In this article, I will write about the basics of hypothesis testing, its key parts, and its underlying assumptions. This article will include theoretical knowledge only. If you are interested in practical knowledge (using R programming), please visit my blog to see the continuation of this article.
Tag: python
The importance of skewness and kurtosis in statistical analysis — examples in R Studio
When performing a statistical analysis, understanding the shape of data is important for good interpretation and further decision making. If you fail to identify potential outliers in a dataset, all models that might come out of it might be wrong and non usable, leading to wrong results. In this article, I’m tackling the topic of skewness and kurtosis and why is it important in statistical analysis.
Choosing the right graph for your data visualization project — examples in R
In this article, we will explore different types of graphs in R and provide suggestions for when to use each of them.
Exploring data variability: analyzing with boxplots in R
In this article, we will explore variability, and how we can approach it from box plot perspective.
Understanding Outliers in Data Analysis: Insights from R
Normal distribution and normality are a rare event in data analytics. It happened almost never that you have a perfect mound, Gaussian distribution of a variable in a dataset. When that occurs, we are talking about outliers. Outliers are data points in a dataset that deviate from the rest of the data. In this article, we will discuss why does it happen and how to deal with it, in order to apply methods and techniques which have a normal distribution as a prerequisite.
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