---
title: "Data analysis"
chunk: 2/5
source: "https://en.wikipedia.org/wiki/Data_analysis"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:43:31.459045+00:00"
instance: "kb-cron"
---

Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality.  In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error).
Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X?  This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X.

=== Data product ===
A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy.

=== Communication ===

Once data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis.
When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience.  Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data. Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained in the data.

== Quantitative messages ==

Stephen Few described eight types of quantitative messages that users may attempt to communicate from a set of data, including the associated graphs.

Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.
Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by salespersons (the category, with each salesperson a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the salespersons.
Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%).  A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.
Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period.  A bar chart can show the comparison of the actual versus the reference amount.
Frequency distribution: Shows the number of observations of a particular variable for a given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A histogram, a type of bar chart, may be used for this analysis.
Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.
Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.
Geographic or geo-spatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is typically used.

== Analyzing quantitative data in finance ==

Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. These include:

Check raw data for anomalies prior to performing an analysis;
Re-perform important calculations, such as verifying columns of data that are formula-driven;
Confirm main totals are the sum of subtotals;
Check relationships between numbers that should be related in a predictable way, such as ratios over time;
Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year;
Break problems into component parts by analyzing factors that led to the results, such as DuPont analysis of return on equity.
For the variables under examination, analysts typically obtain descriptive statistics, such as the mean (average), median, and standard deviation. They may also analyze the distribution of the key variables to see how the individual values cluster around the mean.