Advanced Statistical Modeling: Techniques for Analyzing and Predicting Trends and Patterns
Over time, data has become a critical organizational asset. Companies use advanced statistical modeling techniques to identify, analyze, and predict trends and patterns. This helps them generate value from data and make informed, data-driven decisions. Various advanced statistical modeling techniques can be adapted to numerous industries, applications, and data.
Understanding different advanced statistical modeling techniques and how to utilize them can help you explore your data and discover trends and patterns in your market. These statistical modeling methods go beyond the basic descriptive statistics – they delve into more sophisticated methodologies, helping you unearth complex relationships in your data. Using these techniques enables you to make accurate descriptions. But what are some of the top advanced statistical modeling techniques? Let’s find out.
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What is Statistical Modeling?
As the name suggests, statistical modeling involves the application of statistical analysis to data sets. It is a data science process that helps companies execute experiments on their data to discover relationships between multiple components and generate business insights.
A statistical model is defined as a mathematical association between one or several random variables and various non-random variables. Applying advanced statistical modeling to raw data enables data scientists to approach data analysis from a strategic perspective. As a result, they can offer intuitive visualizations that help in identifying connections between variables and making forecasts.Â
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Top 6 Advanced Statistical Modeling Techniques in 2023
Here are the top 10 advanced statistical modeling techniques for analyzing and predicting trends and patterns in 2023 and beyond:
- Regression Analysis
Regression analysis statistical model explores the relationship between a dependent variable and one or several independent variables. In this case, the dependent variable is the data you are looking to measure. On the other hand, the independent variable is data used to forecast the dependent variable.
The regression statistical analysis technique is used to quantify the impact of independent variables on the dependent variables. Also, it is used for hypothesis testing and prediction.
The line utilized in regression analysis charts and graphs reveals whether the relationships between independent and dependent variables are weak or strong. Also, it shows trends over a given amount of time.
- Factor Analysis
The factor analysis statistical modeling technique identifies common factors that explain the covariance structure of variables under observation. Sometimes, the variables you are interested in are the ones that can’t be directly measured or observed easily. And that’s where factor analysis comes in handy.
For instance, there is no direct measure for usability or a usability thermometer. However, you need to measure usability to determine areas of improvement or make appropriate adjustments to your products. In this case, you must rely on the results of bad and good experiences to assess usability.
The factor analysis technique takes multiple observed correlated variables and minimizes them to a few hidden (latent) variables known as factors. There are several ways to determine the number of factors in your data. Also, different researchers evaluating the same data can unearth a varying number of factors.
Generally, you’ll need a large sample size to perform factor analysis. And like most advanced statistical modeling techniques, the relationship between the identified variables should be linear.
- Cluster Analysis
As identified in data mining techniques, cluster analysis involves grouping (clustering) related items together. The clusters or groupings are not directly measurable. Instead, they are inferred from the data. This technique identifies natural patterns and is often used for classification or segmentation purposes.
For instance, the cluster analysis approach can be used in card sorting, where you need to determine how closely functions, content, and products relate from the end user’s perspective. Also, this technique is used to create personas and segment customers.
When performing client or customer segmentation, several variables are considered, including psychographics (e.g., the probability to use and new product or service) and demographic (age, income, etc.). Doing so helps you identify what determines a segment.
- Time Series Analysis
Time series advanced statistical models are used to analyze data gathered over specified time intervals. It accounts for the temporal ordering of observations and seeks to understand and model the underlying trends, patterns, and seasonality within your data.
This advanced statistical analysis technique includes various methods, such as:
- State space models
- Exponential smoothing models, and
- Autoregressive integrated moving average models.
The time series advanced statistical modeling technology can help you achieve various goals, including the following:
- Predicting future values based on historical data.
- Determining the impact of external factors or interventions on the time series.
- Detecting outliers or anomalies in the time series.
- Discovering and decomposing components such as seasonality and trends.
- Survival Analysis
The survival analysis technique models time-to-event data, such as customer churn or failure rates. The main goal of this advanced statistical modeling method is to estimate the survival probabilities and hazard rates and to discover factors affecting event or survival occurrence.
Businesses across different sectors are at constant risk from various factors, such as technology changes and shifts in customer demands. So, survival analysis is heavily used in different sectors, like marketing, healthcare, and insurance, to assess data related to customer retention, disease progression, and product lifetimes.
This technique can account for censoring, which happens when some events aren’t observed or occur after the study period. It can integrate time-varying covariates, enabling the examination of time-dependent effects.
- Multilevel Modeling
Companies often have hierarchical data, such as patients in hospitals and customers in businesses. In this case, multilevel modeling is an ideal advanced statistical modeling technique that can help analyze such data.
The multilevel models can account for the dependence among observations and estimate the individual- and group-level effects. So, if you have data with a nested structure, multilevel modeling can help you analyze it and predict patterns and trends, gaining crucial insights from your data.
Final Thoughts
Applying advanced statistical modeling techniques can help you analyze and predict trends and patterns. This way, you can gain in-depth insights into your business, enabling you to make informed, data-driven decisions. As a result, it helps you gain a competitive edge.
However, advanced statistical modeling techniques can be complex. And that’s where Indium Software comes in handy. With over 33 years of experience in data analytics, you can rest assured that our experts will get the best out of your data using different advanced statistical modeling techniques, powering your business to digital success. Contact us for more information.Â