# Advanced Data Analysis in PostgreSQL: Statistical Properties Explored

An important part of analyzing any dataset is studying its statistical properties. The standard deviation of a data series and the correlation between different data series give valuable insights into the nature of the data, its interrelatedness, and the underlying information. Basic statistical analysis of this type does not require the use of specialized software packages. It can be done directly within PostgreSQL using built-in functions. This article shows how.

Running the analyses within the database has a few advantages:

- Fewer IT systems to manage and maintain
- Avoiding passing data back and forth between different systems
- Leveraging a mature RDBMS for enforcing data integrity and consistency

Note that this is the second part of the article on data analysis using PostgreSQL. The first part discusses how to study the preliminary properties of the data. It demonstrates SQL queries to get basic features like maximum and minimum values, sums and averages, percentile values, etc. for entire columns as well as for subsets (subgroups) of columns.

**Prerequisites**

**Background**

In addition to hands-on experience with the PostgreSQL database, it is helpful to have a working knowledge of basic statistical analysis. It is also assumed you are comfortable with the material discussed in the first part of this article.

**Systems**

The commands in this article are tested on PostgreSQL 14.5 and should work on all recent versions of PostgreSQL. If you choose to use the included Docker image, your computer needs to have Docker as well as a PostgreSQL client (such as Postico for the Mac or Beekeeper Studio as a cross-platform solution).

**Note: in the examples below, $ denotes the operating system prompt as a non-root user. Code snippets without a prompt are SQL statements.**

**Example Dataset**

To demonstrate basic statistical analysis, this article uses examples based on a dataset on cancer statistics. It is strongly recommended to try out the examples while reading through the article.

**Get the Data**

As discussed in the first part of the article, you have 3 ways of getting the sample data into PostgreSQL and preprocessing it.

**Option 1 - Database Dump File**

Choose this option if you are a regular PostgreSQL user who is comfortable (or wants to practice) importing database dumps.

Download the database dump file:

This is essentially a series of SQL commands to recreate the database. Move the dump file to a location the postgres user has access to.

Before importing the dump file, create a new database:

As the postgres user, import the dump into the newly created database:

Log in as the postgres user and connect to the new database:

After you are able to connect to the database, you can jump directly to the section “Data Description”.

**Option 2 - Preloaded Docker Image**

This is the fastest way for Docker users. Just run the Docker image and you are good to go.

Alternatively, you can use the Docker image with the data preloaded. If your computer does not have PostgreSQL installed, you can install just the client (to connect to the database running on Docker). On Debian/Ubuntu based systems, the PostgreSQL client can be installed as:

Pull the Docker image:

Check that the new image is now part of your system:

Run the new image:

Connect to the running database instance with username and password postgres:

After you are able to connect to the database, you can jump directly to the section “Data Description”.

**Option 3 - Manually Preprocess the Raw Data**

Choose this option to spend some time and gain hands-on experience working on a raw dataset and making it analysis-ready. This is a useful exercise if you are expecting to do analytical work yourself.

Follow the instructions in Part I. It starts from the raw CSV file and imports it into a table in a new database. Preprocessing is done on this table and the dataset for analysis is presented as a materialized view. This materialized view, mv_cancer_data, is used in all the examples in this article.

## Data Description

The materialized view, mv_cancer_data will be used in all further examples. After connecting to the cancer_db database, check the columns and data types in the materialized view:

Below is a description of the columns in mv_cancer_data.

- avg_annual_cases - Mean number of reported cases of cancer diagnosed annually in the county
- percapita_annual_cases - Number of reported cases in the county divided by county population
- avg_annual_deaths - Mean number of reported mortalities due to cancer in the county
- percapita_annual_deaths - Number of cancer deaths in the county divided by county population
- median_income - Median income per county
- population - Population of the county
- pc_poverty - Percent of county populace in poverty
- median_age - Median age of county residents
- county - County name
- state - State name
- pc_employed - Percent of county residents ages 16 and over who are employed
- pc_unemployed - Percent of county residents ages 16 and over who are unemployed

Note that percentages of unemployed and employed people do not add up to 100%. The difference is attributed to people who have quit looking for work, are not looking for work, are unable to work, or otherwise are not in the labor force.

Take a look at the form of the data:

**Statistical Properties of a Single Variable**

**Mean**

The mean of a sample is the average of all the data points in the sample. The avg function discussed earlier calculates the mean value of the population.

The average of per capita annual deaths across all counties is given by:

**Variance and Standard Deviation**

The variance of a sample is the average (over the sample size) of the square of the difference between each data point and the mean. Based on this definition, it follows that if the values are closer to the mean, the dataset has a low variance, and vice versa. Variance is said to measure the *dispersion* of the data. Variance is measured in squared units, which are not very intuitive.

A more commonly used metric of dispersion is standard deviation, which is computed as the square root of the variance. Because it is the square root of the variance, the unit of standard deviation is the same as the unit of the data itself.

The function var_samp returns the sample variance. The variance of per capita deaths across different counties is given by:

Sample standard deviation is computed using stddev_samp. The standard deviation of per capita annual deaths across counties is given by:

Note: the above queries use the sample variance and the sample standard deviation functions. The functions for population variance (var_pop) and population standard deviation (stddev_pop), work similarly. The distinction and relation between sample properties and population properties is beyond the scope of this article.

**Coefficient of Variation**

The coefficient of variation (CV) is a commonly used metric - it denotes the dispersion of the data relative to the mean. It is measured as the ratio of the standard deviation to the mean. The value of CV is computed explicitly (there is no builtin function for it):

**Outliers**

Outliers are data points whose values lie far from the rest of the distribution. The presence of outliers in the data can skew aggregate properties like the mean and variance. Excluding the outliers makes the data better behaved. A common method to identify outliers is based on the 3-sigma rule, also known as the 68-95-99.7 Empirical rule. Values outside 3 standard deviations of the mean can be considered outliers.

To get the number of counties whose **unemployment percentage** is within 3 standard deviations (3-sigma) of the mean:

Note that the distance of 3 sigmas is to be applied on both sides of the mean. Similarly, to get the number of counties whose **employment** percentage is within 3 standard deviations (3-sigma) of the mean:

For comparison, the total number of data points (counties) is:

Notice, based on the output of the above three queries, that the percentage of employed people in different counties (pc_employed) has more outliers than the percentage of unemployed people (pc_unemployed).

The presence of outliers skews statistical properties (like averages and correlations) in their direction. Whether to eliminate outliers from a dataset is a subjective decision. It is advisable to study the outlier values before deciding whether to remove them.

**Statistical Relations of Two Variables**

**Covariance and Correlation**

Covariance and correlation both measure the degree to which two variables jointly deviate from their respective means. For two groups of variables X and Y with n values each, the covariance is measured as:

The correlation (coefficient) is the covariance adjusted by the standard deviations.

Note that the correlation function is symmetrical, Correlation(X, Y) = Correlation(Y, X).

A high correlation between two variables indicates a strong statistical (not causal) relationship between the two variables. The direction and magnitude of the change in one variable strongly affect the direction and magnitude of the change in the other variable. Thus, one variable (the independent variable) can be used to predict unknown values of the other variable (the dependent variable) - this is shown in the next section (Regression).

To get the correlation coefficient between two variables, use the corr function. The following query computes the correlation between the number of annual cases (diagnoses) and the number of annual deaths per county:

Observe that this correlation is a high value. Heuristically, correlation values between 0.3 and 0.5 are considered low, values between 0.5 and 0.7 for the correlation coefficient indicate a moderate degree of correlation and moderate predictive power. Values between 0.7 and 0.9 are considered strongly correlated and values above 0.9 are considered very strongly correlated. Negative values of the correlation coefficient indicate that when one variable increases, the other decreases.

Note that a low (between 0.3 and 0.5) value of the correlation does not mean one variable *cannot* be used to predict the values of the other variable. It only means the prediction errors will be high. Indeed, in many social sciences disciplines, it is common to work with correlations in this range.

The covariance is computed using the covar_pop and covar_samp functions which have similar syntax as the corr function. Verify that the correlation (as obtained in the previous query) is indeed computed as the covariance adjusted by the standard deviation:

**Exercise**

Compute the correlation between median income and per capita annual deaths. Notice that median income is negatively correlated with per capita annual deaths. This indicates fewer people die of cancer in wealthier counties. This conclusion was also reached by studying the average values in an earlier section.

Try also to study the correlations between other pairs of variables you think might be related.

**Regression**

To be able to predict the values of a dependent variable (Y) based on the value of the independent variable (X), you need an expression that encodes the relation between them. This relation is called the regression equation and it is of the form:

In simple regression (Ordinary Least Squares - OLS), the slope is the correlation coefficient (of Y and X) scaled by the ratio of the standard deviations of Y and X:

The intercept is the expected value of Y if X is 0.

Assume, for the sake of illustration, that the dependent variable is the number of annual cases of cancer, avg_annual_deaths, and the independent variable is the number of cases, avg_annual_cases. The goal is to be able to express the number of deaths as a function of the number of cases:

The following query returns the value of the slope:

The Intercept is given by:

Thus, based on the output of the above two queries, the relationship is:

Note that any pair of variables with a significant correlation coefficient can be used to construct a regression equation. Deciding which variable is the dependent variable and which is the independent variable is subjective and dependent on the use case.

**Exercise**

Obtain the regression equation for two other pairs of variables which have a correlation coefficient > 0.3.

**Errors**

The regression equation predicts the value of Y for a given value of X. The difference between the predicted value of Y and the actual value of Y is the error. This error can be positive or negative. To find the error across all values of Y, take the average of the sum of the squares of the individual errors. This is called the Root Mean Square (RMS) error. For the earlier values of slope (0.33) and intercept (-16.7), it can be computed with the following query:

One way of reducing the error is to consider a modified dataset that excludes the outliers. Typically, outliers are eliminated from the columns that go into the regression equation.

Note that the higher the correlation coefficient between X and Y, the lower the error when predicting Y from X (or X from Y).

**Exercises**

- Compute the error based on the regression of two other variables. Verify that the regression equation based on highly correlated variables leads to lower errors than the regression of variables with low-moderate correlation.
- Construct a new dataset without outliers (make a new materialized view based on mv_cancer_data). Try to estimate the correlation, regression equations, and errors based on this dataset. Notice that the dataset without outliers has lower errors.

**Conclusion**

Knowledge of SQL and basic statistics is a valuable tool for the data analyst. It eliminates the need to rely on dedicated software packages to perform preliminary statistical analysis using PostgreSQL.

The built-in functions used in this article are listed under **Aggregate Functions** in the official PostgreSQL documentation.

**Learn More**

More advanced metrics such as *p-values* (used for confidence intervals), cannot be directly computed using built-in functions. It is possible to write SQL queries to do that. However, such queries tend to be complex, error-prone, and hard to debug. PostgreSQL also has no built-in functions for using the Logit and Probit models of regression. In cases where such functionality is needed, it is advisable to use PL/Python - the Python Procedural Language. This allows to run Python commands (and packages) inside a PostgreSQL database. PL/R - the R Procedural Language in PostgreSQL also has many primitives for basic and advanced statistical computations.