Paolo Gironi - appunti di analisi dei dati,seo,statistica, retroinformatica

Statistical Parametric and Non-Parametric Tests

Statistical tests can be either parametric or non-parametric.

Parametric Tests: The Power of Normality

Parametric tests assume an approximately normal distribution.
They involve continuous or interval-type variables and require a sufficiently large sample size (typically > 30).
They also assume homogeneity of variances (homoscedasticity).

These tests have a higher statistical power because they provide a greater probability of correctly rejecting a false statistical hypothesis.

Multiple Regression Analysis, Explained Simply

The phenomena we observe and wish to study in order to deepen our understanding rarely present themselves so simply as to be defined by only two variables: one predictive (independent) and one response (dependent).

Therefore, while simple linear regression analysis holds fundamental theoretical importance, in practice it provides little more information than simply studying the correlation coefficient.

The Data: The 4 Scales of Measurement

The 4 scales of measurement. I understand the instinctive reaction: to skip an article that tastes like an unexciting introduction to a topic considered trivial.
However, I ask readers for an effort that I think is worth making. The concepts presented in this article are basic and precisely for this reason they have fundamental value and importance.
Assimilating these concepts means building a solid foundation for the topics that will follow.

Put concisely, but resolutely: we take nothing for granted, because nothing is taken for granted.

Correlation and Regression Analysis – Linear Regression

In previous posts, we have examined concepts such as the mean and standard deviation, which are capable of describing a single variable. These statistics are of great importance; however, in daily practice, it is often necessary to investigate the relationships between two or more variables. This is where new key concepts emerge: correlation and regression analysis.

Correlation and regression analysis are tools widely used during the analysis of our datasets.
They involve estimating the relationship between a dependent variable and one or more independent variables.

Time Series Analysis and Forecasting in R

What is meant by a time series?

A time series consists of values observed over a set of sequentially ordered periods. This, for those who do SEO, is already an element of utmost interest.

Website traffic data, considered over a time sequence, is in fact an example of a time series.

Time series analysis is a set of methods that allow us to derive significant patterns or statistics from data with temporal information.

In very general terms, we can say that a time series is a sequence of random variables indexed in time.

The purpose of analyzing a time series can be descriptive (consider decomposing the series to remove seasonality elements or to highlight underlying trends) or inferential, with the latter including forecasting values for future time periods that have not yet occurred.