Discussion 1

Assigned Readings:

Chapter 1. The Roles of Data and Predictive Analytics in Business

Chapter 2. Reasoning with Data

Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was the most important concept(s), method(s), term(s), and/or any other thing that you felt was worthy of your understanding in each assigned textbook chapter.Your initial post should be based upon the assigned reading for the week, so the textbook should be a source listed in your reference section and cited within the body of the text. Other sources are not required but feel free to use them if they aid in your discussion.

Also, provide a graduate-level response to each of the following questions:

Based on what you have read in Chapters 1-2, please explain how data analytics applies to your current or future role? What value can data analytics bring to your position? Please share your thoughts. Please cite examples according to APA standards.

[Your post must be substantive and demonstrate insight gained from the course material. Postings must be in the student’s own words – do not provide quotes!]

[Your initial post should be at least 450+ words and in APA format (including Times New Roman with font size 12 and double spaced). Post the actual body of your paper in the discussion thread then attach a Word version of the paper for APA review]

The Roles of Data and Predictive Analytics in Business

Chapter 1

Learning Objectives

Explain how predictive analytics can help in business strategy formulation.

Distinguish structured from unstructured data.

Differentiate units of observation.

Outline a data-generating process.

Describe the primary ways that data analysis is used to aid business performance.

Discriminate between lead and lag information.

Discriminate between active and passive prediction.

Recognize questions pertaining to business strategy that may utilize (active) predictive analytics.

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Defining Data & Data Uses in Business

Data

A collection of information

Database

Organized collection of data that firms use for analysis

Business analytics

The use of data analysis to aid in business decision making

Predictive analytics

The use of data analysis to designed to form predictions about future, unknown, events or outcomes

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Business Strategy

Plan of action designed by a business practitioner to achieve a business objective

Business objectives include profit maximization, enhanced employee satisfaction, etc.

Examples of action include pricing decisions, advertising campaigns, and methods of employee compensation

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Predictive Analytics for Business Strategy

With no data, strong theoretical model is often not enough to predict effective business strategies

Sound theoretical arguments coupled with data becomes a strong tool to predict effective business strategies

Predictive analytics is an ideal complement to create a successful business strategy

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Data Features

Structured Data

Data with well-defined units of observation which can be classified and structured in the form of a spreadsheet.

An example:

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Data Features

Unstructured Data

Any data that cannot be classified and structured.

An example:

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The Unit of Observation

The entity for which information has been collected

Crucial component of structured data

Tells us the way the in which the information in a dataset varies

Answers the questions: What, Where, Who, When?

Four main groupings: cross-sectional data, pooled cross-sectional data, time-series data, and panel data

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Data Types

Cross-Sectional Data

Data that provide a snapshot of information at one fixed point in time.

An example:

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Data Types

Pooled Cross-Sectional Data

Combination of two or more unrelated cross-sectional data merged into one.

An example

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More Data Types

Time-series data:

Data that exhibit only variation in time

An example

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More Data Types

Panel data

Same cross-sectional units over multiple points in time

An example

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Data Generating Process (DGP)

Data Generating Process

The underlying mechanism that produces the pieces of information contained in a dataset

Steps for DGP

Establish both formal and informal DGP

Understand what variables are important

Create a representative statistical model

Collect and analyze relevant variables and perform simple tests

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Basic Uses of Data Analysis for Business

Categories include:

Queries

Pattern discovery

Causal inference

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Queries

Any request for information from a database

Descriptive Statistics

Quantitative measures meant to summarize and interpret properties of a dataset

Pivot Table

A tool for data summarization that enables different views of the underlying dataset.

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Pattern Discovery

Pattern

Any distinct relationship between observations within a dataset

Pattern discovery

The process of identifying distinctive relationships between observations in a dataset

Data mining

Pattern discovery, typically in large datasets

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Pattern Discovery

Types of Pattern Discovery

Association analysis

Looking for conditional probabilities to determine relationships between two or more variables

Cluster analysis

Groups of observations according to some measure of similarity

Outlier detection

Small subsets of observations, if they exist, that contain information far different from the vast majority of the observations in the dataset

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Examples of Pattern Discovery

Example of Outlier Detection and Cluster Analysis

Example of Association Analysis: Scatter Plot on Profit & Price

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Causal Inference

The process of establishing a causal relationship between a variable(s)representing a cause and a variable(s) representing an effect, where a change in the cause variable results in changes in the effect variable

Causal Inference

Direct: A change in the causal variable, X, directly affects change variable Y

Indirect: A change in X causes a change in Y, but only through its impact on a third variable, Z

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Use of Causal Inference

Causal inference occurs in two ways:

Causal Inference has two important applications

Using Experimentation

Econometric Models

Prediction

Campaign Evaluation

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Data Analysis for the Past, Present, and Future

Lag information

Information about past outcomes

Typically contains information on key performance indicators (KPIs), or variables that are used to help measure firm performance

Designed to answer the question, “What happened/ What is happening?”

Lag information can be generated by queries, pattern discovery, and causal inference

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Examples of Lag Information

Reports

Any structured presentation of the information in a dataset

Scorecards

Any structured assessment of variables of interest, typically KPIs, against a given benchmark

Dashboards

A graphical presentation of the current standing and historical trends for variables of interest, typically KPIs

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Report Example

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Dashboard Example

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Scorecard Example

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Lead Information

Information that provides insights about the future

Designed to answer the question, “What is going to happen?”

It helps firms in its future planning process with expectations and strategic moves.

Lead information is not generally presented in a standardized format

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Predictive Analytics and Lead Information

Predictive analytics is data analysis designed to provide lead information

Two ways predictive analytics can predict the future

Active prediction

Passive Prediction

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Passive Prediction

Passive prediction uses predictive analytics to make predictions based on actual or hypothetical data, where no variables are exogenously altered.

Exogenously altered – a variable in a dataset that changes due to factors outside the data-generating process that are independent of all other variables within the data-generating process

Examples: Weather forecasting, prediction about customers likely to drop service etc.

Pattern discovery (data mining) when used to make predictions, is generally used for passive predictions

Model fit – the basis on which analysts choose among competing models for passive prediction

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Active Prediction

Active prediction uses predictive analytics to make predictions based on actual or hypothetical data, for which one or more variables are exogenously altered.

Making active predictions need causal relationship between variable ‘X’ and variable ‘Y’.

If change in X affects Y, this occurs due to a causal relationship between the two.

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Active Prediction for Business Strategy Formation

Predicting an outcome for alternative strategies requires the application of active prediction

To accurately predict an outcome for a range of competing strategies, you must establish the causal effects of those strategies in that outcome

The leap from correlation to causality is a large one, and can lead to grossly incorrect predictions

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Reasoning with Data

Chapter 2

Learning Objectives

Define reasoning.

Execute deductive reasoning.

Explain an empirically testable conclusion.

Execute inductive reasoning.

Differentiate between deductive and inductive reasoning.

Explain how inductive reasoning can be used to evaluate an assumption.

Describe selection bias in inductive reasoning.

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What is Reasoning?

Reasoning is the process of forming conclusions, judgments, or inferences from facts or data

Reasoning and logic are often used interchangeably

Logic is a description of the rules and/or steps behind the reasoning process

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Two Arguments

Argument 1:

The companies profits are up more than 10% over the past year. An increase in profits of 10% is the result of excellent management. You were the manager over the past year. Therefore, I conclude that you engaged in excellent management last year.

Argument 2:

Ten of your 300 employees came to me with complaints about your management. They indicated that you treated them unfairly by not giving them a raise they deserved. Therefore, I conclude that all of your employees are disgruntled with your management.

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Understanding Reasoning

In presenting the two arguments, the goal is not to make a definitive decision about which you believe (if either)

The goal is to think about and distinguish different “lines” of reasoning

In distinguishing between the different types of reasoning, you will be able to establish why you believe or question the claims made in the two arguments

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Two Major Types of Reasoning

Reasoning

Deductive Reasoning Inductive Reasoning

Both play an important role in interpreting and drawing conclusions from data analysis

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Deductive Reasoning

Goes from the general to the specific

Also known as top-down logic

Seeks to prove statements of the form “If A, then B”

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Deductive Reasoning

Such reasoning always implies three underlying components: assumptions (“If A”), methods of proof (“then”), and conclusions (“B”)

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Deductive Reasoning

The purest applications of deductive reasoning are in the field of mathematics

Two of the most used approaches are direct proofs and transposition

Direct proofs

Proof that begins with assumptions, explains methods of proof, and states the conclusion(s)

Transposition

Any time a group of assumptions implies a conclusion, then it is also true that any time the conclusion does not hold, at least one of the assumptions must not hold

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Direct Proof

Let’s prove the following statement by direct proof:

If X and Y are odd numbers, then their sum (X + Y) is an even number

An Example:

If X = 5(odd) and Y = 9(odd), then their sum X + Y = 14 is an even number

Failing to find a contradiction is not the same a proving a statement is generally true

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Direct Proof: A Mathematical Approach

If X and Y are odd numbers, then their sum (X + Y) is an even number

X and Y are odd numbers

If X is an odd number, then X can be written as X=2K+1, where K is an integer. (Example: X=13 X=(2 × 6)+1)

If Y is an odd number, then Y can be written as Y =2M+1, where M is an integer, (Example: Y=23 Y=(2 × 11)+1)

X+Y=(2K+1)+(2M+1)=2K+2M+2=2(K+M+1)

K+M+1 is an integer so X+Y is 2 times an integer

Any number that is 2 times an integer is divisible by 2

This means X+Y is even

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Direct Proof: Common Sense Approach

“If McDonald’s offers breakfast all day, their revenues will increase.”

McDonald’s stores offer breakfast all day.

The addition of breakfast during lunch/dinner hours implies more choices.

Customers already choosing McDonald’s during lunch/dinner hours can continue buying the same meals at McDonald’s.

Customers not choosing McDonald’s during lunch/dinner hours may start eating at McDonald’s.

Retaining current customers and adding new ones, McDonald’s revenues will increase overall.

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Transposition

While direct proofs are sufficient to prove a point logically, an alternative approach, transposition, may be more effective

Transposition

Is the equivalence between the statements “If A, then B” and “If not B, then A”

Any time a group of assumptions implies a conclusion, then it is also true that any time the conclusion does not hold, then at least one of the assumptions must not hold

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Transposition

“If A, then B” AND “If not B, then not A”

ASSUMPTIONS

(A)

CONCLUSIONS

(B)

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Transposition: A Mathematical Approach

Prove the statement: If X2 is even, then X is even

Suppose X is not an even number; it is instead an odd number

If X is an odd number, then X= (2K +1), where K is an integer

X2 = (2K+1)2 = 4K2 + 4K+1.

4K2 + 4K = 4(K2 +K) and so is divisible by 2

4K2 + 4K is an even number

X2 = 4(K2 +K)+1 is an even number plus 1, meaning it is an odd number

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Transposition

The statement was: If X2 is even, then X is even

Using transposition, the opposite of the conclusion is used to proof the opposite of the assumption: If X is odd, then X2 is even would be incorrect

Transposition can also be used without using mathematics to prove statements like “If A, then B”.

Transposition can be particularly effective if an assumption seems indisputably obvious.

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Transposition: An Example

Proof the statement: “If McDonald’s stores offer breakfast all day, revenue will increase”

McDonald’s stores revenues will not increase

This means total revenues from current and new customers will not increase

This means either there will be no new customers or revenues from current customers will decrease

This means there could not have been an expansion in the menu

McDonald’s stores do not offer breakfast all day

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Direct Proof and Transposition

Direct Proof

State assumptions

Explain methods of proof (mathematics, common sense, etc.)

State conclusions

Transposition

Assume the opposite of the conclusion

Explain methods of proof (mathematics, common sense, etc.)

State assumption(s) that is (are) violated (not A)

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Deductive Reasoning

Used commonly in the application of law

If there is disagreement with a conclusion there are two possible sources:

The method of proof, OR

The assumption

There are two ways of resolving disputes about assumptions

Show robustness- the persistent accuracy of a conclusion despite variation in the associated assumption(s) within the context of a deductive argument

Assess consistency with a collected dataset

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Empirically Testable Conclusions

An empirically testable conclusion is a conclusion whose validity can be meaningfully tested using observable data.

Example:

A banana company’s management staffs are divided into two groups about their product’s placement in a major grocery store chain.

Group 1 believes that change in current location will increase its sales.

Group 2 believes that current location is good enough.

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Empirically Testable Conclusions

Company has the sales data in the current location.

Company chooses to move its product to a new location and collects sales data.

Now the company can meaningfully test the validity of the management’s competing conclusions.

Making the actual decision about the validity of an empirically testable conclusion based on observable data is an application of inductive reasoning

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Inductive Reasoning

Inductive reasoning

Reasoning that goes from the specific to the general; bottom-up logic

Population

The entire set of potential observations about which we want to learn

Data sample

A subset of population that is collected and observed

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Inductive Reasoning

Business regularly collect data samples to draw conclusions about the population after applying inductive reasoning.

The conclusion from inductive reasoning requires degree of support (also called inductive probability).

Degree of support is also called the strength of the inductive argument.

Example: if we are 50% confident about the conclusion, then the degree of support is 50%.

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Degrees of Support

Two Types of Degrees of Support

Both play an important role in interpreting and drawing conclusions from data analysis

SUBJECTIVE DEGREE OF SUPPORT

(IT IS BASED ON OPINION AND LACKING STATISTICAL FOUNDATION)

OBJECTIVE DEGREE OF SUPPORT

(IT HAS A STATISTICAL FOUNDATION AND THUS MORE CREDIBLE THAN SUBJECTIVE DEGREE OF SUPPORT)

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Evaluating Assumptions

Through deductive reasoning, an empirically testable conclusion is made

Collect a data sample

Test the conclusion by comparing the observed outcomes in the data samples to their corresponding probabilities

Use inductive reasoning to decide whether the conclusion passes or fails

If it fails, transposition implies we must reject

If it passes, then we must not reject

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Inductive Reasoning for Evaluating Assumptions

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Selection Bias in Inductive Reasoning

Improper use of inductive reasoning may lead to inaccurate, or biased conclusions

Data-generating process is typically the source of the bias

Survey questions constructed in a leading way

Confirmation bias is the tendency to confirm a claim

Predictable patterns are discovered

Predictable-world bias is the tendency to find order when none exists, and occurs when people “read too much” into perceived patterns from random data

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Selection Bias

Selection bias

The act of drawing conclusions about a population using a selected data sample, without accounting for the means of selection

There are two common types:

Collector selection bias occurs when the collector selects the members of the data sample in a systematic way

Availability bias occurs when the collector of the data sample selects the members of the data sample according to what is most readily available

Member selection bias occurs when potential members of the data sample self-select into, or out of, the sample

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