A/B testing is one of the best ways to optimise your website. However, it is not a magical cure for websites that are:
- Poorly Conceived – Website is off-brand or there is a major disconnect with users.
- Poorly Designed – Website is designed in a way that is hard to maintain/confusing.
- Poorly Developed – Website has a lot of code bloat or inefficient code.
- Using Sub-Par/Inflexible Technology – Website is created with technology that allows for very little optimisation or growth.
In this post I am going to go through the code for a relatively simple R Shiny app that will predict Google Ads transactions and cpa (cost per acquisition) for three months plus the current month. There will be a graph and a table of values and four sliders to adjust spend for the current month and the coming three months. You’ll have to experiment with the app to get it to run the way you would like. Continue reading
If we want to understand if two variables are correlated, the first thing we have to understand is if they covary. That means that when one variable moves from its mean, we would expect that a related variables would change in a similar way. Continue reading
A one-way ANOVA is used to test a null hypothesis by comparing three or more sample groups from a population (a t-test is generally used if comparing two sample groups). To use this method, we take a random, equal sample from each group. Then we examine the mean and variance between samples.
With this post I am going to briefly walk through some things about data you many not have thought about. I think that this might give you a different and useful perspective on the nature of data. I’ll start first with the various types of data and data elements. Then I’ll get into some data issues and finish up with data classification and standardization.
Types of Data Generally, data falls into one of three types: