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Temesgen Kifle
We consider questions like these across three topics:
Read more
We consider questions like these across three topics:
Read more
We consider questions like these across three topics:
- Topic 1 starts with simple, familiar ideas like correlation and builds on these to consider how simple linear regression can be applied to quantify the relationships between variables.
- Topic 2 examines multiple linear regression and considers how we can establish models that allow us to predict values for variables of interest in circumstances where there are many variables at work.
- Topic 3 considers the details of time series forecasting , using different methods of trend fitting to make predictions about future data.
What you'll learn
Upon successful completion of this course, you will be able to:
- Interpret the different components of a linear regression equation.
- Distinguish between statistical measurements such as R, R2 and adjusted R2 to assess goodness-of-fit for a regression model.
- Use technological tools to construct simple and multiple linear regression models.
- Describe the components of a time series.
- Select from a range of different methods to determine the most appropriate choice for trend fitting and forecasting for a given set of time series data.
Register for this course and see more details by visiting:
OpenCourser.com/course/yzdrca/statistics
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possible dealbreakers
Develops the statistics skill of quantifying relationships between variables through linear regression techniques
Introduces the principles of time series analysis and forecasting
Suitable for those seeking to enhance their understanding of statistical methods for data analysis
Provides practical applications of regression techniques to real-world scenarios
Requires a basic understanding of statistical concepts
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Save Statistics for Business Analytics: Modelling and Forecasting to your list so you can find it easily later:
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Activities
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 for Business Analytics: Modelling and Forecasting with these
activities:
Review Elementary Statistics
Show steps
Review basic statistical concepts and techniques to enhance understanding of regression analysis.
Browse courses on
Probability
Show steps
-
Review probability distributions, including normal, binomial, and Poisson distributions.
-
Practice calculating measures of central tendency, such as mean, median, and mode.
Review: Introduction to Statistical Learning
Show steps
Supplement course material with a comprehensive text covering statistical learning concepts.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read selected chapters to reinforce regression analysis and forecasting techniques.
-
Complete exercises and examples provided in the book.
Online Tutorials on R
Show steps
Gain proficiency in using R, the statistical software utilized in the course, through guided tutorials.
Browse courses on
R Programming
Show steps
-
Complete interactive tutorials covering the basics of R, such as data manipulation and visualization.
-
Practice using R for statistical analysis, including regression and forecasting.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Course Materials Review
Show steps
Enhance understanding of course concepts by organizing and reviewing notes, assignments, and quizzes.
Show steps
-
Organize and summarize course notes into a cohesive document.
-
Complete practice problems and review solutions to solidify understanding.
Regression and Forecasting Exercises
Show steps
Reinforce understanding of regression and forecasting concepts through solving practice problems.
Browse courses on
Regression Analysis
Show steps
-
Apply regression techniques to simulated datasets, analyzing results and interpreting coefficients.
-
Develop time series forecasts based on historical data, evaluating forecast accuracy.
Data Collection and Analysis Project
Show steps
Develop a real-world data collection and analysis project to apply regression techniques and forecasting methods.
Browse courses on
Data Analysis
Show steps
-
Identify a research question and collect relevant data.
-
Clean and prepare the data for analysis.
-
Build regression models and evaluate their performance.
-
Create time series forecasts and analyze their accuracy.
Regression Model Report
Show steps
Summarize and present findings of a regression analysis in a comprehensive report.
Browse courses on
Regression Analysis
Show steps
-
Describe the research question, data used, and regression techniques employed.
-
Interpret regression results, including significance tests and confidence intervals.
-
Draw conclusions and discuss implications of the analysis.
Review Elementary Statistics
Show steps
Review basic statistical concepts and techniques to enhance understanding of regression analysis.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability distributions, including normal, binomial, and Poisson distributions.
- Practice calculating measures of central tendency, such as mean, median, and mode.
Review: Introduction to Statistical Learning
Show steps
Supplement course material with a comprehensive text covering statistical learning concepts.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters to reinforce regression analysis and forecasting techniques.
- Complete exercises and examples provided in the book.
Online Tutorials on R
Show steps
Gain proficiency in using R, the statistical software utilized in the course, through guided tutorials.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Complete interactive tutorials covering the basics of R, such as data manipulation and visualization.
- Practice using R for statistical analysis, including regression and forecasting.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Course Materials Review
Show steps
Enhance understanding of course concepts by organizing and reviewing notes, assignments, and quizzes.
Show steps
Show steps
Show steps
- Organize and summarize course notes into a cohesive document.
- Complete practice problems and review solutions to solidify understanding.
Regression and Forecasting Exercises
Show steps
Reinforce understanding of regression and forecasting concepts through solving practice problems.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Apply regression techniques to simulated datasets, analyzing results and interpreting coefficients.
- Develop time series forecasts based on historical data, evaluating forecast accuracy.
Data Collection and Analysis Project
Show steps
Develop a real-world data collection and analysis project to apply regression techniques and forecasting methods.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a research question and collect relevant data.
- Clean and prepare the data for analysis.
- Build regression models and evaluate their performance.
- Create time series forecasts and analyze their accuracy.
Regression Model Report
Show steps
Summarize and present findings of a regression analysis in a comprehensive report.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Describe the research question, data used, and regression techniques employed.
- Interpret regression results, including significance tests and confidence intervals.
- Draw conclusions and discuss implications of the analysis.
Career center
Learners who complete Statistics for Business Analytics: Modelling and Forecasting will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Building and improving a company's products and services is a key role of a Data Scientist. A Data Scientist uses statistics and data analysis, and this coursework in Statistics for Business Analytics can help get you started. This course in particular is useful as it focuses on predictive modeling, which is a key aspect of being a Data Scientist. The ability to predict trends and patterns in data can help you advance your career in data science.
Market Research Analyst
Market Research Analyst
Market research is a key element of helping a company increase their sales. Market Research Analysts study trends in the market to help companies come up with strategies to increase their profits. This course is particularly useful for Market Research Analysts as it teaches students how to determine the most likely course of trends through time series forecasting.
Business Analyst
Business Analyst
A Business Analyst uses data analysis and modeling to help solve business problems. The coursework in Statistics for Business Analytics will be very helpful in developing the skills needed to be successful. This course covers both simple and multiple linear regression, which are key elements of business analytics.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to make recommendations. They leverage statistical knowledge and modeling to assess the financial health of a company. This course, Statistics for Business Analytics, has a strong focus on statistical modeling, which can help Financial Analysts advance in their careers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use advanced analytical methods to solve complex problems in business. From figuring out how to best staff a call center to determining how to optimize supply chains, OR Analysts help companies find solutions to tricky problems. Statistics for Business Analytics will help you develop some of the skills needed to become an Operations Research Analyst, especially through its focus on predictive modeling.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses statistics and modeling to analyze financial data. This course, Statistics for Business Analytics, will help you develop skills in statistical modeling that are key for this role.
Statistician
Statistician
Statisticians apply statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, from healthcare to finance to education. Statistics for Business Analytics can help you develop some of the foundational skills needed to become a Statistician, particularly in the area of time series forecasting.
Data Analyst
Data Analyst
Data Analysts analyze and interpret data to help businesses make informed decisions. This course in Statistics for Business Analytics can help you build a foundation in data modeling and analysis. Because it has a focus on linear regression, this course is especially helpful.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While this course is not directly related to software engineering, it may be useful for Software Engineers who want to develop a foundation in data analysis and modeling.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. They use data analysis and modeling to improve efficiency and productivity. This course in Statistics for Business Analytics may be useful for Operations Managers who want to develop a deeper understanding of data analysis and modeling.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. They use data analysis and modeling to track the effectiveness of their campaigns. Statistics for Business Analytics may be useful for Marketing Managers who want to develop a stronger foundation in data analysis and modeling.
Financial Manager
Financial Manager
Financial Managers oversee the financial operations of a business. They use data analysis and modeling to make decisions about investments and other financial matters. This course in Statistics for Business Analytics may be useful for Financial Managers who want to develop a stronger foundation in data analysis and modeling.
Human Resources Manager
Human Resources Manager
Human Resources Managers oversee the human resources operations of a business. They use data analysis and modeling to make decisions about hiring, training, and other HR matters. This course in Statistics for Business Analytics may be useful for Human Resources Managers who want to develop a stronger foundation in data analysis and modeling.
Sales Manager
Sales Manager
Sales Managers oversee the sales operations of a business. They use data analysis and modeling to make decisions about pricing, promotions, and other sales matters. This course in Statistics for Business Analytics may be useful for Sales Managers who want to develop a stronger foundation in data analysis and modeling.
For more career information including salaries, visit:
OpenCourser.com/course/yzdrca/statistics
Reading list
We've selected six 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 Business Analytics: Modelling and Forecasting.
Save
Comprehensive and practical treatment of forecasting methods, covering the most common methods as well as more advanced topics such as time series decomposition and forecasting with explanatory variables.
Comprehensive and authoritative treatment of regression analysis, covering a wide range of topics. It is considered a classic textbook in the field.
More advanced treatment of statistical learning, covering a wide range of topics in depth. It is considered a standard reference in the field.
Comprehensive and accessible introduction to statistical learning, covering a wide range of supervised and unsupervised learning methods.
Provides a comprehensive overview of forecasting methods and applications.
Save
Is an advanced text on time series analysis. It is useful as a reference for more complex time series forecasting.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yzdrca/statistics
Share
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Effort
4 to 8 hours per week for 10 weeks
Level
Introductory
Via
edX
Institution
The University of Queensland
Instructor
Temesgen Kifle
Session
November 30, 2023
- May 14, 2024
Language
English
Transcript
English
Good to know
Develops the statistics skill of quantifying relationships between variables through linear regression techniques
Introduces the principles of time series analysis and forecasting
Suitable for those seeking to enhance their understanding of statistical methods for data analysis
Provides practical applications of regression techniques to real-world scenarios
Requires a basic understanding of statistical concepts
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