Over the years, I’ve been writing a series of posts that I hope can serve as an introduction to the main “foundational” topics in the field of descriptive statistics, inferential statistics, and time series analysis. I’m grouping them here so they can form a path — a way to embark on a journey that I hope will be stimulating.<\/p>\n\n\n\n
Before diving in:<\/em> A Brief (Personal) Manifesto for SEO<\/a> — a reflection on why statistical rigour matters in SEO work.<\/p>\n\n\n\n 1. The Data: The 4 Scales of Measurement<\/a><\/strong> 2. Descriptive Statistics: Measures of Position and Central Tendency<\/a><\/strong> 3. Descriptive Statistics: Measures of Variability (or Dispersion)<\/a><\/strong> 4. First Steps into the World of Probability: Sample Space, Events, Permutations, and Combinations<\/a><\/strong> 5. Probability Distributions: Discrete Distributions and the Binomial<\/a><\/strong> 6. The Beta Distribution Explained Simply<\/a><\/strong> 7. The Geometric Distribution<\/a><\/strong> 8. The Hypergeometric Distribution<\/a><\/strong> 9. The Negative Binomial Distribution (or Pascal Distribution)<\/a><\/strong> 10. The Poisson Distribution<\/a><\/strong> 11. The Normal Distribution<\/a><\/strong> 12. The Central Limit Theorem: Why Statistics Works (Even When Data Isn’t Normal)<\/a><\/strong> 13. Hypothesis Testing: A Step-by-Step Guide<\/a><\/strong> 14. The t Distribution and Hypothesis Testing<\/a><\/strong> 15. The Two-Sample t-Test: How to Test a Hypothesis for Dependent or Independent Samples<\/a><\/strong> 16. Confidence Intervals: What They Are, How to Calculate Them (and What They Do NOT Mean)<\/a><\/strong> 17. Guide to Statistical Tests for A\/B Analysis<\/a><\/strong> 18. Bayesian Statistics: How to Learn from Data, One Step at a Time<\/a><\/strong> 19. Anomaly Detection: How to Identify Outliers in Your Data<\/a><\/strong> 20. Contingency Tables and Conditional Probability<\/a><\/strong> 21. The Chi-Square Test: Goodness of Fit and Test of Independence<\/a><\/strong> 22. Statistical Parametric and Non-Parametric Tests<\/a><\/strong> 23. Analysis of Variance, ANOVA. Explained Simply<\/a><\/strong> 24. The Gini Index: What It Is, Why It Matters, and How to Compute It in R<\/a><\/strong> 25. Correlation and Regression Analysis – Linear Regression<\/a><\/strong> 26. Multiple Regression Analysis, Explained Simply<\/a><\/strong> 27. Logistic Regression: Predicting the Outcome of an Event<\/a><\/strong> 28. Time Series Analysis and Forecasting in R<\/a><\/strong> 29. Multicollinearity, Heteroscedasticity, Autocorrelation: Three Difficult-Sounding Concepts (Explained Simply)<\/a><\/strong> 30. Understanding the Basics of Machine Learning: A Beginner’s Guide<\/a><\/strong> Non-Parametric Tests: The Wilcoxon Test for Non-Normal Data<\/a><\/strong> The Monte Carlo Method Explained Simply with Real-World Applications<\/a><\/strong> How to Use Decision Trees to Classify Data<\/a><\/strong> The Gradient Descent Algorithm Explained Simply<\/a><\/strong> A\/B Test Sample Size Calculator<\/a><\/strong> This blog is listed on R-bloggers.com<\/a>, an aggregator of R tutorials and news from the R community.<\/p>\n","protected":false},"excerpt":{"rendered":" Over the years, I’ve been writing a series of posts that I hope can serve as an introduction to the main “foundational” topics in the field of descriptive statistics, inferential statistics, and time series analysis. I’m grouping them here so they can form a path — a way to embark on a journey that I … Leggi tutto “Statistics and SEO”<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-3452","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"post-thumbnail":false},"uagb_author_info":{"display_name":"autore-articoli","author_link":"https:\/\/www.gironi.it\/blog\/author\/autore-articoli\/"},"uagb_comment_info":0,"uagb_excerpt":"Over the years, I’ve been writing a series of posts that I hope can serve as an introduction to the main “foundational” topics in the field of descriptive statistics, inferential statistics, and time series analysis. I’m grouping them here so they can form a path — a way to embark on a journey that I…","_links":{"self":[{"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/pages\/3452","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/comments?post=3452"}],"version-history":[{"count":4,"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/pages\/3452\/revisions"}],"predecessor-version":[{"id":3501,"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/pages\/3452\/revisions\/3501"}],"wp:attachment":[{"href":"https:\/\/www.gironi.it\/blog\/wp-json\/wp\/v2\/media?parent=3452"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Statistics and SEO: The Topics<\/h2>\n\n\n\n
Quantitative and qualitative data | The 4 levels of measurement | Nominal scale | Ordinal scale | Interval scale | Ratio scale | Complexity levels of measurement types<\/p>\n\n\n\n
Measures of central tendency | Arithmetic mean | Weighted mean | Geometric mean | Harmonic mean | Trimmed mean | Median | Mode | Relationship between mean, median and mode | Quartiles, deciles and percentiles | The five-number summary | Box-plot<\/p>\n\n\n\n
Range | Mean deviation | Variance | Standard deviation | Coefficient of variation | Shape of a distribution | Kurtosis<\/p>\n\n\n\n
Probability | Additivity principle for incompatible events | Multiplication principle | Permutations | Combinations | The binomial distribution as an application<\/p>\n\n\n\n
Discrete and continuous variables | Bernoulli random variable | Binomial distribution | Binomial coefficient | Mean, expected value, variance | The hypergeometric distribution<\/p>\n\n\n\n
An important probability distribution in Bayesian statistics | A practical example using R<\/p>\n\n\n\n
How many attempts until the first success? | Examples | Computing in R<\/p>\n\n\n\n
Starting from the formula | The hypergeometric distribution explained with examples | The urn and balls example | Further reading<\/p>\n\n\n\n
Definition | Usage examples | Differences between the geometric and Pascal distributions<\/p>\n\n\n\n
Lambda: the average rate of events | Poisson vs Binomial | Practical examples | SEO applications<\/p>\n\n\n\n
Visualizing the “normality” of data | Transforming data | The empirical rule | Standardization | Examples | Chebyshev’s inequality<\/p>\n\n\n\n
What is the CLT | Why it matters | Simulation in R | The practical rule: how large should n be? | Standard error | Daily organic traffic example | When the CLT is not enough<\/p>\n\n\n\n
Statistical hypotheses | Type I and II errors | One-tailed or two-tailed? | Setting null and alternative hypotheses | Significance level | Choosing the distribution | Drawing conclusions | Power of a test | Determining sample size<\/p>\n\n\n\n
A brief historical digression | Example | The p-value approach | Confidence intervals | The t-test with R<\/p>\n\n\n\n
Independent samples hypothesis test | Paired t-test for dependent samples | Example<\/p>\n\n\n\n
What is a CI | The 95% misconception | CI for means | CI for proportions | CI vs hypothesis testing | Confidence levels: 90%, 95%, 99% | What affects CI width | Practical example: organic CTR<\/p>\n\n\n\n
Z test | Student’s t-test | Welch’s t-test | Chi-square test | Analysis of Variance (ANOVA) | Mann-Whitney U test | Fisher’s exact test | Regression analysis | Comparative overview table<\/p>\n\n\n\n
Frequentist vs Bayesian | Bayes’ theorem: derivation and components (prior, likelihood, posterior, evidence) | Numerical example in R: ad campaign click rate | Sequential updating | Informative and non-informative priors | Credible interval vs confidence interval | When to use the Bayesian approach<\/p>\n\n\n\n
Why recognizing anomalies matters | Working dataset: simulated sessions with injected anomalies | Method 1: z-score and the empirical rule | Method 2: IQR and Tukey’s method | Method 3: Grubbs’ test and iterative approach | Comparing the three methods on web traffic data<\/p>\n\n\n\n
Two-way tables and marginal distributions | Conditional probability | Dependence and independence<\/p>\n\n\n\n
Goodness of Fit test | Understanding through examples | Using R | The Independence test<\/p>\n\n\n\n
Parametric tests: the power of normality | Non-parametric tests: versatility and creativity<\/p>\n\n\n\n
ANOVA: a parametric test | Why ANOVA instead of multiple t-tests? | One-way ANOVA | The ANOVA table | Using R<\/p>\n\n\n\n
The Lorenz curve | Example | The concentration index R | Computing in R | The Gini index worldwide<\/p>\n\n\n\n
Simple Regression | Pearson’s R | R-squared | Spearman’s rank correlation | Regression equation | Outliers and leverage points | Model assumptions | Residual analysis | Other correlation coefficients<\/p>\n\n\n\n
The multiple regression equation | What information can I extract? | Prerequisites | Getting started | How good is my model? | Summary<\/p>\n\n\n\n
How logistic regression works | Example in R: Titanic survival probability | The logit equation | Summary | Resources<\/p>\n\n\n\n
What is a time series | Classical analysis and decomposition | The four classic components | Creating time series in R | Smoothing techniques | SEO applications | Moving averages | LOESS decomposition | Holt-Winters exponential smoothing | ARIMA models<\/p>\n\n\n\n
Multicollinearity | How to reduce the problem | Heteroscedasticity | Autocorrelation<\/p>\n\n\n\n
Introduction | What is ML | Supervised and unsupervised ML | Main stages | How to get started | Jupyter Lab and Google Colab<\/p>\n\n\n\nAdditional Topics<\/h2>\n\n\n\n
Wilcoxon signed-rank test | Wilcoxon rank-sum test | Practical examples with R<\/p>\n\n\n\n
What is Monte Carlo simulation | Random sampling | Practical applications | R examples<\/p>\n\n\n\n
Decision tree algorithm | Classification and regression trees | Practical examples<\/p>\n\n\n\n
How gradient descent works | Learning rate | Practical implementation<\/p>\n\n\nInteractive Tools<\/h2>\n\n\n
Calculate the required sample size for your A\/B test in real time. Enter baseline conversion rate, minimum detectable effect, significance level and power to get the exact number of users per variant.<\/p>\n\n\n
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