Statistics for Business and Economics | MANH IELTS - EU-Vietnam Business Network (EVBN)

Instructor: Vu Quang Manh

Office: Nguyen Tat Thanh Campus, Building A, Second Floor, Room A.204

Office Phone: 08.39400989 – ext: 164

Course Website: Information, assignments, and grades will be posted on Google Classroom.

1. About this Course

The purpose of the course is to give students, primarily those in business administration and economics, a conceptual introduction to the field of statistics and its many applications. The course is applications-oriented with the needs of the nonmathematician in mind; the mathematical prerequisite is knowledge of algebra.
Applications of data analysis and statistical methodology are an integral part of the organization and presentation of the course. The discussion and development of each technique are presented in an application setting, with the statistical results providing insights into decisions and solutions to problems.
Although the course is applications-oriented, we use notation generally accepted for the topic being covered. Hence, students will find that this course provides good preparation for studying more advanced statistical material.
The course introduces the student to the software packages of Minitab 15 and Microsoft® Office Excel 2007 and emphasizes the role of computer software in the application of statistical analysis. Minitab is illustrated as one of the leading statistical software packages for education and statistical practice. Excel is not a statistical software package, but the wide availability and use of Excel make it important for students to understand the statistical capabilities of this package. Minitab and Excel procedures are provided to use as much computer emphasis as desired for the course.

Chapter 1 Data and Statistics

Statistics in Practice: BusinessWeek

1.1 Applications in Business and Economics

Accounting Finance Marketing Production Economics
1.2 Data

Elements, Variables, and Observations 5 Scales of Measurement
Categorical and Quantitative Data 7 Cross-Sectional and Time Series Data
1.3 Data Sources

Existing Sources 10 Statistical Studies
Data Acquisition Errors
1.4 Descriptive Statistics
1.5 Statistical Inference
1.6 Computers and Statistical Analysis
1.7 Data Mining
1.8 Ethical Guidelines for Statistical Practice

Summary
Glossary
Supplementary Exercises
Appendix: An Introduction to StatTools

Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations

Statistics in Practice: Colgate-Palmolive Company

2.1 Summarizing Categorical Data

Frequency Distribution
Relative Frequency and Percent Frequency Distributions

Bar Charts and Pie Charts

2.2 Summarizing Quantitative Data

Frequency Distribution
Relative Frequency and Percent Frequency Distributions

Dot Plot
Histogram
Cumulative Distributions
Ogive
2.3 Exploratory Data Analysis: The Stem-and-Leaf Display
2.4 Crosstabulations and Scatter Diagrams

Crosstabulation
Simpson’s Paradox
Scatter Diagram and Trendline

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Motion Picture Industry
Appendix 2.1 Using Minitab for Tabular and Graphical Presentations

Appendix 2.2 Using Excel for Tabular and Graphical Presentations

Appendix 2.3 Using StatTools for Tabular and Graphical Presentations

Chapter 3 Descriptive Statistics: Numerical Measures

Statistics in Practice: Small Fry Design

3.1 Measures of Location

Mean Median Mode Percentiles Quartiles
3.2 Measures of Variability

Range
Interquartile Range

Variance
Standard Deviation

Coefficient of Variation
3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers

Distribution Shape

z-Scores
Chebyshev’s Theorem

Empirical Rule

Detecting Outliers

3.4 Exploratory Data Analysis

Five-Number Summary

Box Plot
3.5 Measures of Association Between Two Variables

Covariance
Interpretation of the Covariance 117 Correlation Coefficient
Interpretation of the Correlation Coefficient
3.6 The Weighted Mean and Working with Grouped Data

Weighted Mean

Grouped Data

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Motion Picture Industry
Case Problem 3: Business Schools of Asia-Pacific
Case Problem 4: Heavenly Chocolates Website Transactions

Appendix 3.1 Descriptive Statistics Using Minitab

Appendix 3.2 Descriptive Statistics Using Excel

Appendix 3.3 Descriptive Statistics Using StatTools

Chapter 4 Introduction to Probability

Statistics in Practice: Oceanwide Seafood

4.1 Experiments, Counting Rules and Assigning Probabilities

Counting Rules, Combinations, and Permutation

Assigning Probabilities 155 Probabilities for the KP&L Project
4.2 Events and Their Probabilities
4.3 Some Basic Relationships of Probability

Complement of an Event

Addition Law
4.4 Conditional Probability

Independent Events

Multiplication Law
4.5 Bayes’ Theorem

TabularApproach

Summary

Glossary

Key Formulas

Supplementary Exercises

Case Problem: Hamilton County Judges

Chapter 5 Discrete Probability Distributions

Statistics in Practice: Citibank

5.1 Random Variables

Discrete Random Variables

Continuous Random Variables
5.2 Discrete Probability Distributions
5.3 Expected Value and Variance

Expected Value

Variance
5.4 Binomial Probability Distribution

A Binomial Experiment

Martin Clothing Store Problem

Using Tables of Binomial Probabilities

Expected Value and Variance for the Binomial Distribution
5.5 Poisson Probability Distribution

An Example Involving Time Intervals

An Example Involving Length or Distance Intervals
5.6 Hypergeometric Probability Distribution

Summary

Glossary

Key Formulas

Supplementary Exercises

Appendix 5.1 Discrete Probability Distributions with Minitab

Appendix 5.2 Discrete Probability Distributions with Excel

Chapter 6 Continuous Probability Distributions

Statistics in Practice: Procter & Gamble

6.1 Uniform Probability Distribution

Area as a Measure of Probability
6.2 Normal Probability Distribution

Normal Curve

Standard Normal Probability Distribution

Computing Probabilities for Any Normal Probability Distribution

Grear Tire Company Problem
6.3 Normal Approximation of Binomial Probabilities
6.4 Exponential Probability Distribution

Computing Probabilities for the Exponential Distribution

Relationship Between the Poisson and Exponential Distributions 255

Summary

Glossary

Key Formulas

Supplementary Exercises

Case Problem: Specialty Toys

Appendix 6.1 Continuous Probability Distributions with Minitab

Appendix 6.2 Continuous Probability Distributions with Excel

Chapter 7 Sampling and Sampling Distributions

Statistics in Practice: MeadWestvaco Corporation

7.1 The Electronics Associates Sampling Problem
7.2 Selecting a Sample

Sampling from a Finite Population

Sampling from an Infinite Population
7.3 Point Estimation

Practical Advice

7.4 Introduction to Sampling Distributions
7.5 Sampling Distribution of x_

Expected Value of x_
Standard Deviation of x_

Form of the Sampling Distribution of x
Sampling Distribution of x_ for the EAI Problem

Practical Value of the Sampling Distribution of x_

Relationship Between the Sample Size and the Sampling Distribution of x_

7.6 Sampling Distribution of p

Expected Value of _p

Standard Deviation of p_
Form of the Sampling Distribution of p

Practical Value of the Sampling Distribution of p

7.7 Properties of Point Estimators

Unbiased 295 Efficiency 296 Consistency
7.8 Other Sampling Methods

Stratified Random Sampling

Cluster Sampling

Systematic Sampling

Convenience Sampling

Judgment Sampling

Summary

Glossary

Key Formulas

Supplementary Exercises

Appendix 7.1 The Expected Value and Standard Deviation of x

Appendix 7.2 Random Sampling with Minitab

Appendix 7.3 Random Sampling with Excel

Appendix 7.4 Random Sampling with StatTools

Chapter 8 Interval Estimation

Statistics in Practice: Food Lion

8.1 Population Mean: Known

Margin of Error and the Interval Estimate

Practical Advice 314
8.2 Population Mean: Unknown
Margin of Error and the Interval Estimate – Practical Advice
Using a Small Sample
Summary of Interval Estimation Procedures
8.3 Determining the Sample Size
8.4 Population Proportion

Determining the Sample Size

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Young Professional Magazine

Case Problem 2: Gulf Real Estate Properties

Case Problem 3: Metropolitan Research, Inc.

Appendix 8.1 Interval Estimation with Minitab

Appendix 8.2 Interval Estimation with Excel

Appendix 8.3 Interval Estimation with StatTools

Chapter 9 Hypothesis Tests

Statistics in Practice: John Morrell & Company

9.1 Developing Null and Alternative Hypotheses

The Alternative Hypothesis as a Research Hypothesis

The Null Hypothesis as an Assumption to Be Challenged

Summary of Forms for Null and Alternative Hypotheses
9.2 Type I and Type II Errors
9.3 Population Mean: Known

One-Tailed Test
Two-Tailed Test
Summary and Practical Advice

Relationship Between Interval Estimation and Hypothesis Testing

9.4 Population Mean: Unknown

One-Tailed Test

Two-Tailed Test

Summary and Practical Advice
9.5 Population Proportion

Summary
9.6 Hypothesis Testing and Decision Making
9.7 Calculating the Probability of Type II Errors
9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean

Summary

Glossary

Key Formulas

Supplementary Exercises

Case Problem 1: Quality Associates, Inc.

Case Problem 2: Ethical Behavior of Business Students at Bayview University

Appendix 9.1 Hypothesis Testing with Minitab

Appendix 9.2 Hypothesis Testing with Excel

Appendix 9.3 Hypothesis Testing with StatTools

Chapter 10 Inference About Means and Proportions with Two Populations

Statistics in Practice: U.S. Food and Drug Administration

10.1 Inferences About the Difference Between Two Population Means
10.2 Inferences About the Difference Between Two Population Means
10.3 Inferences About the Difference Between Two Population Means: Matched Samples
10.4 Inferences About the Difference Between Two Population Proportions

Interval Estimation of p1 – p2

Hypothesis Tests About p1 – p2

Summary

Glossary
Key Formulas
Supplementary Exercises
Case Problem: Par, Inc.
Appendix 10.1 Inferences About Two Populations Using Minitab

Appendix 10.2 Inferences About Two Populations Using Excel

Appendix 10.3 Inferences About Two Populations Using StatTools

Chapter 11 Inferences About Population Variances

Statistics in Practice: U.S. Government Accountability Office

11.1 Inferences About a Population Variance

Interval Estimation

Hypothesis Testing
11.2 Inferences About Two Population Variances

Summary
Key Formulas

Supplementary Exercises

Case Problem: Air Force Training Program
Appendix 11.1 Population Variances with Minitab
Appendix 11.2 Population Variances with Excel
Appendix 11.3 Population Standard Deviation with StatTools

Chapter 12 Tests of Goodness of Fit and Independence

Statistics in Practice: United Way

12.1 Goodness of Fit Test: A Multinomial Population
12.2 Test of Independence
12.3 Goodness of Fit Test: Poisson and Normal Distributions

Poisson Distribution

Normal Distribution

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: A Bipartisan Agenda for Change

Appendix 12.1 TestsofGoodnessofFitandIndependenceUsingMinitab

Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel

Chapter 13 Experimental Design and Analysis of Variance

Statistics in Practice: Burke Marketing Services, Inc. 507

13.1 An Introduction to Experimental Design and Analysis of Variance

Data Collection
Assumptions for Analysis of Variance 510 Analysis of Variance: A Conceptual Overview

13.2 Analysis of Variance and the Completely Randomized Design

Between-Treatments Estimate of Population Variance

Within-Treatments Estimate of Population Variance

Comparing the Variance Estimates: The F Test

ANOVA Table

Computer Results for Analysis of Variance

Testing for the Equality of k Population Means: An Observational Study
13.3 Multiple Comparison Procedures

Fisher’s LSD

Type I Error Rates
13.4 Randomized Block Design

Air Traffic Controller Stress Test

ANOVA Procedure

Computations and Conclusions
13.5 Factorial Experiment

ANOVA Procedure

Computations and Conclusions

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Wentworth Medical Center
Case Problem 2: Compensation for Sales Professionals

Appendix 13.1 Analysis of Variance with Minitab

Appendix 13.2 Analysis of Variance with Excel

Appendix 13.3 Analysis of Variance with StatTools

Chapter 14 Simple Linear Regression

Statistics in Practice: Alliance Data Systems

14.1 Simple Linear Regression Model

Regression Model and Regression Equation

Estimated Regression Equation
14.2 Least Squares Method
14.3 Coefficient of Determination

Correlation Coefficient
14.4 Model Assumptions
14.5 Testing for Significance

14.6 Using the Estimated Regression Equation for Estimation and Prediction
Point Estimation
Interval Estimation
Confidence Interval for the Mean Value of y

Prediction Interval for an Individual Value of y

14.7 Computer Solution
14.8 Residual Analysis: Validating Model Assumptions

Residual Plot Against x

Residual Plot Against yˆ

Standardized Residuals

Normal Probability Plot
14.9 Residual Analysis: Outliers and Influential Observations

Detecting Outliers

Detecting Influential Observations

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Measuring Stock Market Risk

Case Problem 2: U.S. Department of Transportation

Case Problem 3: Alumni Giving

Case Problem 4: PGA Tour Statistics
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas

Appendix 14.2 A Test for Significance Using Correlation
Appendix 14.3 Regression Analysis with Minitab
Appendix 14.4 Regression Analysis with Excel
Appendix 14.5 Regression Analysis with StatTools

Chapter 15 Multiple Regression

Statistics in Practice: dunnhumby

15.1 Multiple Regression Model

Regression Model and Regression Equation

Estimated Multiple Regression Equation
15.2 Least Squares Method

An Example: Butler Trucking Company

Note on Interpretation of Coefficients
15.3 Multiple Coefficient of Determination
15.4 Model Assumptions

15.5 Testing for Significance

F Test
t Test

Multicollinearity
15.6 Using the Estimated Regression Equation for Estimation and Prediction
15.7 Categorical Independent Variables

An Example: Johnson Filtration, Inc.

Interpreting the Parameters
More Complex Categorical Variables
15.8 Residual Analysis

Detecting Outliers
Studentized Deleted Residuals and Outliers

Influential Observations
Using Cook’s Distance Measure to Identify Influential Observations
15.9 Logistic Regression

Logistic Regression Equation
Estimating the Logistic Regression Equation

Testing for Significance
Managerial Use
Interpreting the Logistic Regression Equation

Logit Transformation

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Consumer Research, Inc.

Case Problem 2: Alumni Giving

Case Problem 3: PGA Tour Statistics
Case Problem 4: Predicting Winning Percentage for the NFL

Appendix 15.1 Multiple Regression with Minitab
Appendix 15.2 Multiple Regression with Excel
Appendix 15.3 Logistic Regression with Minitab
Appendix 15.4 Multiple Regression with StatTools

Chapter 16 Regression Analysis: Model Building

Statistics in Practice: Monsanto Company

16.1 General Linear Model

Modeling Curvilinear Relationships 714 Interaction

Transformations Involving the Dependent Variable

Nonlinear Models That Are Intrinsically Linear

16.2 Determining When to Add or Delete Variables

General Case

Use of p-Values
16.3 Analysis of a Larger Problem
16.4 Variable Selection Procedures

Stepwise Regression

Forward Selection

Backward Elimination

Best-Subsets Regression

Making the Final Choice
16.5 Multiple Regression Approach to Experimental Design
16.6 Autocorrelation and the Durbin-Watson Test

Summary

Glossary

Key Formulas
Supplementary Exercises
Case Problem 1: Analysis of PGA Tour Statistics
Case Problem 2: Fuel Economy for Cars
Appendix 16.1 Variable Selection Procedures with Minitab

Appendix 16.2 Variable Selection Procedures with StatTools

Chapter 17 Index

Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics

17.1 Price Relatives
17.2 Aggregate Price Indexes
17.3 Computing an Aggregate Price Index from Price Relatives
17.4 Some Important Price Indexes

Consumer Price Index

Producer Price Index

Dow Jones Averages
17.5 Deflating a Series by Price Indexes
17.6 Price Indexes: Other Considerations

Selection of Items

Selection of a Base Period

Quality Changes
17.7 Quantity Indexes

Summary

Glossary
Key Formulas

Supplementary Exercises

Chapter 18 Time Series Analysis and Forecasting

Statistics in Practice: Nevada Occupational Health Clinic

18.1 Time Series Patterns

Horizontal Pattern
Trend Pattern
Seasonal Pattern
Trend and Seasonal Pattern

Cyclical Pattern

Selecting a Forecasting Method
18.2 Forecast Accuracy
18.3 Moving Averages and Exponential Smoothing

MovingAverages

Weighted Moving Averages

Exponential Smoothing
18.4 Trend Projection

Linear Trend Regression
Holt’s Linear Exponential Smoothing

Nonlinear Trend Regression
18.5 Seasonality and Trend

Seasonality Without Trend

Seasonality and Trend
Models Based on Monthly Data
18.6 Time Series Decomposition

Calculating the Seasonal Indexes
Deseasonalizing the Time Series
Using the Deseasonalized Time Series to Identify Trend

SeasonalAdjustments
Models Based on Monthly Data
Cyclical Component

Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Forecasting Food and Beverage Sales

Case Problem 2: Forecasting Lost Sales

Appendix 18.1 Forecasting with Minitab

Appendix 18.2 Forecasting with Excel

Appendix 18.3 Forecasting with StatTools

Chapter 19 Nonparametric Methods

Statistics in Practice: West Shell Realtors

19.1 Sign Test

Hypothesis Test About a Population Median

Hypothesis Test with Matched Samples
19.2 Wilcoxon Signed-Rank Test
19.3 Mann-Whitney-Wilcoxon Test
19.4 Kruskal-Wallis Test
19.5 Rank Correlation

Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 19.1 Nonparametric Methods with Minitab

Appendix 19.2 Nonparametric Methods with Excel

Appendix 19.3 Nonparametric Methods with StatTools

Chapter 20 Statistical Methods for Quality Control

Statistics in Practice: Dow Chemical Company

20.1 Philosophies and Frameworks

Malcolm Baldrige National Quality Award

ISO 9000
Six Sigma
20.2 Statistical Process Control

Control Charts
x_ Chart: Process Mean and Standard Deviation Known
x_ Chart: Process Mean and Standard Deviation Unknown

R Chart
p Chart
np Chart
Interpretation of Control Charts
20.3 Acceptance Sampling

KALI, Inc.: An Example of Acceptance Sampling

Computing the Probability of Accepting a Lot

Selecting an Acceptance Sampling Plan
Multiple Sampling Plans

Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 20.1 Control Charts with Minitab

Appendix 20.2 Control Charts with StatTools

Chapter 21 Decision Analysis

Statistics in Practice: Ohio Edison Company

21.1 Problem Formulation

Payoff Tables

Decision Trees
21.2 Decision Making with Probabilities

Expected Value Approach

Expected Value of Perfect Information
21.3 Decision Analysis with Sample Information

Decision Tree
Decision Strategy
Expected Value of Sample Information
21.4 Computing Branch Probabilities Using Bayes’ Theorem

Summary

Glossary
Key Formulas

Supplementary Exercises
Case Problem: Lawsuit Defense Strategy

Appendix: An Introduction to PrecisionTree

Chapter 22 Sample Survey On Website

Statistics in Practice: Duke Energy

22.1 Terminology Used in Sample Surveys
22.2 Types of Surveys and Sampling Methods
22.3 Survey Errors

Nonsampling Error

Sampling Error
22.4 Simple Random Sampling

Population Mean
Population Total
Population Proportion

Determining the Sample Size
22.5 Stratified Simple Random Sampling

Population Mean
Population Total
Population Proportion

Determining the Sample Size
22.6 Cluster Sampling

Population Mean
Population Total
Population Proportion

Determining the Sample Size
22.7 Systematic Sampling

Summary

Glossary
Key Formulas

Supplementary Exercises

Tiếng Việt

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Lý thuyết Xác suất và thống kê toán là một lĩnh vực toán học nghiên cứu các hiện tượng ngẫu nhiên. Hiện nay, kiến thức của môn học được ứng dụng rộng rãi trong sản xuất công nghiệp và nông nghiệp, khoa học và công nghệ. Người học cần nắm vững các khái niệm và phương pháp chung về xác suất và thống kê, các định nghĩa, lý thuyết cơ bản và các phương pháp tương ứng, các phương pháp đối phó với hiện tượng ngẫu nhiên bằng phương tiện thiết lập các mô hình thống kê cơ bản. Chúng tôi nhấn mạnh kết hợp lý thuyết và thực hành nhằm giúp người học phát huy khả năng ứng dụng các phương pháp thống kê trong việc và nghiên cứu khoa học.

Chương trình giảng dạy là kết quả của quá trình nghiên cứu công phu giáo trình, bài giảng, bài tập, nghiên cứu tình huống mới nhất hiện đang được giảng dạy ở nhiều trường đại học tiên tiến trên thế giới. Các học liệu này được viết bởi các tác giả nổi tiếng như Anderson; Sweeney & Williams. Đây là những tác giả nổi tiếng và kinh nghiệm giảng dạy, nghiên cứu quốc tế và tư vấn thực tiễn về các lĩnh vực thống kê, định lượng, phân tích, hệ thống thông tin và khoa học tương tác hệ thống.

Chương trình giảng dạy hứa hẹn chứng minh một cách rõ ràng làm thế nào kết quả thống kê cung cấp cái nhìn sâu sắc về các quyết định kinh doanh và trình bày các giải pháp cho các vấn đề kinh doanh đương đại. Chương trình giảng dạy bao gồm hàng trăm tình huống; ví dụ kinh doanh thực tế và các bài tập đáng nhớ với dữ liệu thống kê và thông tin kinh doanh mới nhất.

II. M

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Về kiến thức:

Học xong học phần Lý thuyết xác suất và thống kê toán, người học có khả năng:

– Nhận thức đầy đủ về vai trò và ý nghĩa của thống kê trong hoạt động quản trị, kinh doanh, phân tích kinh tế.

– Hiểu được các phương pháp thống kê căn bản.

– Chọn lựa đúng các kỹ thuật thống kê đã nhằm áp dụng giải quyết các nhiệm vụ liên quan đến số liệu thống kê trong công việc một cách phù hợp.

– Hiểu và ứng dụng được các kết quả của phân tích thống kê.

– Có thể viết một báo cáo kinh doanh theo tiêu chuẩn quốc tế cho các công ty ở nhiều lĩnh vực ngành nghề khác nhau của nền kinh tế.

– Có kỹ năng xử lý và trình bày số liệu thống kê trên Excel.

– Đọc thông thạo một số kết quả

Về kỹ năng:

Đối với người học tích cực nhấn mạnh rèn luyện các Kỹ năng sau:̆ng nghề nghiệp sau:

– Hình thành và phát triển năng lực thu thập dữ liệu, kỹ năng tổng hợp; kỹ năng so sánh, phân tích, bình luận, đánh giá một kế hoạch kinh doanh.

– Phát triển kĩ năng lập luận, thuyết trình trước công chúng.

– Phát triển năng lực tự học hỏi để liên tục cập nhật kiến thức mới phục vụ công việc.

– Hình thành và phát triển kỹ năng cộng tác, làm việc nhóm.

– Phát triển kỹ năng tư duy sáng tạo, khám phá tìm tòi.

– Trau dồi, phát triển năng lực đánh giá và tự đánh giá.

– Phát triển kỹ năng bình luận, thuyết trình trước công chúng

Về thái độ:

Đối với học viên tích cực nhấn mạnh rèn luyện Thái độ làm việc mà doanh nghiệp cần như sau:

– Thái độ chủ động, tích cực trong học tập cũng như công việc.
– Thái độ cầu tiến và ham học hỏi.
– Có tính hợp tác trong làm việc nhóm.
– Có ý thức trách nhiệm cao với công việc và nhiệm vụ được giao thông qua nhiệm vụ đọc tài liệu ở nhà, làm bài tập nhóm và đóng góp vào hoạt động thảo luận của nhóm trên lớp.

Đường dẫn các Video bài giảng trực tuyến

Môn Lý thuyết Xác suất và Thống kê toán

Chương 1: Bao gồm 6 Video sau:

1. Hiểu biết sâu sắc về Xác suất đã giúp Công ty hàng đầu thế giới duy trì được danh tiếng như thế nào?,

2. Có bao nhiêu cách để chọn 3 món hàng từ một nhóm gồm 6 món hàng?,

3. Các ước lượng xác suất tốt nhất có được thường là sự kết hợp của các phương pháp tính xác suất nào?,

4. “Phân tích xổ số kiểu Mỹ” & “Xác suất được Ban an toàn giao thông quốc gia ứng dụng như thế nào?”,

5. Ứng dụng xác suất trong việc xem xét các khoản đầu tư chứng khoán,

6. Ứng dụng rộng rãi của Định lý Bayes trong các phân tích hỗ trợ việc ra quyết định,

Chương 2: Bao gồm 2 Video sau:

1. Phân phối xác suất của biến ngẫu nhiên rời rạc (Phần 1), https://youtu.be/mWfpxap2rf4

2. Phân phối xác suất của biến ngẫu nhiên rời rạc (Phần 2), https://youtu.be/zfGx4f_MGfw