---
esco_uri: http://data.europa.eu/esco/skill/7ee4c2ea-b349-4bd2-81a3-ec31475d4833
esco_label: "statistics"
relation: essential
onet_soc: 13-2011.00
source: stackexchange
confidence: high
qa_count: 5
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# statistics — Accountant

For an Accountant, 'statistics' isn’t about running complex regressions for academic papers – it's about turning raw financial data into *actionable insights*. Daily tasks involve analyzing trends in revenue and expenses (time series analysis), forecasting future performance based on historical data (using techniques like moving averages or simple regression), and identifying anomalies that might indicate fraud or errors. Think budgeting, variance analysis, cost accounting, and audit procedures – all heavily reliant on understanding distributions, central tendencies, and deviations from expected values. Tools used are primarily spreadsheet software (Excel is king!), but increasingly include data visualization tools (Power BI, Tableau) and statistical packages for more complex analyses.

## Praxiswissen (Stack Exchange, CC-BY-SA 4.0)

**The user is building a regression model and needs appropriate methods to assess correlations between categorical variables (both two-way and one categorical with one continuous), as well as alternatives to VIF for multi-level categorical predictors.**

When examining relationships between categorical variables, the Chi-Squared test of independence determines if they are associated. A significant p-value suggests a correlation, which can be quantified using Cramér's V. For relating a categorical variable to a continuous one, ANOVA is appropriate; it compares variance *within* groups defined by the categorical variable to variance *between* those groups to detect dependence. These tests assess association rather than linear correlation like Pearson’s coefficient.

*Source: [How to get correlation between two categorical variable and a categorical variab](https://datascience.stackexchange.com/q/893) — answer by Alexey Grigorev, CC-BY-SA 4.0*

**The asker is learning linear regression and questions why cost functions use squared errors instead of simply summing the absolute differences between predicted and actual values.**

Using squared error (or other even powers) in a cost function addresses a fundamental problem: minimizing raw error can lead to unstable or boundary-condition solutions. Squaring ensures the cost is always positive, creating a well-behaved optimization landscape. Furthermore, this choice aligns with statistical assumptions about noise – specifically that errors are often normally distributed due to the Central Limit Theorem – allowing for more robust model fitting when perfect accuracy isn't achievable.

*Source: [Why do cost functions use the square error?](https://datascience.stackexchange.com/q/10188) — answer by Harsh, CC-BY-SA 4.0*

**The user, new to TensorFlow and neural networks, struggles with selecting the appropriate cost function for different machine learning problems beyond simple cases like XOR or regression, finding tutorials lack explanation of *why* certain functions are chosen.**

Choosing a cost function is crucial for achieving desired results; start with Mean Squared Error for regression and error percentage for classification as defaults. However, optimal performance requires understanding your data, goals, and available tools to define a truly 'good' cost function. Be aware that even if two functions measure the same thing, their computational properties (like smoothness) can significantly impact training effectiveness – prioritize smooth functions for gradient-based optimization.

*Source: [Neural networks: which cost function to use?](https://datascience.stackexchange.com/q/9850) — answer by Winks, CC-BY-SA 4.0*

**The questioner, a data scientist with an R background and limited computer science fundamentals, is evaluating the practicality of learning C or C++ to improve performance and expand capabilities beyond what R offers.**

Rather than fully transitioning to C/C++, leverage those languages strategically by building extensions for existing tools like R or Python. Focus on optimizing only the most computationally intensive parts of your work in C/C++ while retaining the higher-level analytical strengths of languages designed for data science. This hybrid approach balances performance gains with development efficiency and access to a broader ecosystem of statistical and machine learning libraries.

*Source: [Data Science in C (or C++)](https://datascience.stackexchange.com/q/5357) — answer by Andre Holzner, CC-BY-SA 4.0*

**The user is questioning how Keras' `validation_split` parameter works, specifically if it always selects validation data from the end of the dataset without shuffling and whether a random selection is possible.**

Using a fixed validation split within Keras can be problematic if your data isn’t randomly ordered, potentially leading to biased evaluation. It's best practice to create separate training and validation sets *before* model training to avoid overfitting and ensure consistent evaluation. For more control over the split – especially for class imbalances – leverage tools like `train_test_split` from scikit-learn to achieve a truly random and representative division of your data.

*Source: [How does the validation_split parameter of Keras' fit function work?](https://datascience.stackexchange.com/q/38955) — answer by JahKnows, CC-BY-SA 4.0*

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
