April Fools day 2020 saw the hive mind of social media asking what the sample size should be to measure the extent of the Coronavirus in the UK. I could see that many people responding were reaching for standard methodologies which are usually are based on specifying a desired confidence interval. In doing so, they were overlooking a much more effective and relevant alternative based on the methodology of Acceptance Sampling, first developed by the US Military in World War 2.
On June 5th 2019, I had the privilege of being able to talk to the Treasury Select Committee about the “Effectiveness of Gender Pay Gap Reporting“. My name was put forward by the Royal Statistical Society and we spent an hour discussing a number of issues with a particular focus on the Finance sector.
Mention P-values and most people will probably shudder at some memory of an incomprehensible lecture or lesson on statistical tests. Words like null hypotheses, t-tests, statistical significance might pop into your mind with little understanding of what they are about. What you may know is that scientists have to report a p-value for any experiment they do or do they?
The area of Statistical Inference is a core area of study for any statistician. Put simply, Inference means to infer from the observations you’ve made about your data and to draw conclusions about what might be happening in real life. There are two parts to Inference.
All organisations want to understand what has happened in the past and what will happen in the future. The use of statistics and statistical thinking is essential to be a better forecaster but that doesn’t mean it is easy to do! At the same time, we are bombarded with forecasts in the media and that can make it difficult to decide which forecasts to pay attention to and which can be ignored.
My course “Identifying Trends & Making Forecasts” is all about doing the basics right when it comes to analysing trends and making predictions. To support this course, this post makes available a variety of material in the public domain covering the following themes:-
If I were to remark to you that “the weather is very nice today” or “I didn’t like that person”, it is unlikely that I would have made such statements based on a single variable. It is more likely that a combination of variables were evaluated to arrive at these statements. When we analysis datasets with multiple variables, we are undertaking Multivariate Statistical Analysis.
Multivariate Analysis comes in two flavours :-
- Analysis of Correlations between Multiple Variables – Known as R-Analysis – Informally known as reducing the dimensionality of your dataset.
- Analysis of Distance between Many Objects – Known as Q-Analysis – Informally known as mapping, clustering or segmentation of your dataset.