Asian School of Business PG Programme in Management (2005-06) Course: Quantitative Methods in Management I Instructor: Chandan Mukherjee Session 6b: Measuring.

Slides:

Advertisements

Similar presentations

SADC Course in Statistics The binomial distribution (Session 06)

Advertisements

CHAPTER 21 Inferential Statistical Analysis. Understanding probability The idea of probability is central to inferential statistics. It means the chance.

Sampling distributions. Example Take random sample of students. Ask “how many courses did you study for this past weekend?” Calculate a statistic, say,

Corporate Finance Lecture 5. Topics covered Decision trees Decision trees Dealing with uncertainty Dealing with uncertainty –Sensitivity analysis –Senario.

Three-way choice Option Price Option Value Real Options Calls & Puts Endogenous/Exogenous Option Price Option Value Real Options Calls & Puts Endogenous/Exogenous.

I am sure you have heard about the farmer in Sidell, Illinois. After that fiasco with the cheese being left standing alone taking the blame for polluting.

© 2009, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH Getting to Know Your Data Basic Data Cleaning Principles.

Properties of the Binomial Probability Distributions 1- The experiment consists of a sequence of n identical trials 2- Two outcomes (SUCCESS and FAILURE.

Estimation of parameters. Maximum likelihood What has happened was most likely.

CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference (Sec. )

Maximum Likelihood We have studied the OLS estimator. It only applies under certain assumptions In particular,  ~ N(0, 2 ) But what if the sampling distribution.

CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference.

Aaker, Kumar, Day Seventh Edition Instructor’s Presentation Slides

BCOR 1020 Business Statistics Lecture 18 – March 20, 2008.

CS 561, Sessions 28 1 Uncertainty Probability Syntax Semantics Inference rules.

Population Proportion The fraction of values in a population which have a specific attribute p = Population proportion X = Number of items having the attribute.

Chapter 11a: Comparisons Involving Proportions and a Test of Independence Inference about the Difference between the Proportions of Two Populations Hypothesis.

Lesson 12-1 Algebra Check Skills You’ll Need 12-4

Discrete Probability Distributions

I am sure you have heard about the farmer in Sidell, Illinois. After that fiasco with the cheese being left standing alone taking the blame for the failure.

Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 8-1 Confidence Interval Estimation.

Significance Testing Statistical testing of the mean (z test)

Review of the Binomial Distribution Completely determined by the number of trials (n) and the probability of success (p) in a single trial. q = 1 – p If.

Asian School of Business PG Programme in Management ( ) Course: Quantitative Methods in Management I Instructor: Chandan Mukherjee Session 1: Variability.

Asian School of Business PG Programme in Management ( ) Course: Quantitative Methods in Management I Instructor: Chandan Mukherjee Session 4: Summarising.

1 Subjective Probability for Modeling Uncertainty with Limited Data Efstratios Nikolaidis The University of Toledo April 2009.

7.2 Frequency and Probability Distributions

Chapter 10 Uncertainty in Future Events

Uncertainty in Future Events Chapter 10: Newnan, Eschenbach, and Lavelle Dr. Hurley’s AGB 555 Course.

M11-Normal Distribution 1 1  Department of ISM, University of Alabama, Lesson Objective  Understand what the “Normal Distribution” tells you.

4.1 Probability Distributions. Do you remember? Relative Frequency Histogram.

Highlighting Standards for Mathematical Practice in an Intro Stats Course by Fides Ushe Springfield College, MA.

7.4 – Sampling Distribution Statistic: a numerical descriptive measure of a sample Parameter: a numerical descriptive measure of a population.

Probability. Sample Spaces and Probability Functions Determining Probabilities Venn Diagrams and Tree Diagrams Conditional Probability Binomial Distributions.

Determining Probabilities Using Tree Diagrams and Tables.

Extreme values and risk Adam Butler Biomathematics & Statistics Scotland CCTC meeting, September 2007.

Asian School of Business PG Programme in Management ( ) Course: Quantitative Methods in Management I Instructor: Chandan Mukherjee Session 10: Useful.

NCTM Series Navigating through Navigating through Probability in Grades 9-12 AATM State Conference September 27, 2008 Shannon Guerrero Asst Professor,

Topic 16 Example Confidence Intervals to Estimate a Population Proportion.

MATH 643 Bayesian Statistics. 2 Discrete Case n There are 3 suspects in a murder case –Based on available information, the police think the following.

4. Marketing research After carefully studying this chapter, you should be able to: Define marketing research; Identify and explain the major forms of.

Probability Distribution of a Discrete Random Variable If we have a sample probability distribution, we use (x bar) and s, respectively, for the mean.

1 Math 4030 – 10b Inferences Concerning Proportions.

SECTION 7.2 Estimating a Population Proportion. Practice  Pg  #6-8 (Finding Critical Values)  #9-11 (Expressing/Interpreting CI)  #17-20.

Chapter 7 Lesson 7.2 Random Variables and Probability Distributions 7.2 Probability Distributions for Discrete Random Variables.

Decision Analysis Pertemuan Matakuliah: A Strategi Investasi IT Tahun: 2009.

CHAPTER 3 THE CALCULATIONS OF RISK. Basic risk calculations  Risk severity:  Severity reaches a maximum when we are in a complete uncertainty (where.

Section 7.3 Sample Means. A look back at proportions… You may have noticed that proportions ALWAYS deal with categorical data. You may have noticed that.

Asian School of Business PG Programme in Management ( ) Course: Quantitative Methods in Management I Instructor: Chandan Mukherjee Session 8: Useful.

Lecture 2 Probability By Aziza Munir. Summary of last lecture Why QBA What is a model? Why to develop a model Types of models Flow chart of transformation.

Confidence Intervals and Hypothesis Testing Mark Dancox Public Health Intelligence Course – Day 3.

Methods of Assigning Probabilities l Classical Probability; l Empirical Probability; and l Subjective Probability l P (A) = N(A) / N l P (X) = ƒ (X) /

Cost and Management Accounting: An Introduction, 7 th edition Colin Drury ISBN © 2011 Cengage Learning EMEA Cost and Management Accounting:

Cost effectiveness Analysis: Valuing Health; Valuing Research!

Warm Up Check solutions to homework 7.2….

Discrete Probability Distributions

Sampling Distribution for Proportions

Chapter 7: Introduction to Sampling Distributions

Review of Probability Concepts

POINT ESTIMATOR OF PARAMETERS

Confidence Interval Estimation

Distribution Model A smooth representation of the distribution of ALL individuals in the POPULATION Quantitative Value

Section 3: Estimating p in a binomial distribution

Probability: The Study of Randomness

Population Proportion

Chapter 7: Introduction to Sampling Distributions

Confidence Intervals for Proportions and Variances

Phone no. (00966) Visit at

Sample Proportions Section 9.2.

Maximum Likelihood We have studied the OLS estimator. It only applies under certain assumptions In particular,  ~ N(0, 2 ) But what if the sampling distribution.

Presentation transcript:

Asian School of Business PG Programme in Management ( ) Course: Quantitative Methods in Management I Instructor: Chandan Mukherjee Session 6b: Measuring Uncertainty – Binomial Distribution Model

Statement of the Problem: A Medical Representative Days left to meet the target: 27days Days required to meet the target: 25 days Decision Required: To participate in the All India Meet of the Medical Representatives Days required to participate: One day Uncertainty: Days may be lost due to Flood or Bandh A Decision Problem Under Uncertainty

From Past Data Chance of Flood Due to Rain in a Day: 1 in Days of Bandh in Last Two Years In other words (taking Relative Frequencies as Estimates of Probabilities): P (Flood) = 1/30 = P (Bandh) = 14/730 =

Read the note: binomial.pdf

Application of Binomial Distribution: Example 1 - Anticipating Events Market Research: 12% of the households will buy new vacuum cleaners during Onam Salesperson visits 10 households. What is the likely number of sales?

Application of Binomial Distribution: Example 2 – Checking Assumption Market Research: 12% of the households will buy new vacuum cleaners during Onam Salesperson visits 10 households & sales 3. Were the market researchers right?

Application of Binomial Distribution: Example 3 – Estimating Proportion Salesperson visits 100 households & sales 30 vacuum cleaners. What is the proportion of households in the area will buy vacuum cleaner? Principle of Maximum Likelihood L = n C k p k (1-p) n-k ln L = ln{ n C k } + k.ln(p) + (n-k) ln(1-p) sample proportion