From raw numbers to meaningful conclusions — we deliver precise, reproducible statistical analyses using industry-leading tools. Trusted by researchers, academics, and professionals across disciplines.
SPSS remains the gold standard for statistical analysis in social sciences, healthcare, and education. We handle everything from data entry to full output interpretation.
Mean, median, mode, standard deviation, frequency tables, and cross-tabulations.
Independent & paired T-tests, one-way & two-way ANOVA, chi-square tests.
Pearson/Spearman correlation, Cronbach's alpha, factor analysis.
Clean, labeled tables and charts formatted for thesis or journal submission.
Microsoft Excel is the most universally accessible analytics tool. We leverage its full power — from complex formulas to dynamic dashboards — to process and present your data professionally.
Remove duplicates, handle missing values, standardize formats, and build clean datasets.
VLOOKUP, INDEX-MATCH, IF/IFS, SUMIF, COUNTIF, array formulas, and named ranges.
Dynamic pivot reports, slicers, drill-down dashboards, and presentation-ready charts.
Automate repetitive data tasks, generate reports, and streamline workflows with VBA scripts.
Python's scientific ecosystem enables powerful, reproducible analysis at any scale. We write clean, documented scripts covering everything from exploratory analysis to advanced machine learning.
Distribution checks, outlier detection, correlation matrices, and profiling reports.
OLS regression, logistic regression, ANOVA equivalents via statsmodels and scipy.
Classification, clustering (K-Means, DBSCAN), dimensionality reduction (PCA, t-SNE).
Publication-quality plots with matplotlib, seaborn, and plotly for interactive charts.
Rigorous hypothesis testing is the backbone of credible research. We design, execute, and interpret the right statistical tests for your research questions and data structure.
Choosing the correct parametric or non-parametric test based on data type, distribution, and study design.
Z-test, T-test (one/two sample, paired), ANOVA, ANCOVA, MANOVA.
Mann-Whitney U, Wilcoxon, Kruskal-Wallis, Friedman, chi-square goodness-of-fit.
Cohen's d, eta-squared, power analysis, and sample size recommendations.
Regression analysis reveals relationships between variables and enables reliable predictions. We build, validate, and interpret regression models across linear, logistic, and advanced frameworks.
Coefficient interpretation, R², adjusted R², ANOVA table, and residual diagnostics.
Binary and multinomial logistic models, odds ratios, ROC curves, and classification reports.
Hierarchical regression, moderation/mediation analysis (PROCESS macro), polynomial regression.
Multicollinearity (VIF), heteroscedasticity, normality of residuals, Durbin-Watson.
Whether it's a single test or a full dissertation analysis, our team is ready to deliver accurate, publication-ready results.
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