Hire an Expert for Statistical Modeling

Statistical modeling is the application of statistical techniques and mathematical models to simplify the representation of a phenomenon using numbers to understand reality better and make predictions. A model explains one or multiple dependent variables using several independent variables through mathematical equations that solve parameters. If you are searching for help with creating a statistical model, our statistical modeling experts offer customized solutions to identify relationships between variables, make predictions, and visualize findings for an accurate presentation of complicated data models.

Creating a statistical model means discovering a mathematical proportionality between various dependent variables and multiple independent variables. It demonstrates the underlying process by which the data was generated, reveals the data structure, provides evidence of statistical inferences, and allows the evaluation of the assumptions that support interpreting analytic findings.

Additionally, the models support future scientific inquiries and the effective distribution of research results. This article illustrates the types and techniques of statistical modeling, the software used for modeling, the procedure of building a statistical model, an example, and a comparison between statistical modeling and machine learning.

What is Statistical Modeling?

Statistical modeling is applying statistical techniques to study the underlying relationship between the independent and dependent variables and generate predictive output. It is an important analytic tool as it enables researchers to identify complex inter-relationships between variables and make judgments about the separate effects of each.

There are three main types of statistical modeling: Parametric, non-parametric, and semi-parametric. A parameter is a fixed measure describing the whole population. Parametric modeling has known set parameters from the beginning of the study; in non-parametric the parameters are flexible and can change anytime, and semi-parametric is a combination of both parameters.

Statistical Modeling Techniques

(1). Linear Regression

Linear regression is a technique that establishes the relationship between the response variable and the predictor variable using a linear equation. It is normally used to predict continuous outcomes. When extended to deal with multiple predictor variables, it is referred to as multiple regression.

(2). Logistic Regression

Logistic regression is a statistical modeling technique used to predict the probability of an outcome with two classes. It is normally used when the response variable is binary(where the results can only take one of two values). Logistic regression models the relationship between the predictor variables and the log of the odds of the response variable.

Statistical Modeling Software Experts

Multiple statistical packages are available to fit statistical models. Our statisticians have in-depth knowledge of  S-Plus, R, SAS, Stata, and the Statistical Package of the Social Sciences (SPSS) software and apply their capabilities to deliver accurate and reliable results. Each software is different in the terminology it adopts, its facilities for handling data, and in other ways.

Hire the Best Statistical Modeling Experts From Our Company

Our statistical modeling services have professionals with advanced degrees in the field of statistics and have years of experience doing statistical modeling projects in different fields of research. They apply this knowledge and expertise to generate sample data, make predictions, and compose visualizations to present the results of the procedure. For further guidance, this section describes the process of building a statistical model, gives an example, and compares statistical modeling and machine learning.

How to Build a Statistical Model

Step 1: Clarify the Research Question

The first step involves being clear about the purpose of the research and defining the hypothesis to be explored and evaluated. The research question should emerge from a theoretical development or a response to a policy issue. It should have context, and previous studies can be used to formulate the research design to generate appropriate data to come up with the study inquiry.

Step 2: Data Collection

Data collection methodologies typically fall into two major categories: Primary sources that involve first-hand retrieval of information through methods such as surveys and secondary sources that use pre-existing information that had been previously collected by other researchers. The data needs to be coded and the variables derived. Finally, it undergoes initial preliminary statistical analysis to clean the data and identify potential relationships.

Step 3: Specify the Model

In statistical modeling, specifying the model involves making an initial assumption of how variables might be related and included in the model. Additionally, specifying the model is done by choosing the form of the model to be adopted based on the type of response variable. To identify which variables are to be considered, the researcher tests the hypothesis and evaluates previous research.

Step 4: Fit and Estimate the Model

Model fitting is mapping the chosen model onto the data to estimate the value of the coefficients or parameters of the model. Two main procedures are used for this step: least squares and maximum likelihood. Least squares have four variants: Generalized, weighted, ordinary, and two-stage least squares.

Step 5: Assess the Adequacy of the Model

The adequacy of the fitted model is evaluated based on three key factors. 1.) The extent of variation in the response variable the model explains. 2.) How it compares with other alternative models 3.) The effectiveness of the model in explaining the variation in the independent variable. To approve these factors, the statistician conducts significance tests and global measures of the model.

Step 6: Draw Conclusions

The sixth step is to draw conclusions from the model and assess how much it answers the research question highlighted in Step 1. The model should not only pass all statistical tests but also solve a real-world problem.

Statistical Modeling Example

A statistician conducted a study to forecast the seasonal climate using seasonal mean temperature, accumulated precipitation, and sea surface temperatures as predictors to improve agricultural planning. They employed a linear regression model to analyze the relationship between the predictors and the climate variables, and validated the model using a retroactive procedure where they trained the model for the first few years and then made a prediction immediately after the training period. They discovered that the sea surface temperatures had a positive impact on the mean temperature and composed a framework for enhancing climate predictions to contribute to better agricultural planning.

Statistical Modeling Vs. Machine Learning

Machine learning is the use of computer systems that learn and adapt without following specific instructions through algorithms and models that analyze and reveal hidden patterns in data.

Similarities

• Variables are generally of two types: Independent in ML are referred to as targets, and dependent are called features.
• In statistical modeling and machine learning, loss and risk are described in terms of the mean squared error.
• Both processes begin with the assumption that data and observations from the past can be used to predict the future.
• The mean squared error is the difference between the predicted and the actual value and measures the loss of the prediction performance in both procedures.

Differences

The table below shows the key differences between machine learning and statistical modeling.

Summary

Statistical modeling is the application of statistical techniques to simplify the representation of a phenomenon using numbers to understand reality better and make predictions. It can be categorized into three key areas: Parametric, non-parametric, and semi-parametric. Common techniques for conducting statistical modeling are linear and logistic regression time series and survival analysis.

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