Об этом курсе: 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
Автор: University of London
Преподаватели: George Kapetanios, Professor of Finance and Econometrics
King's College London
| Основные сведения | Курс 4 из 6 — International Business Essentials Specialization |
| Выполнение | 4 weeks of study, 5 hours per week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 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 видео
- Видео: Introduction - Using graphs to describe data
- Видео: 1. Decision Making in an Uncertain Environment
- Видео: 2. Population and Sample
- Видео: 3. Parameters and Statistics
- Видео: 4. Descriptive and Inferential Statistics
- Видео: 5. Graphs to Describe Numerical Values
- Видео: 6. Shape of a Distribution
- Видео: Summary
Оцениваемый: Quiz: Categorical and Numerical Variables
Оцениваемый: Peer Review: Using Graphs to Describe Data
Оцениваемый: End of Week Quiz
НЕДЕЛЯ 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.
10 видео
- Видео: 1. Descriptive Statistics- Using Measures to Describe Data
- Видео: 2. Measures of Central Tendency and Location
- Видео: 3. Mean, Median, and Mode- Which is Best?
- Видео: 4. Shape of a Distribution
- Видео: 5. Measures of Variability
- Видео: 5.1 Measures of Variability: Examples
- Видео: 6. Weighted Mean
- Видео: 7. Measures of Relationships Between Variables
- Видео: Summary
Оцениваемый: Summative Questions
Оцениваемый: End of Week Quiz
НЕДЕЛЯ 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 видео
- Видео: 1. Introduction
- Видео: 2. Random Experiment
- Видео: 3. Events
- Видео: 4: Probability
- Видео: 4.1: The Definition of Probability
- Видео: 4.2: Probability Rules
- Видео: 4.3: The Addition Rule of Probabilities
- Видео: 4.4: Conditional Probability
- Видео: 4.5: The Multiplication Rule of Probabilities
- Видео: 5: Random Variables
- Видео: 5.1: The Probability Distribution Function
- Видео: 6: Properties of Discrete Random Variables
- Видео: 6.1: The Variance of a Discrete Random Variable
- Видео: 7. Continuous Random Variables
- Видео: 8. The Probability Density Function
- Видео: 9. The Expectations for Continuous Random Variables
- Видео: Probability and Probability Distributions - Summary
Оцениваемый: Summative Questions
Оцениваемый: End of Week Quiz
НЕДЕЛЯ 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.
22 видео, 4 материала для самостоятельного изучения
- Видео: 1. Statistical Estimation
- Видео: 2. Estimator and Estimate
- Видео: 2.1. Point Estimator and Point Estimate
- Видео: 2.2. Unbiased
- Видео: 2.3. Efficiency
- Видео: 3. Confidence Interval Estimation
- Видео: 3.1 Confidence Intervals, Part 1
- Видео: 3.1 Confidence Intervals, Part 2
- Видео: 4. Testing Hypothesis
- Видео: 4.1. Formulation of the Null Hypothesis and the Alternative Hypothesis
- Видео: 4.2. Test Statistic
- Видео: 4.2.1. The Decision Rule
- Видео: 4.2.2. Types of Errors
- Видео: 4.2.3. Performing the Test and the Decision Rule
- Видео: 4.2.3.1 Hypothesis Testing: Examples
- Leyendo: Practice exercise
- Leyendo: Solution to practice exercise
- Видео: 5. Regression Model
- Leyendo: Practice exercise
- Leyendo: Solutions to practice exercise
- Видео: Statistical Estimation - Summary
- Видео: 1. Further on the Linear Regression Model
- Видео: 2. Deriving the OLS b
- Видео: 3.The Statistical Properties of the OLS b
- Видео: 4. Gauss-Marcov Theorem Proof
Оцениваемый: End of Week
Оцениваемый: Summative Assignment
Оцениваемый: End of Course Quiz
Часто задаваемые вопросы
Как это работает
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
Авторы
University of London
The University of London is a federal University which includes 18 world leading Colleges. Our distance learning programmes were founded in 1858 and have enriched the lives of thousands of students, delivering high quality University of London degrees wherever our students are across the globe. Our alumni include 7 Nobel Prize winners. Today, we are a global leader in distance and flexible study, offering degree programmes to over 50,000 students in over 180 countries. To find out more about studying for one of our degrees where you are, visit www.london.ac.uk
Стоимость
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Получите сертификат о прохождении курса, ссылкой на который можно делиться с другими людьми | Доступен |
Рейтинги и отзывы
Very challenging , kind of forgetting my college statistics
Great!
Good course but poor content and communication!
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2018 Все права защищены.
Great course !