data scientists have an ability to communicate their find- ings to organizational leaders so convincingly that they are able to strongly influence how an organization approaches a business opportunity.

The educational requirements for being a data scientist are quite rigorous—requiring a mastery of statistics, math, and computer programming. Most data scientist positions require an advanced degree, such as a master’s degree or a doctorate. Some organizations accept data scientists with under- graduate degrees in an analytical concentration, such as computer science, math and statistics, management information systems, economics, and engi- neering. Colorado Technical University, Syracuse University, and Villanova University are among the many schools that offer online master degree pro- grams related to BI and analytics.

Many schools also offer career-focused courses, degrees, and certificates in analytical-related disciplines such as database management, predictive ana- lytics, BI, big data analysis, and data mining. Such courses provide a great way for current business and information systems professionals to learn data scientist skills. Most data scientists have computer programming skills and are familiar with languages and tools used to process big data, such as Hadoop, Hive, SQL, Python, R, and Java.

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Critical Thinking Exercise

The job outlook for data scientists is extremely bright. The McKinsey Global Institute (the business and economics research arm of the management consulting firm McKinsey & Co.) predicts that by 2018 the United States may face a shortage of 140,000 to 190,000 data scientists.7 The recruitment agency Glassdoor pegs the average salary for a data scientist at $118,709, and highly talented, educated, and experienced data scientists can earn well over $250,000 per year.

Components Required for Effective BI and Analytics A number of components must be in place for an organization to get real value from its BI and analytics efforts. First and foremost is the existence of a solid data management program, including data governance. Recall that data management is an integrated set of functions that defines the processes by which data is obtained, certified fit for use, stored, secured, and processed in such a way as to ensure that the accessibility, reliability, and timeliness of the data meet the needs of the data users within an organization. Data gover- nance is the core component of data management; it defines the roles, respon- sibilities, and processes for ensuring that data can be trusted and used by the entire organization, with people identified and in place who are responsible for fixing and preventing issues with data.

Another key component that an organization needs is creative data scientists—people who understand the business as well as the business ana- lytics technology, while also recognizing the limitations of their data, tools, and techniques. A data scientist puts all of this together to deliver real improvements in decision making with an organization.

Finally, to ensure the success of a BI and analytics program, the manage- ment team within an organization must have a strong commitment to data- driven decision making. Organizations that can put the necessary components in place can act quickly to make superior decisions in uncertain and changing environments to gain a strong competitive advantage.

Argosy Gaming Argosy Gaming Company is the owner and operator of six riverboat gambling casinos and hotels in the United States. Argosy has developed a centralized enter- prise data warehouse to capture the data generated at each property. As part of this effort, Argosy selected an extract-transform-load (ETL) tool to gather and inte- grate the data from six different operational databases to create its data ware- house. The plan is to use the data to help Argosy management make quicker, well-informed decisions based on patrons’ behaviors, purchases, and preferences. Argosy hopes to pack more entertainment value into each patron’s visit by better understanding their gambling preferences and favorite services. The data will also be used to develop targeted direct mail campaigns, customize offers for specific customer segments, and adapt programs for individual casinos.8

Review Questions 1. What are the key components that Argosy must put into place to create an

environment for a successful BI and analytics program? 2. What complications can arise from gathering data from six different opera-

tional databases covering six riverboat gambling casinos and hotels?

Critical Thinking Questions 1. The Argosy BI and analytics program is aimed at boosting revenue not at

reducing costs. Why do you think this is so? 2. What specific actions must Argosy take to have a successful program that will

boost revenue and offset some of the increases in costs?

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Business Intelligence and Analytics Tools

This section introduces and provides examples of many BI and analytics tools, including spreadsheets, reporting and querying tools, data visualization tools, online analytical processing (OLAP), drill-down analysis, linear regression, data mining, and dashboards. It will also cover the strategy of self-service ana- lytics, presenting its pros and cons.

Spreadsheets Business managers often import data into a spreadsheet program, such as Excel, which then can be used to perform operations on the data based on formulas created by the end user. Spreadsheets are also used to create reports and graphs based on that data. End users can employ tools such as the Excel Scenario Manager to perform “what-if” analysis to evaluate various alterna- tives. The Excel Solver Add-in can be used to find the optimal solution to a problem with multiple constraints (e.g., determine a production plan that will maximize profit subject to certain limitations on raw materials).

North Tees and Hartlepool National Health Services Trust provides health- care services and screenings to a population of 400,000 people in the United Kingdom. Professor Philip Dean, head of the Department of Pharmacy and Quality Control Laboratory Services, wanted a way to better understand the clinical use of drugs, the efficacy of treatment, and the associated costs. Dean worked with resources from Ascribe, a BI software and consulting firm, to pilot the use of Microsoft Power BI for Office 365, part of the Microsoft Office 365 cloud-based business productivity suite that works through familiar Excel spreadsheet software (see Figure 9.1). Ascribe developers took an extract of North Tees’s data and imported it into a Power BI model. They then incorpo- rated other data sets of interest to Dean and his colleagues, such as publicly available data on the activity of general practitioners, weather data, and

Sharepoint Distribute

and interact

Power Pivot Link and calculate

Data sources

Excel Analyze, design

and present

Power Map Visualize

Power View Visualize

Greatest sales opportunties

Power Query Extract and transform

T ri ff /S h u tt e rs to ck .c o m

FIGURE 9.1 Components of Microsoft Power BI for Office 365 Microsoft Power BI has been used to better understand the clinical use of drugs, the efficacy of treatment, and the associ- ated costs.

Source: Access Analytics, Power BI for Business, Power Analytics, http://www.accessanalytic.com.au/Power-BI.html.

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treatment data. With all of this new data integrated in the Power BI model, Dean was able to create graphs of his findings, visualize data on regional maps, and even zoom in and around the data to gain various levels of insight. According to Dean, the ability to link disparate data sets for an integrated analysis was “one of the ‘wow’ things” that impressed him most in his use of BI tools. Incorporating additional, external data sets into his analyses comple- mented and helped explain trends, as well as provided useful benchmarks. Use of the weather data helped identify the impact of inclement weather on the frequency of respiratory disease. The treatment data helped Dean and his team to understand which drugs were being prescribed and how prescription patterns varied by locality.9

Reporting and Querying Tools Most organizations have invested in some reporting tools to help their employ- ees get the data they need to solve a problem or identify an opportunity. Reporting and querying tools can present that data in an easy-to-understand fashion—via formatted data, graphs, and charts. Many of the reporting and que- rying tools enable end users to make their own data requests and format the results without the need for additional help from the IT organization.

FFF Enterprises is a supplier of critical-care biopharmaceuticals, plasma products, and vaccines. Its 46,000 customers include over 80 percent of U.S. hospitals.10 The company employs the QlikView query and reporting tool to provide employees with real-time access to data that affects its business and the timely delivery of safe, effective products and services. For example, the company is the largest flu vaccine distributor in the United States, and accu- rately tracking its vaccine shipments is critical to avoiding shortages. As part of those efforts, FFF Enterprises uses QlikView to track and monitor the volume and value of all product transactions, such as the receipt, internal movement, and distribution of products.11

Data Visualization Tools Data visualization is the presentation of data in a pictorial or graphical format. The human brain works such that most people are better able to see significant trends, patterns, and relationships in data that is presented in a graphical format rather than in tabular reports and spreadsheets. As a result, decision makers welcome data visualization software that presents analytical results visually. In addition, representing data in visual form is a recognized technique to bring immediate impact to dull and boring numbers. A wide array of tools and techniques are available for creating visual representations that can immediately reveal otherwise difficult-to-perceive patterns or relation- ships in the underlying data.

Many companies now troll Facebook, Google Plus+, LinkedIn, Pinterest, Tumblr, Twitter, and other social media feeds to monitor any mention of their company or product. Data visualization tools can take that raw data and immediately provide a rich visual that reveals precisely who is talking about the product and what they are saying. Techniques as simple and intuitive as a word cloud can provide a surprisingly effective visual summary of conversa- tions, reviews, and user feedback about a new product. A word cloud is a visual depiction of a set of words that have been grouped together because of the frequency of their occurrence. Word clouds are generated from analy- ses of text documents or a Web page. Using the text from these sources, a simple count is carried out on the number of times a word or phrase appears. Words or phrases that have been mentioned more often than other words or phrases are shown in a larger font size and/or a darker color, as shown in Figure 9.2. ABCya, Image Chef, TagCloud, ToCloud, and ToCloud, Tagul, and Wordle are examples of word cloud generator software.

Data visualization: The presenta- tion of data in a pictorial or graphical format.

word cloud: A visual depiction of a set of words that have been grouped together because of the frequency of their occurrence.

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A conversion funnel is a graphical representation that summarizes the steps a consumer takes in making the decision to buy your product and become a customer. It provides a visual representation of the conversion data between each step and enables decision makers to see what steps are causing customers confusion or trouble. Figure 9.3 shows a conversion funnel for an online sales organization. It shows where visitors to a Web site are dropping off the successful sales path.

FIGURE 9.2 Word cloud This Word cloud shows the topics covered in this chapter.

FIGURE 9.3 The conversion funnel The conversion funnel shows the key steps in converting a consumer to a buyer.

Web site visits 100%

Visit Visit Visit Visit

Visit

Product views 73%

Cart additions 23%

Checkouts 11%

Purchases 3%

conversion funnel: A graphical representation that summarizes the steps a consumer takes in making the decision to buy your product and become a customer.

m in ds ca nn er /S hu tt er st oc k. co m

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Technologia is a business training firm that has trained over 70,000 clients in dozens of technical topics, such as project management, SQL, and Microsoft Windows. One Technologia course can cost $1,000 or more, so most potential customers do careful detailed research and make multiple visits to the com- pany’s Web site before enrolling in a course. Technologia used Multi-Channel Funnels from Google Analytics to determine what factors had the most impact in influencing students to enroll. For the first time, Technologia learned that nearly 18 percent of its sales paths included paid advertising—much higher than previously thought. As a result, it raised its online ad budget by nearly 100 percent, and online conversions shot up 120 percent.12

Dozens of data visualization software products are available for creating various charts, graphs, infographics, and data maps (see Figure 9.4). Some of the more commonly products include Google Charts, iCharts, Infogram, Modest Maps, SAS Visual Statistics, and Tableau. These tools make it easy to visually explore data on the fly, spot patterns, and quickly gain insights.

Online Analytical Processing Online analytical processing (OLAP) is a method to analyze multidimen- sional data from many different perspectives. It enables users to identify issues and opportunities as well as perform trend analysis. Databases built to support OLAP processing consist of data cubes that contain numeric facts called measures, which are categorized by dimensions, such as time and geog- raphy. A simple example would be a data cube that contains the unit sales of a specific product as a measure. This value would be displayed along the metric dimension axis shown in Figure 9.5. The time dimension might be a specific day (e.g., September 30, 2018), whereas the geography dimension might define a specific store (e.g., Krogers in the Cincinnati, Ohio community of Hyde Park).

The key to the quick responsiveness of OLAP processing is the preaggre- gation of detailed data into useful data summaries in anticipation of questions that might be raised. For example, data cubes can be built to summarize unit sales of a specific item on a specific day for a specific store. In addition, the detailed store-level data may be summarized to create data cubes that show

FIGURE 9.4 Data visualization This scatter diagram shows the relationship between MSRP and horsepower. Source: “Data Visualization,” SAS, http:// www.sas.com/en_us/insights/big-data/data -visualization.html#m=lightbox5, accessed April 19, 2016.

online analytical processing (OLAP): A method to analyze multidi- mensional data from many different perspectives, enabling users to identify issues and opportunities as well as perform trend analysis.

data cube: A collection of data that contains numeric facts called mea- sures, which are categorized by dimensions, such as time and geography.

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unit sales for a specific item, on a specific day for all stores within each major market (e.g., Boston, New York, Phoenix), for all stores within the United States, or for all stores within North America. In a similar fashion, data cubes can be built in anticipation of queries seeking information on unit sales on a given day, week, month, or fiscal quarter.

It is important to note that if the data within a data cube has been sum- marized at a given level, for example, unit sales by day by store, it is not pos- sible to use that data cube to answer questions at a more detailed level, such as what were the unit sales of this item by hour on a given day.

Data cubes need not be restricted to just three dimensions. Indeed, most OLAP systems can build data cubes with many more dimensions. In the busi- ness world, data cubes are often constructed with many dimensions, but users typically look at just three at a time. For example, a consumer packaged goods manufacturer might build a multidimensional data cube with information about unit sales, shelf space, unit price, promotion price, and level of newspaper advertising—all for a specific product, on a specific date, in a specific store.

In the retail industry, OLAP is used to help firms to predict better cus- tomer demand and maximize sales. Starbucks employs some 149,000 workers in 10,000 retail stores in the United States. The firm built a data warehouse to hold 70 terabytes of point-of-sale and customer loyalty data. This data is com- pressed into data cubes of summarized data to enable users to perform OLAP analysis of store-level sales and operational data.13

Drill-Down Analysis The small things in plans and schemes that don’t go as expected can frequently cause serious problems later on—the devil is in the details. Drill-down analysis is a powerful tool that enables decision makers to gain insight into the details of business data to better understand why something happened.

Drill-down analysis involves the interactive examination of high-level summary data in increasing detail to gain insight into certain elements—sort of like slowly peeling off the layers of an onion. For example, in reviewing the worldwide sales for the past quarter, the vice president of sales might want to drill down to view the sales for each country. Further drilling could be done to view the sales for a specific country (say Germany) for the last quarter. A third level of drill-down analysis could be done to see the sales for a specific country for a specific month of the quarter (e.g., Germany for the month of September). A fourth level of analysis could be accomplished by drilling down to sales by product line for a particular country by month (e.g., each product line sold in Germany for the month of September).

FIGURE 9.5 A data cube The data cube contains numeric facts that are categorized by dimensions, such as time and geography.

Time dimension

Geography dimension

Metric dimension

drill-down analysis: The interactive examination of high-level summary data in increasing detail to gain insight into certain elements—sort of like slowly peeling off the layers of an onion.

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Brisbane is a city on the east coast of Australia that is subject to frequent creek flash flooding from the many streams in the area. One year, particularly heavy rainfall caused many houses to be flooded, brought down power lines, closed roads, and put the city into a state of emergency. Following this disaster, the city installed telemetry gauges across Brisbane to obtain real-time measure- ments of rainfall and water levels. The data is captured and displayed on color- coded maps, which enable staff to quickly spot areas of concern. They can also perform a drill-down analysis to see increasing levels of detail within any critical area. The system enables staff to provide more advanced warnings to the popu- lation of impending flooding and take action to close roads or clean up debris.14

Linear Regression Simple linear regression is a mathematical technique for predicting the value of a dependent variable based on a single independent variable and the linear relationship between the two. Linear regression consists of finding the best- fitting straight line through a set of observations of the dependent and indepen- dent variables. By far, the most commonly used measure for the best-fitting line is the line that minimizes the sum of the squared errors of prediction. This best- fitting line is called the regression line (see Figure 9.6). Linear regression does not mean that one variable causes the other; it simply says that when one value goes up, the other variable also increases or decreases proportionally.

The regression line can be written as Y ¼ a þ bX þ “. In this equation, the following are true:

● X is the value of the independent variable that is observed ● Y is the value of the dependent variable that is being predicted ● a is the value of Y when X is zero, or the Y intercept ● b is the slope of the regression line ● ” is the error in predicting the value of Y, given a value of X

The following key assumptions must be satisfied when using linear regression on a set of data:

● A linear relationship between the independent (X) and dependent (Y) variables must exist.

● Errors in the prediction of the value of Y are distributed in a manner that approaches the normal distribution curve.

● Errors in the prediction of the value of Y are all independent of one another.

FIGURE 9.6 Simple linear regression This graph shows a linear regression that predicts students’ final exam scores based on their math aptitude test score.

100

160

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