About this course: This course introduces core areas of statistics that will be useful in business and for several MBA modules. It covers a variety of ways to present data, probability, and statistical estimation. You can test your understanding as you progress, while more advanced content is available if you want to push yourself. This course forms part of a specialisation from the University of London designed to help you develop and build the essential business, academic, and cultural skills necessary to succeed in international business, or in further study. If completed successfully, your certificate from this specialisation can also be used as part of the application process for the University of London Global MBA programme, particularly for early career applicants. If you would like more information about the Global MBA, please visit www.londoninternational.ac.uk/mba This course is endorsed by CMI
Created by: University of London
Taught by: George Kapetanios, Professor of Finance and Econometrics
King's College London
| Basic Info | Course 4 of 6 in the International Business Essentials Specialization |
| Commitment | 4 weeks of study, 5 hours per week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Using Graphs to Describe Data
In our study of statistics, we learn many methods to help us summarize, analyze, and interpret data with the aim of making informed decisions in an uncertain environment. In this first week we introduce tables and graphs that help us get a handle of data. These tools provide visual support for better decision making. With this in mind, we will guide you through the concept of decisions based on incomplete information. Beginning from here, we will introduce you to the concept of population vs. sample, of parameter vs. statistic and of descriptive statistics vs. inferential statistics. We will then go through the concept of describing data, and we will introduce the idea of creating and interpreting graphs to describe categorical and continuous random variables.
9 videos
- Video: Welcome to Statistics for International Business
- Video: Introduction - Using graphs to describe data
- Video: 1. Decision Making in an Uncertain Environment
- Video: 2. Population and Sample
- Video: 3. Parameters and Statistics
- Video: 4. Descriptive and Inferential Statistics
- Video: 5. Graphs to Describe Numerical Values
- Video: 6. Shape of a Distribution
- Video: Summary
Graded: Quiz: Categorical and Numerical Variables
Graded: Peer Review: Using Graphs to Describe Data
Graded: End of Week Quiz
WEEK 2
Using Measures to Describe Data
This week we will describe and summarize the information in the data using numerical values or measures that are able to summarise information. This is a crucial extension to the analysis of the previous week. While graphs are informative it is usually crucial for improved understanding of the data at hand to discuss their numerical properties. In this week, we will look at a range of measures, such as measures of central tendency, the range, variance, standard deviation, and so on.
9 videos
- Video: Introduction- Using Measures to Describe Data
- Video: 1. Descriptive Statistics- Using Measures to Describe Data
- Video: 2. Measures of Central Tendency and Location
- Video: 3. Mean, Median, and Mode- Which is Best?
- Video: 4. Shape of a Distribution
- Video: 5. Measures of Variability
- Video: 6. Weighted Mean
- Video: 7. Measures of Relationships Between Variables
- Video: Summary
Graded: Summative Questions
Graded: End of Week Quiz
WEEK 3
Probability and Probability Distributions
Probability theory is a young arrival in mathematics- and probability applied to practice is almost non-existent as a discipline. We should all understand probability, and this lecture will help you to do that. It’s important for you to understand first that the world in which your future occurs is not deterministic- and there are future outcomes where a probability model cannot be developed…
This week, we will cover the basic definition of probability, the rules of probability,random variables, -probability density functions, expectations of a random variable and Bivariate random variables.
18 videos
- Video: Probability and Probability Distributions - An Introduction
- Video: 1. Introduction
- Video: 2. Random Experiment
- Video: 3. Events
- Video: 4: Probability
- Video: 4.1: The Definition of Probability
- Video: 4.2: Probability Rules
- Video: 4.3: The Addition Rule of Probabilities
- Video: 4.4: Conditional Probability
- Video: 4.5: The Multiplication Rule of Probabilities
- Video: 5: Random Variables
- Video: 5.1: The Probability Distribution Function
- Video: 6: Properties of Discrete Random Variables
- Video: 6.1: The Variance of a Discrete Random Variable
- Video: 7. Continuous Random Variables
- Video: 8. The Probability Density Function
- Video: 9. The Expectations for Continuous Random Variables
- Video: Probability and Probability Distributions - Summary
Graded: Summative Questions
Graded: End of Week Quiz
WEEK 4
Statistical Estimation
For statistical analysis to work properly, it’s essential to have a proper sample, drawn from a population of items of interest that have measured characteristics. This week, we will cover statistical estimation, sampling distribution of the mean, point estimation, interval estimation, hypothesis testing, the Null hypothesis and look at some real life examples of their use.
19 videos, 4 readings
- Video: Statistical Estimation - An Introduction
- Video: 1. Statistical Estimation
- Video: 2. Estimator and Estimate
- Video: 2.1. Point Estimator and Point Estimate
- Video: 2.2. Unbiased
- Video: 2.3. Efficiency
- Video: 3. Confidence Interval Estimation
- Video: 4. Testing Hypothesis
- Video: 4.1. Formulation of the Null Hypothesis and the Alternative Hypothesis
- Video: 4.2. Test Statistic
- Video: 4.2.1. The Decision Rule
- Video: 4.2.2. Types of Errors
- Video: 4.2.3. Performing the Test and the Decision Rule
- Reading: Practice exercise
- Reading: Solution to practice exercise
- Video: 5. Regression Model
- Reading: Practice exercise
- Reading: Solutions to practice exercise
- Video: Statistical Estimation - Summary
- Video: 1. Further on the Linear Regression Model
- Video: 2. Deriving the OLS b
- Video: 3.The Statistical Properties of the OLS b
- Video: 4. Gauss-Marcov Theorem Proof
Graded: End of Week
Graded: Summative Assignment
Graded: End of Course Quiz
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Very challenging , kind of forgetting my college statistics