Applied Statistics for Business (MAS202)
Vietnamese title: Thống kê ứng dụng trong kinh doanh
Credits: 3

Description:
The course focuses on data summaries and descriptive statistics; introduction to a statistical computer package; probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariance data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus. Teaching method is flexible. Lecturers and tutors can mix between traditional teaching method with project-based learning, problem-based learning and flipped classroom.

Learning outcomes:
Discuss the basic probability rules. CLO2.1. Understand basic probability concepts: Events, Sample spaces, Simple probability, Joint probability, Marginal probability, General addition rule. CLO2.2. Understand Conditional probability, Multiplication rule and Determine if two events are independent. CLO2.3. Use Bayes' theorem to revise probabilities and apply counting rules.
Describe the distribution of a variable. CLO3.1. Discrete probability distributions, Expected value, Variance and Standard deviation of a discrete variable, Binomial and Poisson distribution, Covariance of a probability distribution and its application in Finance. CLO3.2. Compute the probabilities from the normal/uniform distribution and use the normal distribution to solve business problems. CLO3.3. Use the normal probability plot to determine whether a set of data is approximately normally distributed.
Discuss the sampling distributions and Central limit theorem. CLO4.1. Learn about the concept of the sampling distribution. CLO4.2. Describe Sampling distribution of the mean and the Central limit theorem. CLO4.3. Describe sampling distribution of the propotion.
Construct and interpret confidence interval estimates for a population parameter/ difference of population parameters. CLO5.1. Construct a confidence interval for the population mean/difference of two population means and interpret its meaning; determine the size of the sample to improve the estimation. CLO5.2. Construct a confidence interval for the population proportion/difference of two population pproportions and interpret its meaning; determine the size of the sample to improve the estimation.
Verify a claim about a population parameter/ differnce of population parameters by using hypothesis testing. CLO6.1. Formulate the two hypotheses; identify critical values, test statistic and compute P-value for a test of hypotheses on population mean/difference of two population means. CLO6.2. Formulate the two hypotheses; identify critical values, test statistic and compute P-value for a test of hypotheses on population proportion/difference of two population proportions. CLO6.3. Identify type of errors in testing hypotheses. CLO6.4. F-test for the ratio of two variances.
ANOVA analysis. CLO7.1. Use the one-way ANOVA to test for differences among the means of several groups. CLO7.2. Use the two-way ANOVA and interpret the interaction effect.
Regression Analysis. CLO8.1. Discuss about Simple linear/Multiple regression models, determine the simple linear regression equation, understand the meaning of the regression coefficients. CLO8.2. Compute various sums of squares for a set of data pairs, coefficient of determination, standard error of estimate, use residual analysis to visually evaluate the assumptions of regression. CLO8.3. Determine the existence of a significant linear relationship by testing for the Slope and Correlation coefficient. CLO8.4. Evaluate the overall multiple regression model: the coefficient of multiple determination, the adjusted r square, the overall F test.
Be able to present a decision making for a business problem. CLO9.1. Be able to work in a team or individually in making business decisions based on Statistical analysis/ Statistical package (Excel). CLO9.2. Understand of being ethical when conducting statistical analysis.
Discuss the DCOVA framework. CLO1.1. Defining variables: categorical and numerical types, discrete and continuous variables, identify the measurement scales. CLO1.2. Collecting data: population and sample, statistic and parameter, sampling methods, types of survey errors, data cleaning. CLO1.3. Organizing variables by using Summary table, Contingency table, Ordered array, Frequency distribution, Multidimensional tables. CLO1.4. Visualizing variables by using Charts, pitfalls in visualizing variables. CLO1.5. Analysing data: describe the properties of Central tendency, variation and shape of distribution, boxplot, covariance and coefficient of correlation.

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible for doing all exercises, and assignments given by instructors in class or at home and submitting them on time
- Use a laptop in class only for learning purposes
- Promptly access to the course websites for up-to-date course information

Tools:
- Textbook and all learning materials provided
- Internet and a statistical computer package

Materials:
description: Basic business statistics: concepts and applications
author: Mark L.Berenson, David M.Levine, Kathryn A.Szabat, David F.Stephan
publisher: Pearson
published_date: 2019
edition: 14th
isbn: 9781292265032
description: '- PowerPoint Slides
description: '- Instructor's Resource Manual
description: - Test bank (Test gen and Doc. file)

Assessments:
category: Assignment
type: on-going
weight: 20.0
duration: 3 hours
question_type: LO1, LO2, LO3, LO4, LO5, LO6
no_question: OP1: Comprehensive exercises // OP2: (For Constructivism Approach only): Follow lecturer's proposal
knowledge_and_skill: Problem solving of 5-10 questions
grading_guide: Assignment 1 covers Chapters 1, 2, 3, 4, 5; Assignment 2 covers Chapters 6, 7, 8, 9.
note: Each assignment is divided into two parts: the take-home component counts for no more than 50% of the total grade, and the in-class component counts for at least 50% (this may take the form of a presentation, essay, problem-solving, etc.).
category: Computer Project
type: on-going
weight: 15.0
duration: 5 hours
question_type: LO5, LO6, LO7, LO8, LO9
no_question: OP1: Analysis, reports and presentation // OP2: (For Constructivism Approach only): Follow lecturer's proposal
knowledge_and_skill: Open
grading_guide: Cover all Chapters 8, 9, 10, 11, 13, 14
note: OP1: Groups of students are required to collect secondary data (topics may be suggested by instructor), form their own questions, and apply learned knowledge and methods of inferential statistics to analyze data and make conclusions about the populations.
Groups are expected to use a statistical software to perform the analysis and present their results to other groups in class.
The work (slides, source files, etc.) accounts for up to 50% of the grade, while presentation skills and the ability to answer questions account for at least 50%.
OP2: (For Constructivism Approach only): Follow lecturer's proposal
category: Quizzes
type: on-going
weight: 30.0
duration: 30'
question_type: LO1, LO2, LO3, LO4, LO5, LO6, LO7, LO8
no_question: OP1: Multiple choices or written questions.Marked by Computer or a suitable format // OP2: (For Constructivism Approach only): Follow lecturer's proposal
knowledge_and_skill: 10 to 30
grading_guide: - Integration of studied knowledge and skills of chapters assigned- Quiz 1: cover chapter 1, 2, 3, 4, 5- Quiz 2: cover chapters 6, 7, 8, 9- Quiz 3: cover chapters 10, 11, 13, 14
note: In class, by instructor
category: Final exam
type: Final exam
weight: 35.0
duration: 60'
question_type: All LOs
no_question: - Multiple choice. Marked by Computer
knowledge_and_skill: 50 MCQ
grading_guide: All topics, knowledge, skills covered in the classes
note: By exam board

Schedule:
session: Course Introduction
First things first
Chapter 1 Defining and collecting data
1.1 Defining variables
1.2 Collecting data
learning_outcomes: I
T
session: 1.3 Types of sampling methods
1.4 Data cleaning
1.5 Other data processing tasks
1.6 Types of survey errors
learning_outcomes: I
T
materials: MAS202
session: Chapter 2 Organizing and Visualizing variables
2.1 & 2.2 Organizing catergorical variables and numerical variables
2.3, 2.4 Visualizing categorical and numerical variables
learning_outcomes: T
session: 2.5 Visualizing Two Numerical Variables
2.6 & 2.7 Organizing and visualizing a mix of variables
2.8 Filtering and Querying data
2.9 Pitfalls in Organizing and visualizing variables
learning_outcomes: T
session: Chapter 3 Numerical descriptive measure
3.1 Measures of Central tendency
3.2 Measures of Variation and Shape
3.3 Exploring numerical variables
learning_outcomes: IT
session: 3.4 Numerical descriptive measures for a population
3.5 The covariance and the coefficient of corelation
3.6 Descriptive statistics: Pitfalls and ethical issues
learning_outcomes: IT
session: Tutorial:
Solve questions and problems for chapter 0 - 3
Introduce to a statistical package (Excel)
learning_outcomes: U
session: Tutorial:
Solve questions and problems for chapter 0 - 3
Introduce to a statistical package (Excel) (cont. )
learning_outcomes: U
session: Group project release
Homework exercises release
Chapter 4 Basic probability
4.1 Basic probability concepts
4.2 Conditional probability
learning_outcomes: ITU
session: 4.3 Ethical issues and probability
4.4 Bayes's Theorem
4.5 Counting rules
learning_outcomes: ITU
session: Tutorial:
Review chapter 4
Solve questions and problems for chapter 4
Guide to use a statistical package (Excel) (cont. )
learning_outcomes: U
session: Tutorial:
Review chapter 4
Solve questions and problems for chapter 4
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Chapter 5 Discrete probability distribution
5.1 The probability distribution for a discrete variable
5.2 Binomial distribution
learning_outcomes: ITU
session: 5.3 Poisson distribution
5.4 Covariance of a probability distribution and its application in Finance
learning_outcomes: ITU
session: Tutorial:
Solve chapter 5's questions and problems
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Tutorial:
Solve chapter 5's questions and problems
Guide to use a statistical package (Excel) (cont. )
learning_outcomes: U
session: Quiz 1 (Chapter 1+2+3+4+5)

Chapter 6 The normal distribution and other continuous distributions
6.1 Continuous probability distributions
learning_outcomes: ITU
session: 6.2 The normal distribution
learning_outcomes: ITU
session: 6.3 Evaluating normality
6.4 The uniform distribution
learning_outcomes: U
session: Tutorial
Solve questions and problems for chapter 6
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Chapter 7 Sampling distribution
7.1 Sampling dítributions
7.2 Sampling distribution of the mean
learning_outcomes: IT
session: 7.3 Sampling distribution of the proportion
learning_outcomes: IT
session: Tutorial
Solve questions and problems for chapter 6
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Tutorial
Solve questions and problems for chapter 7
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Chapter 8 Confidence interval estimation
8.1, 8.2 & 8.3 Confidence interval estimate for the mean and proportion
learning_outcomes: IT
session: 8.4 Determining sample size
8.5 Confidence interval estimation and ethical issues
learning_outcomes: IT
session: Tutorial
Solve question and problems of chapter 8
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Tutorial
Solve question and problems of chapter 8
Guide to use a statistical package (Excel)(cont. )
learning_outcomes: U
session: Chapter 9 Fundamental of hypothesis testing: one-sample tests
9.1 Fundamentals of hypothesis testing
learning_outcomes: IT
session: 9.2 T-test of hypothesis for the mean
9.3 One-tail tests
learning_outcomes: IT
session: 9.4 Z-test of hypothesis for the proportion
learning_outcomes: U
session: 9.5 Potential hypothesis- testing pitfalls and ethical issues
learning_outcomes: U
session: Tutorial: Solve questions and problems for chapter 9
Guide to use a statistical package (Excel)
session: Tutorial: Solve questions and problems for chapter 9
Guide to use a statistical package (Excel) (cont. )
session: Quiz 2 (Chapter 6+7+8+9)
learning_outcomes: ITU
session: Chapter 10 Two-sample tests
10.1 & 10.2 Comparing the means of two populations
learning_outcomes: ITU
session: 10.3 Comparing the proportions of two independent populations
10.4 F-test for the ratio of two variance
learning_outcomes: TU
session: Tutorial
Solve questions and problems for chapter 10
Guide to use a statistical package (Excel)
learning_outcomes: TU
session: Chapter 11 Analysis of Variance
11.1 The completely randomized design: One-way ANOVA
learning_outcomes: IT
session: 11.2 The Factorial design: Two-way ANOVA (Excel)
learning_outcomes: IT
session: Tutorial
Solve questions and problems for chapter 11
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Tutorial
Solve questions and problems for chapter 11
Guide to use a statistical package (Excel) (cont.)
learning_outcomes: U
session: Chapter 13 Simple Linear Regression
13.1 Simple linear regression models
learning_outcomes: IT
session: 13.2 Determining the simple linear regression equation
13.3 Measures of variation
learning_outcomes: IT
session: 13.4 Assumptions of Regression
13.5 Residual analysis
learning_outcomes: IT
session: 13.7 Inference about the slope and correlation coefficient
13.9 Potential pitfalls in regression
learning_outcomes: IT
session: Tutorial
Solve questions and problems for chapter 13
Guide to use a statistical package (Excel)
learning_outcomes: U
session: Tutorial
Solve questions and problems for chapter 13
Guide to use a statistical package (Excel) (cont.)
learning_outcomes: U
session: Chapter 14 Introduction to multiple regression
14.1 Developing a multiple regression model
14.2 R-square, Adjusted R-square and the overall F test
learning_outcomes: U
session: Tutorial
Solve questions and problems for chapter 14
learning_outcomes: U
session: Quiz 3 (Chapter 10+11+13+14)
session: Quiz 3 (Chapter 10+11+13+14) (review)
session: Group project submission
learning_outcomes: U
session: Group project submission (cont.)
learning_outcomes: U
session: Group project presentation
learning_outcomes: U
session: Group project presentation (cont.)
learning_outcomes: U
session: Group project presentation (Cont.)
learning_outcomes: U
session: Revision and Wrap-up
learning_outcomes: U
session: Revision and Wrap-up (Cont.)
learning_outcomes: U
session: Revision and Wrap-up (cont.)
learning_outcomes: U