Statistics for Business Analytics: Modelling and Forecasting from edX

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.

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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

Reviews summary

Statistics for business analytics

According to learners who might take this course, it is likely to offer a strong foundation in statistical methods crucial for business analytics. Students would find the coverage of simple and multiple linear regression, along with various time series forecasting techniques, particularly valuable. The course aims to equip them with skills to use technological tools for model construction and interpretation, making the content practical and applicable to business scenarios.

Well-defined objectives guide a structured learning path.

"The learning objectives were very clear and the course delivered on them precisely."

"I always knew what I was supposed to learn, which made following the structured content easy."

"It was helpful to have a clear roadmap from interpreting equations to selecting forecasting methods."

Builds from basic correlation to complex forecasting.

"It builds from simple correlation to complex forecasting methods, a good progression."

"The distinction between R, R2, and adjusted R2 was explained well, crucial for model assessment."

"I found it comprehensive enough for a foundational course, covering both basic and more advanced topics like multiple regression."

Integrates software for hands-on model construction.

"Using technological tools to construct models made the learning very practical and hands-on."

"It's helpful that they show how to implement these statistical models using relevant software, not just theory."

"I can now confidently use tools to perform simple and multiple linear regression analyses for my work."

Applies statistical concepts to real business problems.

"I appreciate how the course constantly relates statistical concepts back to business analytics scenarios."

"The content feels immediately applicable to predicting values for variables of interest in a business context."

"This course is great for anyone looking to use statistics to make predictions about future data in business."

Strong coverage of essential regression and forecasting.

"This course really broke down simple and multiple linear regression clearly. I finally grasp how to quantify relationships."

"I found the focus on time series forecasting extremely valuable for my business needs."

"It covers exactly what's needed for understanding different components of a linear regression equation."

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

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Review basic statistical concepts and techniques to enhance understanding of regression analysis.

Browse courses on Probability

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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

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Supplement course material with a comprehensive text covering statistical learning concepts.

View An Introduction to Statistical Learning: with... on Amazon

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Read selected chapters to reinforce regression analysis and forecasting techniques.
Complete exercises and examples provided in the book.

Online Tutorials on R

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Gain proficiency in using R, the statistical software utilized in the course, through guided tutorials.

Browse courses on R Programming

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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

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Course Materials Review

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Enhance understanding of course concepts by organizing and reviewing notes, assignments, and quizzes.

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Organize and summarize course notes into a cohesive document.
Complete practice problems and review solutions to solidify understanding.

Regression and Forecasting Exercises

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Reinforce understanding of regression and forecasting concepts through solving practice problems.

Browse courses on Regression Analysis

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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

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Develop a real-world data collection and analysis project to apply regression techniques and forecasting methods.

Browse courses on Data Analysis

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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

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Summarize and present findings of a regression analysis in a comprehensive report.

Browse courses on Regression Analysis

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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

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.

See salaries and explore the career path for Data Scientist

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.

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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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Financial 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Human Resources 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.

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Statistics for Business Analytics

Modelling and Forecasting

What you'll learn

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Statistics for business analytics

Activities

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