Free online courseApplied Statistics

Course Description

Welcome to "Applied Statistics," an intensive 16-hour and 31-minute course designed to introduce you to the foundational concepts and practical applications of statistics. Although it currently has no reviews, this course is an essential starting point for anyone looking to build a solid understanding of statistical methods and their uses.

This course begins with an introduction to the fundamentals of statistics, covering topics such as classifying data and understanding different types of statistical studies, including experiments and observational studies. You will also learn how to critique published studies, ensuring you develop a critical eye for evaluating data and research findings.

Understanding data distribution is crucial, and this course delves into frequency distributions and various methods for displaying both qualitative and quantitative data. Additionally, you will learn how to analyze graphs to extract meaningful insights.

Measures of center and spread are essential concepts in statistics. You will explore how to calculate and interpret measures of center, such as mean and median, and measures of spread, including range, variance, and standard deviation. The course also introduces you to the Empirical Rule, Chebyshev's Theorem, and measures of relative position like z-scores and box plots.

Probability is a fundamental aspect of statistics, and this course provides a comprehensive introduction to probability rules, including the addition and multiplication rules, as well as permutations and combinations. You will practice calculating probabilities and learn about different probability distributions, such as binomial, Poisson, and hypergeometric distributions.

The normal distribution is a cornerstone of statistical analysis. This course covers the properties of the normal distribution, calculating areas under the curve, and determining probabilities. You will also learn how to approximate binomial distributions using the normal distribution and apply the Central Limit Theorem to means and proportions.

Confidence intervals are a key concept in inferential statistics. You will be introduced to constructing confidence intervals for population means and proportions, using both known and unknown standard deviations. The course also covers the Student's t-distribution, which is crucial for small sample sizes.

Comparing two populations is a common statistical task. This course guides you through comparing means and proportions for two populations, including cases with known and unknown variances, and dependent or paired samples.

Hypothesis testing is a critical skill in statistics. You will learn how to formulate and test hypotheses, interpret conclusions, and understand errors in testing. The course covers one-tailed and two-tailed tests for population means and proportions, as well as tests for two sample means with equal and unequal variances.

Finally, you will explore the relationship between variables through scatter plots, correlation, and linear regression. The course covers determining statistical significance for the Pearson correlation coefficient, creating and interpreting the Least Squares Regression Line (LSRL), and analyzing linear and multiple regression models, including prediction and confidence intervals.

Embark on this comprehensive journey through applied statistics and build the analytical skills you need to succeed in various fields requiring statistical expertise.