What is Descriptive Statistics?
Descriptive statistics is the foundation of any data analysis. Before drawing conclusions or building models, you need to understand the basic characteristics of your dataset — how values are distributed, what the typical values are, how much variation exists, and how variables relate to each other.
I provide a full statistical summary with written interpretations that are clear and actionable, even for non-technical audiences.
What's Included
- Central tendency: mean, median, mode
- Dispersion: variance, standard deviation, IQR, range
- Distribution shape: skewness and kurtosis
- Normality tests (Shapiro-Wilk, Kolmogorov-Smirnov)
- Correlation matrix (Pearson, Spearman)
- Frequency tables for categorical variables
- Percentile breakdown (25th, 50th, 75th, 90th)
- Written interpretation of all findings in plain language
Process
Data Review
I review your dataset and identify all numerical and categorical variables to be analyzed.
Statistical Analysis
I run the full statistical suite using Python (scipy, pandas) or Jamovi depending on your preferences.
Interpretation
I write a plain-language explanation of every finding so results are actionable for any audience.
Delivery
You receive a structured report (Excel or PDF) with all tables, charts, and the written summary.
Tools Used
Pricing
Pricing scales with the number of variables and dataset complexity.
- Mean, median, std
- Frequency tables
- Basic correlation
- Summary report
- All Basic metrics
- Normality tests
- Full correlation matrix
- Written interpretation
- All Standard features
- Subgroup analysis
- Hypothesis testing
- Executive summary
- All Advanced features
- Multi-dataset comparison
- Custom metrics
- Priority delivery