Free online courseStatistics Course for Beginners

Course Description

Welcome to the "Statistics Course for Beginners." Perfectly designed for those new to statistics, this comprehensive course offers a duration of 9 hours and 48 minutes to thoroughly cover fundamental statistical concepts.

In the introduction, you'll delve into an overview of the course, encapsulating the essentials of what you will learn and exploring why this course is hailed as one of the best introductory statistics courses available.

The journey begins with a series of video tutorials that provide a broad yet detailed look at the statistics curriculum. Terminologies and definitions will be explained, ensuring you are familiar with the essential language of statistics.

One of the most critical aspects of any study is the design. This section covers various study designs, including cross-sectional, case-control, and cohort studies, giving you insight into how to structure your statistical inquiries.

Understanding variables and their types is crucial in any statistical analysis. You'll learn to distinguish between different types of variables and the applications of bar charts, pie charts, and frequency tables in representing data.

For those interested in visual data representation, the course explores histograms and density plots for numeric data, as well as boxplots and plots for two variables, providing the skills needed to graphically summarize data effectively.

Distributions are a cornerstone of statistical analysis. You'll learn to describe distributions in terms of center and spread and gain insights into key metrics like mean, median, mode, percentiles, quantiles, and quartiles.

Key components like standard deviation, understanding the difference between sample and population, and the normal distribution are covered extensively. You'll also explore the concept of Z-scores and the relevance of the Central Limit Theorem.

Confidence intervals and hypothesis testing form the backbone of inferential statistics. This course offers thorough explanations of these concepts, alongside practical examples to heighten your understanding of the material.

Deep dive into specialized topics such as the t-distribution, margin of error, bootstrapping, and resampling techniques. You’ll learn to compare confidence intervals and hypothesis tests to draw more precise conclusions from your data.

When it comes to hypothesis testing, you'll understand one-sided versus two-sided alternatives and gain insights into errors and power in hypothesis testing and the intricacies of power calculations.

Move beyond single-variable analysis with bivariate analysis, exploring its meaning and practical applications through categorical data and various tests such as the paired t-test and Wilcoxon signed rank test.

For those comparing groups, the two-sample t-test is discussed, including considerations of equal versus unequal variance assumptions and bootstrap methods for hypothesis testing and creating confidence intervals.

ANOVA or Analysis of Variance offers a robust method for assessing differences among group means. This section covers all necessary details, from introduction to the computation of F-statistic and p-values, and includes the Bonferroni correction for multiple comparisons.

Broaden your statistical toolkit with tests such as the Chi-square test for independence and measures like odds ratios, relative risks, and risk differences, specifically in the context of case-control studies.

Conclude your journey with a focus on simple linear regression, underst.