Master statistical analysis for business decision-making through sampling, confidence intervals, and hypothesis testing in this comprehensive 4-week course.
Master statistical analysis for business decision-making through sampling, confidence intervals, and hypothesis testing in this comprehensive 4-week course.
This foundational course explores essential statistical processes for understanding populations through sample analysis. Students learn to determine appropriate sample sizes, calculate confidence intervals, and conduct various forms of hypothesis testing. The course is structured around three main topics: point estimates for population inference, confidence interval calculations for establishing upper and lower limits, and comprehensive hypothesis testing including one-, two-, and many-sample forms. The content is designed to be accessible to beginners while providing practical skills for business analytics applications.
Instructors:
English
English
What you'll learn
Master point estimate analysis for population inference
Calculate and interpret confidence intervals with varying confidence levels
Conduct and interpret hypothesis tests using Z-score and p-value methods
Perform statistical analysis for comparing means and proportions
Apply ANOVA testing for multiple group comparisons
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This comprehensive course focuses on statistical methods for business analytics, covering three essential topics. First, students learn about point estimates and their role in population inference. Second, they master confidence interval calculations for establishing statistical boundaries. Finally, they delve into various forms of hypothesis testing, including one-sample, two-sample, and ANOVA testing. The course emphasizes practical applications in business decision-making while maintaining accessibility for beginners.
Fee Structure
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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