Quantitative Curriculum

Econ 265

Tasks

  1. Overall Purpose

To prepare students to use analytical thinking in all of their business decisions

Students will be able to apply analytical thinking to business decisions (How would we demonstrate that student are able to do this?)

Objectives

Knowledge:

  • Understand the role of data and analytical thinking in solving business problems
  • Students will be able to recognize the role of data and analytical thinking in solving business problems(How would we demonstrate that students are able to do this?)

Skills:Students will be able to:

  • Identify and/or create data appropriate for decision making
  • Summarize data appropriately
  • Draw (appropriate) conclusions and make appropriate conclusions recommendations based on available data
  • Recognize limitations and risks in their decision making

(How would we demonstrate that students are able to do this?)

Attitude:

  • Believe that the best option for decision making is to use data and statistical reasoning
  • Students will have a preference to use data and statistical thinking as primary decision making tools ((How would we demonstrate this?)

Competencies:

  • Use Excel and JMP to summarize and analyze data
  • Students will be able to use Excel and JMP to summarize data, analyze data, and generate business insights ((How would we demonstrate that students are able to do this?)
  1. Link Content to Statistical Thinking
  2. Recognize business context and the role of data in that context
  3. Recognize and quantify variability in the system
  4. Understand Recognizethe impact variability has on decisions
  1. Establish Context and organize course

See attached Modules for Econ 265

  1. Delivery Methods
  2. In class delivery
  3. Case based
  4. Increased group work in class
  5. Increased use of technology – including Excel and JMP
  6. Minimal use of online homework system to reinforce calculations
  7. Phase out use of textbook

Long term – re-visit distance learning and/or hybrid approach

Top Down Approach to Econ 265

Module 1: Motivation

Module 2: Describing data and data summary

Module 3: Probability distributions

Module 4: Estimation

Module 5: Hypothesis Testing

Module 6: Relationships between variables

Module 1: Motivation

Day 1:

  • Activity to introduce Business Intelligence
  • Discussion of Big Data issues
  • Demonstration of Google Analytics

Day 2:

  • Group work – case studies
  • Short cases dealing with BI/BA issues
  • Groups report on issues and solutions

Module 2: Describing Data – 2 weeks (6 class days)

Day 1:

  • Introduce case study
  • Identify business need/questions
  • How would you address the issue
  • Direct discussion to what data would help answer the questions
  • Where would data come from (include discussion of multiple data sources)
  • What kind of data
  • Would it likely be available
  • If data not likely available how would you get it

Day 2:

  • Introduce data company has available
  • Discuss data quality issues
  • Present some data summaries – tables and charts
  • Begin discussion of business solutions (Group work)

Day 3:

  • Based on data and discussion from Day 2 begin developing statistical ideas
  • Location, Spread, Shape
  • Why are they important
  • How do we use the information

Day 4:

  • Use of Excel/JMP to create tables and charts
  • Intertwine use of technology and statistical concepts

For instance, what are the mean and median?

What does changing the class size in the histogram really mean?

Day 5:

  • Groups receive new case study and data
  • Work together in class to answer business questions

Day 6:

  • Groups present their results to “management”
  • Critiques will include style and writing issues
  • Is decision actionable

Module 3: Uncertainty/variability (9-10 class days)

Day 1:

  • Introduce case study – including data
  • Identify business need/questions
  • Direct discussion to variability in the data

Where does the variability come from

Why does it matter

Can we change it – increase or decrease it

  • How does it impact our decision making?

Day 2:

  • Using case study data look at shape of data
  • Introduce Normal distribution
  • How does variability impact the distribution
  • If we reduced the variability what would we see
  • How could we reduce the variability

Day 3:

  • Calculate probabilities from normal distribution
  • Calculate probabilities of interest from case study
  • Use of probabilities to make business decision
  • “What if” analysis – how would decision change
  • Use of percentiles in decision making

Day 4:

  • Groups receive new case study and data
  • Work together in class to answer business questions
  • Groups present their results to “management”

Day 5:

  • Introduce case study to illustrate Binomial distribution
  • Discussion of “uncertainty” in this context
  • Brainstorm other applications that would lead to similar “distribution”

Day 6:

  • Use simple example to motivate calculations/formulas
  • Use technology to calculate probabilities
  • Discussion of what is “unusual”

Day 7:

  • Groups receive case study
  • Use Binomial probabilities to make decision

Day 8:

  • Introduce case study to illustrate Poisson distribution
  • Brainstorm other applications that would lead to similar “distribution”
  • Use technology to calculate probabilities
  • Discussion of what is “unusual” in the context of case study

Day 9:

  • Groups receive case study
  • Use Poisson probabilities to make decision

Module 4: Sampling and Estimation

Day 1:

  • Introduce case study

For instance, the service department at a car dealership needs to know how much to charge for labor for a particular type of repair?

  • How would you address the issue
  • What is the question?
  • Discussion of population versus sample
  • Discussion of sampling issues

Day 2:

  • Groups receive case studies – with sample data
  • Is it a population or sample
  • Summarize data
  • How would you answer the question?
  • Is your answer correct?
  • How sure are you?

Day 3:

  • Based on Day 2 discussion introduce sampling error and sampling distributions
  • How does sample size impact variability
  • So what?
  • Introduce concept of confidence interval
  • How does sample size impact interval

Day 4:

  • Use Excel/JMP to calculate confidence intervals
  • Compare intervals with σ known and σ estimated
  • Groups calculate (using JMP) and use their confidence intervals to make business decisions
  • If we could reduce the variability what happens to the intervals and how does that impact the decision

Day 5:

  • Case studies using proportions

Module 5: Comparisons

Day 1:

  • Introduce case study (compare with a constant – 1 sample testing)
  • What is the business decision?
  • Talk about strategies to use
  • Guide discussion to use of data
  • How do we use data to make the decision

Day 2:

  • Groups get case studies
  • What is the business decision
  • How do we formulate the decision in terms of a parameter?
  • What evidence are we looking for?
  • What do we expect to see if our idea is correct?

Day 3:

  • Use JMP to do hypothesis testing
  • What is the p-value telling us?
  • What decision does this lead to?
  • Could we be wrong?
  • What if we are wrong?

Day 4 and 5:

  • Two sample mean comparisons

Day 6 and 7:

  • Proportions

Module 6: Relationships Between Variables

Day 1:

  • Introduce case study (Two qualitative variables)
  • What is the business decision?
  • Group work – how would you approach this

Guide discussion to contingency table

Describe what you see

Day 2:

  • Conditional probability
  • Chi-square
  • Use in decision making

Day 3:

  • Groups get case studies
  • What is the business decision?
  • Use JMP to do calculations
  • Make connection back to Module 5
  • Make appropriate business decision

Day 4:

  • Introduce case study (Two quantitative variables)
  • What is the business decision?
  • Group work – how would you approach this

Guide discussion to scatterplot

Describe what you see

  • Use scatterplot to make the business decision

Day 5:

  • Use same case study to introduce linear regression
  • Use Excel and/or JMP to do regression calculations
  • Use regression to make decision

Day 6:

  • Discussion of variability in context of regression – based on case study
  • Difference in prediction and estimation

Day 7:

  • Groups get case studies
  • What is the business decision?
  • Use JMP to do calculations
  • Make appropriate decisions

Day 8:

  • Introduce case study for multiple regression
  • What’s the same
  • What’s different
  • Issues and concerns