course outlines_statistics

8/16/2019 Course Outlines_statistics

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Indian School of Business

Statistical Methods for Management Decisions

Academic Year & Term, 2016, Term 1

Instructor: Robert Stine ( Sessions 1 – 5)

Affiliation: Wharton School, University of Pennsylvania.

Email: ISB id:Home School id: [email protected]

Office Hours: Mon-Thurs, 18:00-19:30

Instructor: Richard Waterman ( Sessions 6 – 10)

Affiliation: Wharton School, University of Pennsylvania.

Email: ISB id:Home School id: [email protected]

Office Hours: ___ day to ____day from

___Hrs to ___ Hrs

Course Objective and Key-takeaways from the course

This course develops the key statistical ideas that are essential in business decision-making. The courseconcentrates on understanding, modeling, and managing the variability that surrounds business information. Thecourse focuses on applied statistical modeling, in particular the foundations of inference and the use of the linearregression model. The course begins by developing methods that describe variation and ultimately allowinferences and judgment in the presence of uncertainty. The course then develops the linear regression model asa framework for modeling associations among economic processes. Students will be expected to appreciate theunderlying concepts and will be required to put these concepts to practical use in the analysis of data.

Learning Goals

1. Learn the role of data and statistical modeling in business decision making . Data provide information thatallows managers to make informed choices, but the growing presence of too much data of poor qualityleads only to confusion. Managers need the ability to relate data to business tasks, critically assessing thequality of information and presence of random variation. Statistics provides methods that penetrate thefog of information and produce profitable insights.

2. Identify opportunities for integrating quantitative methods into business processes . Statistical methodshave great relevance across the spectrum of management roles, but only when managers are able toseize upon the opportunity. This course places statistical methods within the context of challengingmanagement decisions, ranging from managing human resources to allocating financial assets.

3. Communicate the relevance and outcomes of statistical analysis to a business audience. Statisticalmodeling is powerful, but only to the degree that managers are able to convey the results of models tocolleagues and decision-makers. Students will be expected to explain the relevance of data and modelsto decisions.

4. Recognize the limits and assumptions of statistical methods. Statistical methods require care in assessingwhether important conditions have been met. Failure to check assumptions often leads to misleading

conclusions and deceptive answers that border on unethical behavior.5. Collaborate in the modeling process. Statistical modeling requires the collaboration of many players,

from those who gather data and build models to those who implement the results within the businessprocess. Students will participate in teams that develop and present statistical results. Central to theseskills is the ability to convey the implications of quantitative analysis upon business decisions.

Required Text Book(s)

Stine & Foster (2013). Statistics for Business: Decision Making and Analysis . Addison Wesley. SecondEdition.


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Recommended Text Book(s) o Foster, Stine, and Waterman (1998). Business Analysis using Regression . Springer.o Foster, Stine, and Waterman (1998). Basic Business Statistics . Springer.

Software requirements for the course

JMP software will be required for the course.

Session-Wise Topics/Readings

S No. Topic Intended learning outcome References1 Quality control and

sampling variationRole of independenceProperties of simple random samplesShape of a distribution, includingrecognizing deviations from normality.

Distinction between sample statistics andpopulation parameters

Role of normal distributionsStandard error of the mean and its use indefining control charts

Consequences of Type I and Type II errorsRelevance of central limit theorem in

developing testing methods

SF, review Chapter 13-14(Chapter 1-6 supplybackground that may beneeded to review.)

2 Confidenceintervals

Interpretation of a confidence interval as amethod of describing a population

Design of a sampling method to obtain asought level of precision; margin of error

Identify key assumptionsConfidence interval for a proportion using anormal model

Confidence interval for a mean usingmethods that allow estimates of variation

SF, Chapter 15

3 Hypothesis tests Relationship among hypothesis testing,confidence intervals, and control charts

Choice of null and alternative hypothesesand the connection to break-even analysis

Errors in testing and consequences for thebusiness manager

Tests for proportions and meansDistinguishing statistical significance fromsubstantive importance

SF, Chapter 16

4 Comparing twosamples

Use of randomized experiments in businessand the presence of confounding

Design of comparisons between twosamples and matched pairs

Procedures for comparing two samples

using proportions and meansRecognizing the role of dependence in thedesign of comparisons

SF, Chapter 17

5 Linear regressionmodel

Using lines and curves to capture therelationship between processes

Interpreting the parameters that define anequation fit to data

Least squares estimationRole of residuals in checking quality of fitSummarizing the precision of a fitted modelPredicting new observations

SF, Chapter 19-20


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6 The use of linear

regression inbusiness problems

Review of linear regression models. Assumptions and diagnostics.Case studies illustrating the types ofbusiness problems that simple regressioncan address.

SF, Chapter 21-22

7 Inference inregression models

Prediction intervals for a new observation.Confidence intervals for the regression line.Hypothesis testing for the regression slope.T-statistics, p-values and confidenceintervals.

SF, Chapter 21-22

8 Introduction tomultiple regression

Multiple regression model definition andassumptions.

Marginal v. partial association.Graphical diagnostics in multipleregression: the scatterplot matrix, leverageplots, Mahalanobis plot and the 3-Dscatterplot.

SF, Chapter 23

9 Collinearity andinference in multipleregression

Definition of collinearity: consequences,diagnostics and remedies.

Hypothesis testing in multiple regression: t-tests, omnibus F-test and the Partial F-test.

Review of assumptions in multipleregression.

SF, Chapter 23-24

SF, Chapter 24

10 Two-levelcategoricalpredictor variablesin regression

Making comparison between two groups.Inference for a two-level categorical.Parallel lines and interaction models.

Least squares means.

SF, Chapter 25

Evaluation Components

Assignment Schedule

Name of theComponent

Date of Submission/Deadline

Take-homeor in-class

Group Assignment

(Y/N)

Instructions to students on wordlimit/format of submission etc

CodingScheme

Project 1 May 2 TH Y Initial descriptive data analysis of

project data set for team.2N-A or B

Project 2 May 9 TH Y Inference-based analysis ofvariables in project data set. 2N-A or B

Project 3 May 22 TH Y Simple regression model forproject data set 2N-A or B

Project 4 May 29 TH Y Multiple regression model forproject data set 2N-A or B

Students can talk freely with others within their team about the project, but not with those from otherteams. Reference materials from the course (text, etc) are allowed, but we would not want them usingcoursework submitted by prior students.

Team project 30% Learning teams assigned by ISB will produce weekly analyses of datafrom a business scenario. Teams will produce a concise writtenreport, and selected teams will present results in class. Collaborationis not permitted outside the assigned student teams. Each team isexpected to produce its analysis without assistance from others.

Midterm exam 30% Covers material from the first 4 classes; multiple choice format;closed book

Final exam 40% Covers material from the entire course, with emphasis on the final 6classes (regression analysis)


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Attendance & Punctuality

Learning is an interactive process. ISB students are admitted partly based on the experiences they bring to thelearning community and what they can add to class discussions. Therefore attendance is an important aspect ofstudying here. You have to be present in all the classes. Absence is only appropriate in cases of extremepersonal illness, injury, or close family bereavement. Voluntary activities such as job interviews, business schoolcompetitions, travel plans, joyous family occasions, etc. are never valid reasons for missing any class. Thefaculty with the assistance of the Academic Associate will keep track of your attendance and decide on the natureand extent of penalty for any absence from the class. Penalty may include reduction in grade.

Coding scheme for ALL course work

What kinds of collaborative activities areallowed? What material can be referred to?[1]

References/CodingScheme

Can I discuss generalconcepts and ideasrelevant to theassignment withothers?

Can I discuss specificissues associatedwith the assignmentwith others?

Can I refer toexternalmaterial?[2]

Can I refer to thecase-studysolutions orproblem setsolutions?

4N N N N N 3N- a

Y

N

N

N 3N-b N N Y N

2N-a Y Y N N 2N-b Y N Y N 2N-c N N Y Y

1N Y Y Y N 0N Y Y Y Y

As a general rule:

Students are responsible for submitting original work that reflects their own effort and interpretation.Remember that any submission should be your own work and should not be copied in part or verbatim fromany other source whether external or internal.

An honour code violation is an honour code violation. A violation under coding scheme 0N is not less severethan others. A 0N coding scheme submission is judged against a 0N coding scheme, and a 4N codingscheme submission is judged against a 4N coding scheme; therefore, any honour code violation is equallysevere irrespective of the coding scheme of the submission.

Students can discuss cases and assignments with the course instructor and the Academic Associate for thecourse.

Required and recommended textbooks for the course and the course pack can be used to answer anyindividual or group assignment.

Although not all submissions may be subject to academic plagiarism checker (e.g. turn-it-in), in retrospect,if the Honour Code committee feels the need, any of the previous submissions of an individual or a groupcan be subjected to turn-it-in or any other academic plagiarism checker technology.

When in doubt, the student should contact the instructor for clarifications.


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Course Name SMMDProfessor Name Stine and WatermanAcademic Associate(s): Santosh Kumar, Bitan Chakraborty

Grading Components Marks will be released within __ days ofsubmission (Please Tick) Soft Copy Submissions(Please Tick)

Individual Assignments 3___ 5___ not applicable Y _______ N_________ Group Assignments 3___ 5x_ __ 7_____ Yx _______ N_________ Quiz 3___ 5___ not applicable Y _______ N_________ Term Report 5___ 7___ 10___ not applicable Y _______ N_________ Other (midterm exam) 5___ 7x ___ 10___ Y _______ Nx _________

After L5 and L10 ____ Only after L10 ____

*AA can inform ASA about any extra venue requirement 1 working day in advance

*All changes to course outline/submission deadlines/class schedule/grading to be posted on LMS.

Evaluation and Grading Policy

*We request that all marks are revealed by first Wednesday of the next term.*All grading timelines to be communicated to the students by end of Lecture 1.*All components (except CP) revaluation policies will be similar to end term exam revaluation policies.

CP

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