Statistical Consulting Services

In the research field, all projects are unique. Here are some of the most common project types that we work on. These project types are not unique to us; generally, these would be the core services a consulting company would offer.

This can include setting up servers, creating a data pipeline, creating SQL databases, building websites to capture data, and even organizing data in Excel using VBA. Data management is a fundamental part of statistical consulting. Properly managed data is needed to build models.

For market research, MaxDiff and Conjoint analysis are two of the main tools used in market research. These survey types measure the most valued parts of a product or service. Both survey types use logistic regression in the analysis, which is standard in our report builder; depending on the project needed, a custom model might need to be built.

Regression analysis estimates the relationships between a dependent variable and one or more independent variables. There are two main types of regression: linear regression and logistic regression.

Linear regression is used when the dependent variable is a number, often called a continuous number—for example, trying to predict if gender affects Vitamin levels in the blood. Vitamin A levels are a number that theoretically has infinite values.

Logistic regression is used when the dependent variable is categorical. Categorial refers to a named level, such as like/dislike. This could be used to predict whether the store location affects a product's liking.

Regression analysis is used in financial forecasting and healthcare and is the fundamental concept in machine learning.

You use a Chi-square test to test the significant difference between two categorical (or qualitative) variables. This test examines the differences between variables from a random sample to judge the fit between expected and observed results.

Survey data often use Chi-square tests because the data is categorical, for example, testing the proportion of females compared to males that approve of a particular product.

When you want to test the statistical significance between two or more quantitative variables, you use a Student's t-test or an ANOVA test. The Student's t-test is used when you have two groups, while the ANOVA test is used when you have three or more groups.

When you record data at specific time intervals, the data would be called a time series. When you want to analyze that data, it is referred to as time series analysis. One of the most powerful features of time series analysis is forecasting. For example, predicting what day of the month will have the highest sales volume.

Grouping similar categorical data can help identify hidden patterns. This process is called latent class analysis. A great example of this would be in a MaxDiff study. You might want to find the size of groups who prefer product A over B and groups who prefer product B over C.

A decision tree is like a flowchart that maps the possible outcomes of related choices. Decision trees are also referred to as probability trees. While decision trees can be e drawn out by hand for use in presentations or to document algorithms, they are usually programmed into a system to generate an expected result from a given input.

Predictive analytics refers to using data to predict outcomes. The building blocks of predictive analytics include regression, time series analysis, and decision trees.