Chương trình đào tạo
16 Sections
96 Lessons
Lifetime
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I. Data and Statistics
10
1.1
A. Statistics in Practice: Bloomberg Businessweek
1.2
B. Applications in Business and Economics
1.3
C. Data
1.4
D. Data Sources
1.5
E. Descriptive Statistics
1.6
F. Statistical Inference
1.7
G. Analytics
1.8
H. Big Data and Data Mining
1.9
I. Computers and Statistical Analysis
1.10
J. Ethical Guidelines for Statistical Practice
II. Descriptive Statistics: Tabular and Graphical Displays
6
2.1
A. Statistics in Practice: Colgate-Palmolive Company
2.2
B. Summarizing Data for a Categorical Variable
2.3
C. Summarizing Data for a Quantitative Variable
2.4
D. Summarizing Data for Two Variables Using Tables
2.5
E. Summarizing Data for Two Variables Using Graphical Displays
2.6
F. Data Visualization: Best Practices in Creating Effective Graphical Displays
III. Descriptive Statistics: Numerical Measures
7
3.1
A. Statistics in Practice: Small Fry Design
3.2
B. Measures of Location
3.3
C. Measures of Variability
3.4
D. Measures of Distribution Shape, Relative Location, and Detecting Outliers
3.5
E. Five-Number Summaries and Box Plots
3.6
F. Measures of Association Between Two Variables
3.7
G. Data Dashboards: Adding Numerical Measures to Improve Effectiveness
IV. Introduction to Probability
11
4.1
A. Statistics in Practice: National Aeronautics and Space Administration
4.2
B. Random Experiments, Counting Rules, and Assigning Probabilities
4.3
C. Events and Their Probabilities
4.4
D. Some Basic Relationships of Probability
4.5
E. Conditional Probability
4.6
F. Bayes’ Theorem
4.7
G. Summary
4.8
H. Glossary
4.9
I. Key Formulas
4.10
J. Supplementary Exercises
4.11
K. Case Problem: Hamilton County Judges
V. Discrete Probability Distributions
8
5.1
A. Statistics in Practice: Citibank
5.2
B. Random Variables
5.3
C. Developing Discrete Probability Distributions
5.4
D. Expected Value and Variance
5.5
E. Bivariate Distributions, Covariance, and Financial Portfolios
5.6
F. Binomial Probability Distribution
5.7
G. Poisson Probability Distribution
5.8
H. Hypergeometric Probability Distribution
VI. Continuous Probability Distributions
4
6.1
A. Statistics in Practice: Procter & Gamble
6.2
B. Uniform Probability Distribution
6.3
C. Normal Probability Distribution
6.4
D. Normal Approximation of Binomial Probabilities
VII. Sampling and Sampling Distributions
9
7.1
A. Statistics in Practice: Meadwestvaco Corporation
7.2
B. The Electronics Associates Sampling Problem
7.3
C. Selecting a Sample
7.4
D. Point Estimation
7.5
E. Introduction to Sampling Distributions
7.6
F. Sampling Distribution of x
7.7
G. Sampling Distribution of p
7.8
H. Properties of Point Estimators
7.9
I. Other Sampling Methods
VIII. Interval Estimation
5
8.1
A. Statistics in Practice: Food Lion
8.2
B. Population Mean: σ Known
8.3
C. Population Mean: σ Unknown
8.4
D. Determining the Sample Size
8.5
E. Population Proportion
IX. Hypothesis Tests
1
9.1
A. Overview of Hypothesis Tests
X. Inference About Means and Proportions with Two Populations
2
10.1
A. Inferences About Two Population Means
10.2
B. Inferences About Two Population Proportions
XI. Inferences About Population Variances
2
11.1
A. Inferences About a Population Variance
11.2
B. Inferences About Two Population Variances
XII. Comparing Multiple Proportions, Test of Independence, and Goodness of Fit
3
12.1
A. Testing the Equality of Population Proportions for Three or More Populations
12.2
B. Test of Independence
12.3
C. Goodness of Fit Test
XIII. Experimental Design and Analysis of Variance (ANOVA)
5
13.1
A. Introduction to Experimental Design and ANOVA
13.2
B. Analysis of Variance: Completely Randomized Design
13.3
C. Multiple Comparison Procedures
13.4
D. Randomized Block Design
13.5
E. Factorial Experiment
XIV. Simple Linear Regression
9
14.1
A. Simple Linear Regression Model
14.2
B. Least Squares Method
14.3
C. Coefficient of Determination
14.4
D. Model Assumptions
14.5
E. Testing for Significance
14.6
F. Using the Estimated Regression Equation for Estimation and Prediction
14.7
G. Computer Solution
14.8
H. Residual Analysis: Validating Model Assumptions
14.9
I. Residual Analysis: Outliers and Influential Observations
XV. Multiple Regression
9
15.1
A. Multiple Regression Model
15.2
B. Least Squares Method
15.3
C. Multiple Coefficient of Determination
15.4
D. Model Assumptions
15.5
E. Testing for Significance
15.6
F. Using the Estimated Regression Equation for Estimation and Prediction
15.7
G. Categorical Independent Variables
15.8
H. Residual Analysis
15.9
I. Logistic Regression
XVI. Regression Analysis: Model Building
5
16.1
A. General Linear Model
16.2
B. Determining When to Add or Delete Variables
16.3
C. Analysis of a Larger Problem
16.4
D. Variable Selection Procedures
16.5
E. Multiple Regression Approach to Experimental Design
Thống Kê Kinh Doanh
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