Instructor Room No. Office Hours Email Telephone Secretary TA Office Hours Course URL (if any) DISC 203 Probability and Statistics Fall Semester 2016 (Tentative Under review) Hira Nadeem/Fahad Mehmood hira.nadeem@lums.edu.pk; fahad.mehmood@lums.edu.pk suraj.lums.edu.pk/~ro/ COURSE BASICS Credit Hours 3 Lecture(s) Nbr of Lec(s) Per Week 2 Duration 75 minutes Recitation/Lab (per week) Nbr of Lec(s) Per Week Duration Tutorial (per week) Nbr of Lec(s) Per Week Duration COURSE DISTRIBUTION Core Elective Open for Student Category Yes Sophomore Close for Student Category COURSE DESCRIPTION This course is designed to provide students majoring in management and finance with an elementary introduction to probability and statistics with applications. Both descriptive and inferential statistics are covered. We first review techniques for organizing and presenting the raw data and elementary probability theory. Next, we discuss various techniques to make inferences. Along with probability theory, sampling distribution and central limit theorem shall be discussed. The idea of central limit theorem will naturally lead towards the confidence intervals and hypothesis tests for mean and proportion. We follow this discussion with single and multiple regression analysis, model building, design of experiments and categorical data analysis. The course also aims to give a hands on experience with using a statistical package R for carrying out data analysis. The main objective of the course is to provide students with the foundations of statistical inference mostly used in business and economics. COURSE PREREQUISITE(S) Calculus I (Math 101)
COURSE LEARNING OBJECTIVES (CLO) 1. 2. 3. 4. 5. To enable students to solve problems using basic concepts of probability To introduce students to the theory of inferential statistics To enable students to analyze data by identifying appropriate statistical techniques, computing statistics and interpreting results To enable students to use R for statistical analysis of data To enable students to present and defend their empirical analysis effectively LEARNING OUTCOMES (LO) 1. 2. 3. 4. 5. 6. 7. By the end of the course, students should be able to: summarize the data in a useful and informative manner use the basic concepts of probability and random variables explain the concept of the sampling distribution of a static, and in particular describe the behavior of the sample mean describe the foundations of classical inference involving confidence intervals and hypothesis testing and apply inferential methods apply modeling techniques in simple and multiple linear regression analysis discuss critical elements in the design of a sampling experiment and analyze designed experiments using analysis of variance analyze count data with two or more categories use R for statistical analysis of data defend their empirical analysis effectively, both in oral and written forms UNDERGRADUATE PROGRAM LEARNING GOALS & OBJECTIVES General Learning Goals & Objectives Goal 1 Effective Written and Oral Communication Objective: Students will demonstrate effective writing and oral communication skills Goal 2 Ethical Understanding and Reasoning Objective: Students will demonstrate that they are able to identify and address ethical issues in an organizational context. Goal 3 Analytical Thinking and Problem Solving Skills Objective: Students will demonstrate that they are able to identify key problems and generate viable solutions. Goal 4 Application of Information Technology Objective: Students will demonstrate that they are able to use current technologies in business and management context. Goal 5 Teamwork in Diverse and Multicultural Environments Objective: Students will demonstrate that they are able to work effectively in diverse environments. Goal 6 Understanding Organizational Ecosystems Objective: Students will demonstrate that they have an understanding of Economic, Political, Regulatory, Legal, Technological, and Social environment of organizations. Major Specific Learning Goals & Objectives Goal 7 (a) Discipline Specific Knowledge and Understanding Objective: Students will demonstrate knowledge of key business disciplines and how they interact including application to real world situations (Including subject knowledge). Goal 7 (b) Understanding the science behind the decision making process (for MGS Majors) Objective: Students will demonstrate ability to analyze a business problem, design and apply appropriate decision support tools, interpret results and make meaningful recommendations to support the decision maker
Indicate below how the course learning objectives specifically relate to any program learning goals and objectives. PROGRAM LEARNING GOALS AND OBJECTIVES Goal 1 Effective Written and Oral Communication Goal 2 Ethical Understanding and Reasoning Goal 3 Analytical Thinking and Problem Solving Skills Goal 4 Application of Information Technology Goal 5 Teamwork in Diverse and Multicultural Environments Goal 6 Understanding Organizational Ecosystems Goal 7 (a) Discipline Specific Knowledge and Understanding Goal 7 (b) Understanding the science behind the decision making process COURSE LEARNING OBJECTIVES Students get a number of opportunities to demonstrate their ability to communicate effectively (CLO # 5) Students demonstrate an honest reporting and use of data (CLO #5) This is an important objective of the course (CLO # 1,3,5) Students learn to use R for data analysis (CLO # 4) Students work in groups on the project NA Comprehensive coverage of topics in elementary probability and statistics (CLO # 1 5 & LO # 1 9) Students apply appropriate statistical methods to answer data based decision problems (CLO # 1 5) COURSE ASSESSMENT ITEM Project and Exams Project Quizzes, Project and Exams Project Project NA Quizzes, HW, Project and Exams Quizzes, HW, Project and Exams GRADING BREAKUP AND POLICY Quizzes (unannounced): 15% (No make up quiz) Attendance: 5% Midterm Examination: 35% Project: 10% (to be completed in groups number of students in a group: Project submission Deadline: Final Examination: 35% EXAMINATION DETAIL Midterm Exam Final Exam Yes/No: Yes Combine Separate: Combine Duration: 100 minutes Preferred Date: Exam Specifications: closed book, closed notes, help sheet allowed (A4 size, single sided, hand written), calculators allowed Yes/No: Yes Combine Separate: Combine Duration: 100 minutes Exam Specifications: closed book, closed notes, help sheet allowed (A4 size, two sided, hand written), calculators allowed
COURSE OVERVIEW LECTURE 1 TOPICS Introduction: Statistics, Data and Statistical Thinking 2 3 Methods of Describing Sets of Data Graphical Methods; Summation Notation; Central Tendency; Variability; Relative Standing; Standard Deviation RECOMMENDED READINGS Chapter 1 Chapter 2 OBJECTIVES Course introduction Understand the nature and scope of Statistics Choose a suitable way of presenting raw Statistical Data Calculate and interpret measures of central tendency and variability 4 Lab Session Describe data using R Probability Events, Sample Spaces and Probability; Unions and Intersections; Complementary Events; The Additive Rule and Chapter 3 Describe the sample space Compute probabilities Understand the notion of Random Sampling 5 6 Mutually Exclusive Events; Conditional Probability; The Multiplicative Rule and Independent Events; Random Sampling; Bayes Rule Random Variables and Probability Distributions Discrete Random Variables: Probability Distributions for Discrete Random Variables; Expected Values of Discrete Random Variables; The Binomial Random Variable; The Poisson Random Variable Continuous Random Variables: Probability Distributions for Continuous Random Variables; The Uniform Distribution; The Normal Distribution; The Exponential Distribution Sampling Distributions: The Concept of Sampling Distributions; Properties of Sampling Distributions; The Sampling Distribution of the Sample Mean Chapter 4 Chapter 5 Find probabilities for distributions over discrete sets Calculate the mean and variance of a discrete random variable Recognize cases where Binomial Distribution could be an appropriate model; compute probabilities for a Binomial Distribution and approximate Binomial probabilities using a Normal Distribution; Find probabilities for continuous distributions Use the key properties of the Normal Distributions Recognize cases where Poisson, Uniform and Exponential Distributions could be appropriate and compute corresponding Probabilities 7 10 Describe properties of the sampling distribution of sample mean Understand and apply Central Limit Theorem
11 13 14 15 16 17 18 19 20 Chapter 6 Inference Based on a Single Sample: Estimation with Confidence Intervals Large Sample Confidence interval for a Population Mean; Small Sample Confidence Interval for a Population Mean; Large Sample Confidence Interval for a Population Proportion; Determining the sample size; Sample Survey Designs Tests of Hypothesis The Elements of a Test of Hypothesis; Large Sample Test of Hypothesis About a Population Mean; Small Sample Test of Hypothesis About a Population Mean; Large Sample Test of Hypothesis About a Population Proportion; Observed Significance Levels: p values Midterm Exam Inference Based on Two Samples Comparing Two Population Means: Independent Sampling; Comparing Two Population Means: Paired Difference Experiments; Comparing Two Population Proportions: Independent Sampling; Determining the Sample Size; Comparing Two Population Variances: Independent Sampling Categorical Data Analysis: The Chi Square Test Chi Square Tests Simple Linear Regression Probabilistic Models; Fitting the Model: The Least Squares Approach; Model Assumptions; Assessing the Utility of the Model: Making Inference about the Slope; The Coefficients of Correlation and Determination; Using the Model for Estimation and Prediction Lab session Chapter 7 Chapter 8 Chapter 10 Chapter 11 Calculate and interpret Confidence Intervals and Confidence Levels Remember steps in Classical Hypothesis testing Describe Type I and Type II Errors Conduct Tests of Hypothesis according to a given situation and interpret the results. Apply Classical Hypothesis Testing to compare two populations and draw inference Conduct Chi Squared Test Define the concept of least squares estimation in linear regression Explain why correlation need not necessarily imply causation Evaluate the fit of a linear model Conduct inference for the slope and intercept parameters Use R for linear regression
21 22 23 25 26 28 Chapter 12 Multiple Regression and Model Building (Lectures and R demonstrations) Multiple Regression Multiple Regression: The Model and the Procedure; The Least Squares Approach; Model assumptions; Inference About the Slope Parameters; R 2 and the Analysis of Variance F Test; Using the Model for Estimation and Prediction Model Building The Two Types of Independent Variables: Quantitative and Qualitative; Models with a Single Quantitative Independent Variable; Models with Two or More Quantitative Independent Variables; Testing Portions of a Model; Models with One Qualitative Independent Variable; Comparing the Slopes of Two or More Lines; Comparing Two or More Response Curves; Stepwise Regression Project Presentations Define the concept of Least Squares Regression in Multiple Regression Test the utility of a Multiple Regression Model and use it for estimation and prediction Interpret the results of a Multiple Regression Model and draw inference Understand how to select a model that is appropriate for given data Use R for Multiple Regression Analysis TEXTBOOK(S)/SUPPLEMENTARY READINGS Required Texts: Supplementary: Gonick, L. and Smith W.,The Cartoon guide to Statistics, Harper Perennial, NY. Resources for R (statistical computing and graphics software): https://www.rproject.org/ https://www.rstudio.com/