Statistical Inference Fundamentals
Master the essentials of statistical inference, from random sampling to confidence intervals, and learn to apply these concepts to solve real-world problems.
Master the essentials of statistical inference, from random sampling to confidence intervals, and learn to apply these concepts to solve real-world problems.
This comprehensive course, part of the MathTrackX XSeries, focuses on statistical inference fundamentals and their real-world applications. Students explore key concepts including random sampling techniques, sample means and proportions, and confidence intervals. The course emphasizes practical application, building upon probability and random variable knowledge. Led by experts from the University of Adelaide's School of Mathematics and Maths Learning Centre, participants develop essential skills in statistical analysis and problem-solving. The curriculum combines theoretical understanding with hands-on practice, preparing students to apply statistical methods effectively.
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What you'll learn
Master the fundamentals of random sampling and identify potential sources of bias
Understand sample proportions as random variables and their properties
Analyze the distribution of proportions in large sample scenarios
Develop skills in creating and interpreting interval estimates
Apply statistical inference concepts to real-world problems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
The course provides a comprehensive introduction to statistical inference and its practical applications. Students learn about random sampling methods, understanding potential biases and ensuring sample randomness. The curriculum covers sample proportions as random variables, exploring their distribution properties for large samples. Participants study interval estimates for parameters and learn to calculate margins of error. Throughout the course, emphasis is placed on applying these concepts to real-world statistical problems.
Sample means
Module 1
Sample proportions
Module 2
Significance tests
Module 3
Assessment
Module 4
Fee Structure
Instructors
7 Courses
Distinguished Mathematics Educator and Learning Innovation Expert
David Butler serves as Lecturer and Coordinator of the Maths Learning Centre and Writing Centre at the University of Adelaide, where he specializes in helping students develop effective mathematical learning strategies across all disciplines. His academic background combines pure mathematics, with a PhD in Finite Geometry, and educational expertise through a Graduate Diploma in Education. As a former high school mathematics teacher, he brings practical pedagogical experience to his university role. His innovative approach emphasizes the integration of play and artistic elements in mathematical education, making complex concepts more accessible to students. At the Maths Learning Centre, he focuses on developing students' mathematical thinking skills and confidence, working across diverse academic disciplines to support students in mastering course-specific mathematical concepts. His passion for mathematics education extends beyond the classroom through his active engagement in mathematical education communities and his development of creative learning resources.
5 Courses
A Pioneering Statistician Bridging Applied Mathematics and Forensic Science
Melissa Humphries serves as Senior Lecturer in Statistics at the University of Adelaide, where she combines her unique background as a former chef with expertise in statistical analysis and cognitive modeling. After completing her PhD in Statistics and Mathematical Psychology from the University of Tasmania in 2017, she has built an impressive career spanning teaching and research. Her work focuses on applied statistics across diverse fields including forensic science, defense, psychology, and wastewater analysis, with particular emphasis on developing tools that support expert decision-making. As a lecturer, she coordinates multiple courses including second-year undergraduate statistics and has helped develop innovative online learning units in Bayesian reasoning. Her teaching portfolio includes Data Literacy, Statistical Analysis and Modeling, and Research Methods and Statistics. Recently named a Superstar of STEM for 2023-24, she advocates for making academia more accessible while maintaining an active research program in Bayesian inference, stochastic processes, and large dataset management. Her expertise extends to analyzing spatially and temporally autocorrelated data, contributing to fields ranging from astrophysics to forensic anthropology, while her background in psychology enhances her ability to communicate complex technical concepts to diverse audiences.
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