Statistical Data Report Generator

Problem

When engineers have Excel or CSV data but need to decide between descriptive statistics, group comparisons, regression analysis, or composite evaluation, the workflow can become fragmented. data-statistical-tool covers the full path from data inspection to statistical execution and Word report delivery.

How It Works

• Data profiling: Reads .xlsx, .xls, and .csv files, then reports sample size, variable names, data types, missing values, and nominal versus quantitative variables.
• Method selection: Matches variables and research questions to methods such as independent t tests, one-way ANOVA, chi-square tests, Pearson correlation, linear regression, and Logit regression.
• Precondition checks: Runs normality, homogeneity of variance, and multicollinearity checks where required, then chooses parametric or nonparametric tests accordingly.
• Statistical execution: Uses pandas, scipy.stats, statsmodels, pingouin, and other Python libraries to compute results, with matplotlib charts embedded into the Word report.
• Interpretable output: Presents p values alongside effect-level interpretation, reliability coefficients, regression diagnostics, and report-ready conclusions.

Boundaries

• Best for structured analysis on tabular data, including surveys, descriptive statistics, group differences, correlation/regression, composite evaluation, and clustering.
• Missing values and outliers are reported, but removal should be confirmed by the user rather than assumed.
• Complex custom pipelines, unstructured text analytics, or production data platforms are outside the intended scope.