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Statistics for International Business

This course is part of International Business Essentials Specialization

Instructor: George Kapetanios

16,789 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

316 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

82%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

316 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

82%

Most learners liked this course

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the International Business Essentials Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in 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 https://mba.london.ac.uk/. This course is endorsed by CMI

Module details

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.

What's included

9 videos2 assignments1 peer review

9 videosTotal 42 minutes

Welcome to Statistics for International Business2 minutes
Introduction - Using graphs to describe data3 minutes
1. Decision Making in an Uncertain Environment3 minutes
2. Population and Sample5 minutes
3. Parameters and Statistics4 minutes
4. Descriptive and Inferential Statistics12 minutes
5. Graphs to Describe Numerical Values8 minutes
6. Shape of a Distribution3 minutes
Summary3 minutes

2 assignmentsTotal 60 minutes

Quiz: Categorical and Numerical Variables30 minutes
End of Week Quiz30 minutes

1 peer reviewTotal 120 minutes

Peer Review: Using Graphs to Describe Data120 minutes

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.

What's included

10 videos2 assignments

10 videosTotal 55 minutes

Introduction- Using Measures to Describe Data3 minutes
1. Descriptive Statistics- Using Measures to Describe Data3 minutes
2. Measures of Central Tendency and Location8 minutes
3. Mean, Median, and Mode- Which is Best?3 minutes
4. Shape of a Distribution6 minutes
5. Measures of Variability13 minutes
5.1 Measures of Variability: Examples10 minutes
6. Weighted Mean2 minutes
7. Measures of Relationships Between Variables4 minutes
Summary2 minutes

2 assignmentsTotal 60 minutes

Summative Questions30 minutes
End of Week Quiz30 minutes

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.

What's included

18 videos2 assignments

18 videosTotal 43 minutes

Probability and Probability Distributions - An Introduction5 minutes
1. Introduction1 minute
2. Random Experiment2 minutes
3. Events5 minutes
4: Probability2 minutes
4.1: The Definition of Probability4 minutes
4.2: Probability Rules2 minutes
4.3: The Addition Rule of Probabilities2 minutes
4.4: Conditional Probability2 minutes
4.5: The Multiplication Rule of Probabilities1 minute
5: Random Variables2 minutes
5.1: The Probability Distribution Function2 minutes
6: Properties of Discrete Random Variables1 minute
6.1: The Variance of a Discrete Random Variable2 minutes
7. Continuous Random Variables3 minutes
8. The Probability Density Function2 minutes
9. The Expectations for Continuous Random Variables3 minutes
Probability and Probability Distributions - Summary2 minutes

2 assignmentsTotal 60 minutes

Summative Questions30 minutes
End of Week Quiz30 minutes

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.