This course is part of Statistics for Business Analytics.
This introductory course provides a thorough foundation in statistical methods for business analytics, focusing on examining relationships between variables and making future predictions. Students learn to apply correlation and regression analysis, construct predictive models with multiple variables, and use time series forecasting techniques. The course addresses practical business questions about customer experience, demographic analysis, and sales forecasting. Through three main topics, students master simple and multiple linear regression, model assessment, and various forecasting methods, all with a focus on real-world business applications.
Instructors:
English
English
What you'll learn
Interpret and apply linear regression equations for business analysis
Evaluate regression models using R2 and adjusted R2 metrics
Develop multiple linear regression models using statistical tools
Analyze time series components for business forecasting
Select appropriate trend fitting methods for different data sets
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
The course covers fundamental statistical methods for business analytics through three comprehensive topics. Students begin with correlation and simple linear regression, progress to multiple linear regression for complex variable relationships, and conclude with time series forecasting techniques. The curriculum emphasizes practical applications in business contexts, including customer behavior analysis, demographic predictions, and sales forecasting.
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Statistics for Business Analytics
Instructor

3 Courses
Leading Expert in Labor Economics and Job Satisfaction Research
Dr. Temesgen Kifle serves as Lecturer in the School of Economics at The University of Queensland, where he combines expertise in labor economics with innovative research in education and life satisfaction. After completing his PhD and MA from the University of Bremen, he has established himself as an influential researcher with over 233 citations for his work. His research spans multiple areas including immigrant labor markets, job satisfaction, and higher education assessment, demonstrated through his groundbreaking studies on gender differences in job satisfaction and immigrant occupational mobility in Australia. Through his extensive publication record in premier journals including Economic Analysis and Policy and Work, Employment and Society, he continues to advance understanding of labor market dynamics while maintaining active research in student evaluation methods. His expertise particularly shines in analyzing immigrant experiences in the Australian labor market, demonstrated through influential studies on immigrant job satisfaction and occupational mobility. Currently teaching at UQ's School of Economics while maintaining research collaborations across multiple institutions, he contributes significantly to understanding educational outcomes and labor market dynamics through econometric analysis and empirical research methods. His recent work focuses on making student evaluations fairer for teachers and developing strategies to discourage academic plagiarism, while continuing his research on financial literacy among immigrant populations.
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