'regression' 검색 결과

Improve code through repeated changes, checks, and decisions to keep or discard each result. Start with a goal or measurable target, limit iteration counts, and review logs for progress, stalled improvements, or regressions.

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• Improve code against a measurable target
• Investigate and fix recurring errors
• Check whether changes introduce regressions

Organize Nexent test assets into a traceable chain from requirements and bugs to cases, scripts, and results. Review existing coverage, maintain execution settings, and generate a registry and Excel views for formal testing.

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• Designing test cases for new requirements
• Reviewing regression coverage after a bug fix
• Adding missing tests after development

Assess whether tests detect the wrong behavior relevant to a specific change, PR, or commit. Link findings to exact revisions, compare faulty and correct behavior when execution is authorized, and distinguish observed results from predictions.

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• Check whether a regression test detects the original bug
• Assess tests for a new feature
• Review behavior preservation during a refactor

Diagnose why an existing skill fails evaluations or shows no clear improvement. Separate content problems from test data, execution failures, activation issues, and insufficient evaluation tasks, then apply targeted fixes and validate them.

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• Investigating a regression after a skill change
• Diagnosing evaluations that return no results
• Fixing a skill that fails to activate

Review completed code through three roles: a critical reviewer, a verifier, and an adjudicator. Check evidence, look for bugs introduced by fixes, and produce an actionable repair list with regression tests and traceable review reports.

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• Reviewing code before a release
• Checking major refactors for regressions
• Verifying that reported fixes actually work

Fit regression and time series models in Python, then examine coefficients, uncertainty, and predictions. Check model assumptions, investigate unusual observations, and compare alternatives using detailed statistical summaries and diagnostic tests.

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• Interpret regression coefficients and confidence intervals
• Model binary outcomes or event counts
• Forecast time series with uncertainty intervals

Choose and implement methods for estimating causal effects from observational data or policy changes. Guidance covers method assumptions, estimation, diagnostic checks, and robustness tests that examine whether findings hold under alternative analysis choices.

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• Choosing a causal method for evaluating a policy change
• Analyzing policies introduced at different times across regions
• Checking instrumental variable strength and underlying assumptions

Write, debug, and understand Stata code for data cleaning, statistical analysis, causal inference, and charts. The reference covers command syntax, common pitfalls, Mata programming, and community packages for estimation and reporting.

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• Write and debug Stata .do files
• Clean and merge analysis datasets
• Implement regression and causal inference methods

Generate a Stata .do file that compares every subset of optional control variables across parallel instances. The script ranks combinations by the focal variable’s absolute t-statistic, reports the top ten, and fits the highest-ranked model.

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• Comparing all subsets of optional control variables
• Keeping mandatory controls in every regression
• Preparing parallel regression runs in Stata

Choose and interpret models for panel data, which tracks the same firms, people, or countries over time. Use Python, R, or Stata examples to compare fixed and random effects, check assumptions, and select standard errors.

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• Choosing between fixed and random effects
• Estimating entity and time effects together
• Selecting standard errors for correlated observations

Create summary tables, treatment and control comparisons, and correlation matrices using Python, R, or Stata. Summarize averages, variation, sample sizes, and missing data to understand a dataset before regression or prepare tables for a research paper.

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• Explore a dataset before regression
• Prepare a paper's summary statistics table
• Compare treatment and control groups

Organize Python and Stata research code using shared conventions for folders, filenames, data checks, logs, and outputs. Templates guide data cleaning, merging, regression analysis, and figure or table creation, with documentation for reproducing results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up folders and script order for a research project
• Standardizing Python data cleaning and merging scripts
• Preparing Stata regressions and output tables

Evaluate policy effects with difference-in-differences, a method comparing changes in treated and untreated groups. Guides assumption checks, regression models, staggered adoption analysis, robustness checks, and reporting with event study plots.

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• Evaluating policy effects with comparison groups
• Checking outcome trends before treatment
• Analyzing policies adopted at different times

Improve R code used in empirical economics by finding slow operations, reducing memory use, and optimizing data processing and regressions. It covers large panel datasets and repeated calculations, with checks that optimized code preserves the original results.

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• Optimizing imports and summaries for large survey datasets
• Speeding up fixed-effects regressions on panel data
• Parallelizing Bootstrap and Monte Carlo calculations

Formats regression results as LaTeX tables for economics papers, with standard errors, significance stars, fixed-effect labels, and statistical notes. Provides templates and guidance for converting Stata, Python, or R output into main-text and appendix tables.

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• Converting Stata, Python, or R results into LaTeX tables
• Comparing baseline and fixed-effects regression models
• Preparing tables for instrumental-variable estimates

Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.

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• Build a reproducible R or Quarto analysis workflow
• Compare regression models with progressively added controls
• Test robustness across samples and statistical methods

Build causal inference analyses to estimate intervention effects using templates drawn from Causal Inference: The Mixtape. Adapt code across Python, R, and Stata, with checks for method assumptions and guidance on language-specific packages and limitations.

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• Implement a difference-in-differences analysis
• Set up an event study and check parallel trends
• Build instrumental variable or discontinuity models

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.

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• Compare performance before and after v3 optimization
• Evaluate vector search speed and memory use
• Track performance regressions over time

Apply the project's rules for commit messages, branch names, and bug fixes. The skill also points agents to bundled documentation and requires regression tests, which check that a fixed bug does not return.

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• Write commit messages that follow project conventions
• Choose a branch name for a development task
• Include a regression test with a bug fix

Guide development in Apache Tika with project rules for Maven builds, module tests, code conventions, and checks before committing. Covers safe file handling, regression tests, and build pitfalls that can hide failures.

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• Preparing an Apache Tika development session
• Building and testing selected modules
• Checking parser changes and regression tests

Compare Tika character encoding detection using one build and two configurations. Find files whose detected encoding changed, cross-check text quality signals, and inspect detector traces to investigate garbled text and identify which detector contributed to the result.

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• Investigating encoding changes after a detector configuration update
• Finding files misclassified from Western encodings to CJK
• Tracing which detector selected a file's encoding

Compare text and metadata extracted by two Tika builds from the same file collection. Generate reports to investigate content differences, encoding changes, new exceptions, and missing embedded documents, then repeat the evaluation after fixes.

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• Comparing extracted content before and after a Tika change
• Checking whether parser changes lose attachments
• Investigating new exceptions and large content differences

Estimate study sample sizes and statistical power, the chance of detecting a specified effect. Calculate detectable effects for a fixed sample, compare assumptions with power curves, and document adjustments for dropout, multiple comparisons, or clustered observations.

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• Estimate participant or sample counts before data collection
• Find the smallest effect detectable with a fixed sample
• Compare sample requirements across plausible effect sizes