What are the best practices for statistical modeling and inference in programming?
Statistical modeling and inference are essential skills for programmers who want to analyze data, make predictions, and test hypotheses. However, there are many pitfalls and challenges that can affect the quality and validity of your results. In this article, you will learn some of the best practices for statistical modeling and inference in programming, such as choosing the right tools, methods, and assumptions, validating and interpreting your models, and communicating your findings effectively.
Depending on your data, your research question, and your programming language, you will need to select the appropriate tools for statistical modeling and inference. These tools include libraries, packages, frameworks, and APIs that provide functions, classes, and methods for data manipulation, analysis, visualization, and reporting. Some of the most popular and powerful tools for statistical modeling and inference in programming are R, Python, MATLAB, SAS, SPSS, and Stata. You should familiarize yourself with the features, advantages, and limitations of each tool, and choose the one that best suits your needs and preferences.
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TBH there is a key element missing! Make yourself familiar with databases and data storages. Thereby you can mix and match different tools. For instance one could use Python and Geopandas to analyse geospatial data, store the data in a PostgreSQL database and then do the statistical modelling with R.
Once you have chosen the right tools, you will need to apply the right methods for statistical modeling and inference. These methods include techniques, algorithms, and procedures that help you create, fit, evaluate, and compare statistical models that represent your data and your hypotheses. Some of the most common and useful methods for statistical modeling and inference in programming are linear and logistic regression, ANOVA and ANCOVA, t-tests and chi-square tests, correlation and causation analysis, cluster analysis and factor analysis, and machine learning and deep learning. You should understand the logic, assumptions, and requirements of each method, and apply the one that best matches your data and your research question.
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In the context of Big Data, applying the right statistical modeling methods is crucial for extracting valuable insights. Tools like Spark facilitate complex data processing, allowing for scalable machine learning algorithms to be applied directly on big datasets. When working within Azure Databricks, one can leverage built-in libraries for regression, clustering, and more, using languages like Python and Scala. It's important to ensure the chosen methods align with the data's nature and the computational resources available, as well as the specific requirements of the Hadoop ecosystem tools like Hive and HBase for data storage and management.
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This is a strange list. My recommendation is the following: plot -> describe -> test for standard distributions & correlate -> model Every time you find something strange, go back, alter the data and repeat. Especially in the real world where data is not as nice and clean like in the Kaggle challenges, this will save you a lot of headaches.
Before you run your statistical models and draw your inferences, you will need to check your assumptions. These assumptions are conditions, rules, and criteria that your data and your models must satisfy in order to produce valid and reliable results. Some of the most important and common assumptions for statistical modeling and inference in programming are normality, homoscedasticity, independence, linearity, multicollinearity, outliers, and missing values. You should use various tools and methods to test, verify, and correct your assumptions, such as histograms, boxplots, scatterplots, Q-Q plots, Shapiro-Wilk test, Levene's test, Durbin-Watson test, VIF, Cook's distance, and imputation.
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Assumptions are like the hidden rules of your statistical model in programming. They're the conditions under which your model works best and gives accurate results. Using a model with violated assumptions can lead to misleading results, throwing off your analysis for a toss and eventually leading to bad decisions.
After you run your statistical models and draw your inferences, you will need to validate and interpret your models. These steps involve assessing the quality, accuracy, and significance of your models and your inferences, and explaining what they mean in the context of your data and your research question. Some of the most relevant and helpful measures and indicators for validating and interpreting your models and your inferences are R-squared, adjusted R-squared, p-values, confidence intervals, effect sizes, coefficients, odds ratios, ROC curves, AUC, precision, recall, and F1-score. You should use these measures and indicators to evaluate your models and your inferences, and to report your findings in a clear and concise way.
The final step of statistical modeling and inference in programming is to communicate your findings. This step involves presenting and sharing your results, conclusions, and recommendations with your audience, whether it is your colleagues, clients, or the public. Some of the most effective and engaging ways to communicate your findings are graphs, charts, tables, dashboards, reports, slides, blogs, and podcasts. You should use these methods to visualize, summarize, and highlight your findings, and to tell a compelling and convincing story that answers your research question and supports your hypotheses.
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