The first step in any kind of data analysis in R is to load the data, that is, to import a dataset into the R Environment. There are variety of data files you can import into R, which includes: Among all the files types used to store the data, perhaps the most widely used on …
When you wanted to import the text file in R, you must have used read.table() function. But do you know that you can pass on more than 20 different arguments to read.table() function? You must have used lm() function to come up with linear regression equation, but do you know that you can pass on …
Need HELP in R? A sneak peak into R’s Help ecosystem. Read More »
When the new data is presented to you for the analysis, you would like to get first hand information on the data set before diving deep into analysis. This is similar to doing a warm up exercises before engaging your body into high intensity workout. In this tutorial we will look at six different ways …
Learn 6 easy functions from base R to spot check your data set Read More »
When I speak to beginner data analysts, I could hear few miss-beliefs about data mining tools. I am writing this article to debunk some data mining fallacies. Here are the list of six Data Mining fallacies that I want to clarify: Fallacy 1: Data Mining tools are automated tools that can be deployed on the …
In my last article we learned how to estimate population mean by using sample mean when population standard deviation is known. In most instances, the population standard deviation will be unknown and thus business analyst won’t able to estimate the population mean. When the population standard deviation is unknown, the sample standard deviation must be …
What is t-distribution? – An intuitive understanding using business example Read More »
What is point estimation and what is its fundamental drawback? The use of single sample value such as X_bar (sample mean value) to estimate the population value is known as point estimation, because single value X_bar represent one point on the real number line. For e.g. if we are trying to calculate mean height of …
What is the difference between Point estimation and interval estimation? Read More »
In this article we will have high-level overview of the process of data science. We will look at different stages of data science work. The process of solving a data science problem is summarized in the following figure. This is called as Data Science Road Map. The first step is always to frame the problem: …
Many statistical tests assume that the data is normally distributed. Hence if the underlying data is not normal, we need to transform a data to make it near normal before we apply these tests. The transformed data set removes skewness, mitigate biases and enhance robustness of statistical models. In this tutorial, we will use different …
How to remove skewness in the data? Learn 6 powerful data transformations. Read More »
The basic idea of inferential statistics is to use a statistic (mean,Standard Deviation etc.) calculated on a sample in order to estimate a parameter of a population (mean,Standard Deviation etc.). However, we have to accept an unavoidable fact that value we get for any statistic will vary from sample to sample, even when all samples …
In data analysis, we may obtain greater insight while expressing a variable in different form. For e.g. you could use different scale to better visualize the variables that have points close by. Many statistical tests assume that the data is normally distributed. Hence if the underlying data is not normal, we need to transform a …
