This statistics video introduces the different measures of spread/variability for a numeric variable. In this video, we introduce the Interquartile Range, Variance, Standard Deviation, and other measures of "spread" for a numeric variable. One measure is not necessarily 'better' than the other, they simply try to convey similar information in slightly different ways, each with their respective pros and cons. These measures each focus on certain features while ignoring others. How do you define which country did the "best" in the Olympics? Is it the country that won the most gold medals? or the most medals in total? or do you give weight to gold, silver, and bronze, and use the totality of the weights to determine the "best" performing country? Similarly, here, how do we define the "spread" of a distribution or "variation/dispersion". These different measures are simply different ways to estimate variability, each with their own pros and cons. You should focus on the concept of what they are, and NOT the calculation (as you will likely never calculate these by hand). We do present the formulas for them in the video, but this is for the sake of understanding the underlying concept of what they are trying to measure.
In this statistics tutorial, we learn how to use paired t-test (dependent sample t-test) to compare means of 2 matched, paired, or dependent groups. We also cover building a confidence interval for the mean difference, as well as how this can be used to test a hypothesis. The paired t-test is used to compare the means of 2 matched, paired, or dependent groups. It essentially becomes the univariate (one sample) t-test, by taking the difference in observations in the 2 groups and then conducting a test on the mean difference. The paired t-test is also known as paired two-sample t-test, paired-sample t-test or dependent sample t-test.
This statistics video tutorial presents the idea of a histogram as well as a density plot. A histogram is a common plot to see, which helps describe the distribution of a single numeric variable. A (Kernel) Density plot may be less commonly known by name, but is a useful companion to a histogram; you may have seen the density plot without knowing their name. These plots are very useful in helping us to visualize and describe the distribution of a numeric (quantitative/continuous) variable. Here we will talk about what these plots display and why they are useful in statistics and in research.
This statistics video shows how to summarize a categorical variable. In this tutorial we discuss the idea of a frequency table, a proportion, a percentage, and bar charts and pie charts. This video will help to put all these ideas together in the context of summarizing a single categorical (qualitative) variables or factors.
Puppet Master of Statistics Series is a collection of 3 videos that introduces and explores the concepts of hypothesis testing and p value, sampling distribution, and confidence intervals and their use in statistical inference. You can learn these introductory yet very important statistical concepts with fun examples and visual tools in these absolutely free video tutorials.
📝 Find the web visualization Tools Here
The Bivariate Analysis in R (Series 4 of R Statistical Software Tutorials) will walk you through conducting one sample and two sample t-tests and Mann Whitney U (Wilcoxon Rank-Sum test) in R, Bootstrapping with R and Permutation hypothesis test in R. Here, we will also learn to preform Paired t test, Wilcoxon Signed Rank test, analysis of variance (ANOVA), Chi-Square test and Cross Tabulations, Relative Risks and Odds Ratio, Correlations and Covariance and Simple Linear Regression using R programming Language.
These R video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio.
In this R video tutorial, we show how to do a permutation hypothesis test in R, using an example where we compare a numeric (quantitative, continuous) variable for two groups formed by a categorical variable (qualitative, factor). A permutation test in statistics is considered an "exact" test, as it calculates an exact p value (although in practice, we often take a random sample of all possible permutations, and so it is no longer truly an exact test).
📝 Find R Script and R Dataset for Permutation Hypothesis Test Here
In this statistics video tutorial we present the general concept of a Permutation Test. Permutation tests in statistics also get referred to as “Exact Hypothesis Tests”, and serve as an alternative approach to large-sample parametric approaches.
In this R video, we will learn to generate a confidence interval using a bootstrap approach, Step by Step with no R Packages. Bootstrapping in statistics is a resampling based approach useful for estimating the sampling distribution and standard error of an estimate. An R packages do exist for bootstrapping in R (package name: boot), although the package is limited in the sorts of estimates/statistics it can conduct a bootstrap approach for.
📝 Find R Script and R Dataset for Bootstrapping Confidence Interval Here
