Understand fundamental concepts relating to statistical inference and how they can be applied to solve real world problems.
MathTrackX XSeries Program - University of Adelaide
Welcome to the Statistical Inference course, an integral part of the MathTrackX XSeries Program offered by the University of Adelaide. This comprehensive course is designed to elevate your understanding of mathematics and its real-world applications, with a specific focus on statistical inference, one of the most crucial aspects of statistics.
Building upon the foundation of probability and random variables explored in previous courses, this program delves deep into the realm of statistical analysis. Led by expert instructors from the School of Mathematics and the Maths Learning Centre at the University of Adelaide, you'll embark on an enlightening journey through the intricacies of random sampling, sample means and proportions, confidence intervals, and one-sample tests.
While there are no formal prerequisites for this course, a basic understanding of probability and random variables would be beneficial. The course is designed to build upon knowledge gained from previous courses in the MathTrackX XSeries, but newcomers with a foundational understanding of mathematics can also succeed.
This course is ideal for students, professionals, and enthusiasts who want to:
The skills acquired in this Statistical Inference course have wide-ranging applications across numerous industries and disciplines. Learners will be able to:
By mastering these concepts, you'll not only enhance your mathematical prowess but also gain a competitive edge in today's data-driven world. Join us in this exciting journey to develop your skills and confidence in applying mathematics to solve real-world problems through the MathTrackX XSeries Program.
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This lecture series discusses how the concept of probability can be used to handle, control, and exploit uncertainty in the real-world. It is an undergraduate-level lecture series on probability, but is entirely different from the usual courses on probability theory. The lectures cover the basics of probability theory including the relevant mathematics, but instead of focusing on mathematics, the lectures explain how probability theory can help understand real-world uncertainty using various examples. The examples are used to describe how uncertainty can be exploited to implement modern randomized algorithms such as Markov chain Monte Carlo and deep learning.
Test your knowledge and ability to apply the concepts and methods from the four courses included in the LSE MicroBachelors program in Statistics Fundamentals and take your first step towards further study at undergraduate level or upskill in high-growth careers.
This course covers two important methodologies in statistics – confidence intervals and hypothesis testing. Confidence intervals allow us to make probabilistic statements such as: “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” Hypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug?"
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The first in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
This course provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions – including the normal distribution, which in turn leads to the Central Limit Theorem. We then discuss elementary descriptive statistics and estimation methods.
