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  1. Comparing groups is often one of the main goals in data visualizations. The ggplot2 package in R, along with its powerful extensions, makes it easy to create visualizations that highlight differences, trends, and relationships between groups.

    Click this link for detailed information: statisticsglobe.com/online-cou

    #businessanalyst #datavisualization #statistics #package #ggplot2 #rstats

  2. New 📚 Release! Day by day as an IT BA: A Business Analyst Journey for Everyone by Mikhail Bakhrakh #books #ebooks #businessanalyst #career #management

    Find it on Leanpub!

    Link: leanpub.com/babook1

  3. When performing multiple imputation of missing data, it is essential to evaluate how the imputed values compare to the observed data.

    The attached image, created with the bwplot() function, showcases how the distributions of observed and imputed values vary across different imputations for multiple variables.

    Detailed information: eepurl.com/gH6myT

    #rstats #bigdata #businessanalyst #datavisualization

  4. Listwise deletion, also known as complete case analysis, is one of the simplest methods for handling missing data.

    The attached image illustrates the challenges of listwise deletion when the missing data is not random.

    Tutorial: statisticsglobe.com/listwise-d

    More: eepurl.com/gH6myT

    #businessanalyst #database #rprogramminglanguage #dataanalytics

  5. Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

    For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: statisticsglobe.com/mean-imput

    More details are available at this link: eepurl.com/gH6myT

    #research #datastructure #businessanalyst #data

  6. Statistical inference is a powerful tool in data analysis that helps us make conclusions about a population based on a sample.

    The visualization of this post shows the distribution of sample data, highlighting the sample mean with a red dashed line and illustrating the 95% confidence interval with a blue error bar.

    Click this link for detailed information: statisticsglobe.com/online-cou

    #rstats #database #statistics #data #businessanalyst

  7. Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

    Image: en.wikipedia.org/wiki/Local_re

    More details: eepurl.com/gH6myT

    #database #package #bigdata #businessanalyst #tidyverse #datavisualization #rprogramming

  8. I love #physics, I love #businessanalysis - two on the surface completely different disciplines, but in the heart they are very similar.

    Physics is a science which is "...s a systematic discipline that builds and organises knowledge in the form of testable hypotheses and predictions about the universe" (Wikipedia).

    And what is so different to business analysis (maybe execept the part about the universe)?

    #iiba #businessanalyst #requirements

  9. How could it look like when a #BusinessAnalyst 🧑‍💻 meets #AI 🤖? Definitely not like this. 😆 But you can find out at our next event in January 👉 linkedin.com/events/7264524020.

    "AI for Better Requirements - A New Era for Business Analysts" - Three different short presentations on how #AI has shaped their way of work in a fantastic location and wrapped up with discussions & networking afterwards.

    IIBA IREB #iiba #requirementsengineering #software

  10. Because it is #Friday13th I will use my black rubber duck for #rubberducking 🦆

    Even as a #BusinessAnalyst it is a good approach to get out of the rabbit hole and think in different perspectives. Rubber ducking is such a valuable technique for business analysts, developers, and problem-solvers alike.

  11. When building regression models, watch out for significant predictors! 🚨 Sometimes, variables that seem important might lose their significance when the model gets better.

    See this link for additional information: statisticsglobe.com/webinar-da

    #businessanalyst #dataanalytic #visualanalytics #dataviz #data #bigdata