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Christophe Smet,
Rik Lopuhaä,
and
Annoesjka Cabo

Read more

In many engineering master’s programs, statistics is used quite intensively. As soon as you are dealing with real-life data, you will need to get an idea of what these data tell you and how you can visualize this (descriptive statistics). But you will also want to perform some analysis (inferential statistics): you may want to build a model that mimics reality, estimate some quantities, or test some hypotheses.

The statistics course in this series will help you refresh your knowledge on these topics. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.

This course offers enough depth to cover the statistics you need to succeed in your engineering master’s or profession in areas such as machine learning, data science and more.

This is a review courseThis self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.

This format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. You will get many exercises, to be solved using Grasple or R, for which you will receive intelligent, personal and immediate feedback.

Register for this course and see more details by visiting:
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- Make and interpret numerical and graphical summaries of datasets.
- Use various techniques to find estimators for unknown parameters and how to compare them.
- Construct and interpret confidence intervals, learn how to perform hypothesis testing in various settings, and know how these two concepts are related.

- Perform simple and multiple linear regression on quantitative and categorical variables.
- Apply certain procedures (resampling, bootstrapping, non-parametric approach) when confronted with non-standard situations.
- Use the r software package to perform all these tasks.

- Make and interpret numerical and graphical summaries of datasets.
- Use various techniques to find estimators for unknown parameters and how to compare them.
- Construct and interpret confidence intervals, learn how to perform hypothesis testing in various settings, and know how these two concepts are related.
- Perform simple and multiple linear regression on quantitative and categorical variables.
- Apply certain procedures (resampling, bootstrapping, non-parametric approach) when confronted with non-standard situations.
- Use the r software package to perform all these tasks.

Week 1: Descriptive statistics

graphical summaries of datasets

numerical summaries of datasets

connection with probability theory

Read more

Week 2: Estimator theory

quality of estimators

methods to obtain estimators

Week 3: Hypothesis testing

concepts

how to perform a test in various settings

Week 4: Confidence intervals (CI)

motivation

how to construct a CI in various settings

Week 5: Linear regression

simple and multiple linear regression

categorical variables

interpreting output

Week 6: Bootstrap and resampling

parametric and non-parametric approach

how to deal with non-standard situations

Good to know

Know what's good ,
what to watch for , and
possible dealbreakers

Develops the foundation for the statistics you need to succeed in your engineering master's or profession in areas such as machine learning, data science and more

Offers a comprehensive study of statistics as applied to engineering

Emphasizes the application of concepts and skills through exercises and immediate feedback

Taught by instructors who are experts in the field of statistics and engineering

Covers a wide range of topics, including descriptive statistics, hypothesis testing, and regression

Save Statistics to your list so you can find it easily later:

Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Statistics with these
activities:

Review probability theory

Show steps

This book provides a solid foundation in probability theory, which is essential for understanding the concepts covered in this course.

View
Probability & Statistics for Engineers &...
on Amazon

Show steps

- Read Chapter 1: Introduction to Probability
- Solve the practice problems at the end of the chapter

Learn R for Statistical Analysis

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This activity provides an opportunity to learn the basics of R, which is the statistical software used in this course.

Browse courses on
R Programming

Show steps

- Follow the R Tutorial
- Complete the practice exercises

Practice hypothesis testing

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This activity provides practice in hypothesis testing, which is a key concept in this course.

Browse courses on
Hypothesis Testing

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- Solve the practice problems on hypothesis testing
- Use R to conduct hypothesis tests

Three other activities

Expand to see all activities and additional details

Show all six activities

Form a study group

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This activity provides a supportive environment to discuss the course material and work on assignments with other students.

Show steps

- Find other students who are taking the course
- Schedule regular study sessions

Create a data visualization

Show steps

This activity provides an opportunity to apply the concepts learned in this course to real-world data.

Browse courses on
Data Visualization

Show steps

- Choose a dataset
- Clean and prepare the data
- Create a data visualization
- Interpret the results

Participate in a data science competition

Show steps

This activity provides an opportunity to apply the skills learned in this course to a real-world problem.

Show steps

- Find a data science competition that interests you
- Form a team or work independently
- Develop a solution to the problem
- Submit your solution

Learners who complete Statistics will develop knowledge and skills
that may be useful to these careers:

Statistician

Statisticians collect, analyze, and interpret data to solve problems in a variety of fields, such as healthcare, finance, and engineering. This course may help you build a foundation in statistics, including descriptive statistics, estimator theory, hypothesis testing, and confidence intervals.

Data Scientist

Data Scientists use statistics to analyze and interpret data, helping businesses make better decisions. This course may help you develop the quantitative skills needed for this role, such as statistical modeling, hypothesis testing, and using the R software package.

Actuary

Actuaries use statistics to assess risk and uncertainty, helping businesses make informed decisions. This course may help you develop the mathematical and statistical skills needed for this role, such as probability theory, risk assessment, and using the R software package.

Operations Research Analyst

Operations Research Analysts use statistics to improve decision-making in various industries, such as healthcare, transportation, and manufacturing. This course may help you develop the quantitative and analytical skills needed for this role, such as hypothesis testing, optimization, and using the R software package.

Data Analyst

Data Analysts use statistics to collect, analyze, and interpret data. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

Quantitative Analyst

Quantitative Analysts use statistics and financial modeling to make investment decisions. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

Econometrician

Econometricians use statistics to analyze economic data and build models to predict economic behavior. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

Business Analyst

Business Analysts use data to improve business processes and make better decisions. This course may help you develop the analytical skills needed for this role, such as data summarization, hypothesis testing, and regression analysis.

Epidemiologist

Epidemiologists use statistics to investigate the causes and patterns of disease. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

Biostatistician

Biostatisticians use statistics to analyze and interpret data in the field of healthcare. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

Market Researcher

Market Researchers use statistics to collect and analyze data about consumer behavior and market trends. This course may help you develop the statistical and analytical skills needed for this role, such as survey design, hypothesis testing, and using the R software package.

Financial Analyst

Financial Analysts use statistics to analyze financial data and make investment recommendations. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

Data Engineer

Data Engineers design and build systems for storing and processing data. This course may help you develop the statistical and programming skills needed for this role, such as data summarization, hypothesis testing, and using the R software package.

Risk Manager

Risk Managers use statistics to assess and manage risk. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

For more career information including salaries, visit:
**
OpenCourser.com/course/dudlkn/statistic
**

We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Statistics.

For more information about how these books relate to this course, visit:
**
OpenCourser.com/course/dudlkn/statistic
**

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