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Company X has recently introduced a new type of bidding, average bidding, as an alternative to the current type of bidding called maximum bidding. One of our clients, bombabomba.com, decided to test this new feature and wants to do an A/B test to see if average bidding converts more than maximum bidding.
The data relates to several user actions or interests recorded on two variants of landing pages for an online news portal. The objective is to analyse these interests by performing statistical analyses to determine if one variant is more effective based on chosen metrics (A/B testing).
This project explores Yulu bike rental data to analyze user demand, seasonal trends, and environmental influences on ride volume. Additionally, hypothesis testing was performed to statistically validate key assumptions regarding rental patterns.
A collection of simple parameter estimation and significance tests for the comparison of multivariate means and variation, covered in Chapters 4 and 5 of the book Multivariate Statistical Methods. A Primer. 5th edition.
This project explores Yulu bike rental data to analyze user demand, seasonal trends, and environmental influences on ride volume. Additionally, hypothesis testing was performed to statistically validate key assumptions regarding rental patterns.
Gain hands-on experience with ANOVA analysis, understanding its assumptions, and applying it to real-world datasets to understand differences among group means.
🚖🌦️ Desvendando os segredos das corridas de táxi em Chicago: explorando informações das corridas e a meteorologia, utilizando também dados estatísticos, gráficos e testes de hipóteses. Respondendo como o clima afeta a demanda? Existe correlação entre duração e condições meteorológicas? 📊
Exploratory Data Analysis is applied to analyse past data of video games that are sold during year 1980 to 2016. The purpose of the analysis it to find out which games, platforms, genres, and ESRB rating class that highly marketable in year 2017.
Gain hands-on experience with ANOVA analysis, understanding its assumptions, and applying it to real-world datasets to understand differences among group means.