Regression Analysis Help

We can help you with all different types of Regression Analysis projects that you may need help with, either if it is a Simple Regression, Multiple Regression, Hierarchical Regression, Logistic Regression, or a Stepwise Regression

Regression Analysis is one of the most prominent types of statistical analysis that is used to try to construct a quantitative model to relate one dependent variable (DV) with one or various independent variables (IV's)

Regression analysis is one topic that can easily be misinterpreted. Most statistical software packages come with facilities that make regression analysis very easy to conduct. Though, in order for the regression results to be valid or meaningful, certain regression assumptions need to be met.

Being able to identify whether or not the assumptions are met requires expertise, and it matters because if the assumptions required for regression are not met, it is likely that the results from the regression model are rather useless, with little predictive power.

It requires a good amount of expertise to conduct regression analysis effectively and correctly. It is critical to know which type of model to use, how to deal with problems like outliers or the existence of leverage, etc. We can help you with that!

What does a regression analysis tell you?

Regression analysis can help you unveil useful associations between variables of interest, from an academic point of view, or from the point of view of a business need.

Typically, when conducted correctly, regression will yield a quantitative model that dials in the relationship between one dependent variables and one or more independent variables.

But then work with regression analysis does not end there, you need to interpret the model and assess its validity, for which the help of an expert statistics consultant can prove to be vital.

What is regression analysis used for example?

In an academic context, for example, a regression analysis could be used to assess the relationship between employee turnover and employee satisfaction, along with other covariates from a personality inventory.

In a business context, a linear regression analysis could be used to assess the relationship between wages and workshop productivity.

Different models can be used depending on the type of data used. For regular interval data for the dependent variable, the ordinary least squares methodology is applicable. And for binary dependent variables, we would use a logistic regression.

What is the main purpose of regression?

The main purpose of regression is to assess the association between variables of interest. The importance of regression is that you can estimate one variable in terms of others.

The ultimate goal of regression analysis is to create a model that helps you to estimate or predict one variable in terms of one or more variables.

The key is that the model is valid, in terms of adhering to the assumption of the theoretical linear regression model.

What are the benefits you get by using Regression Analysis Help

You can save a lot of guesswork working your regression analysis by working with us:

• Expertise - Guidance from a skilled statistician well-versed in regression techniques like linear, logistic, multiple, and nonlinear regression
• Correct Methodology - Experts ensure you use the right type of regression for your goals and avoid faulty approaches
• Error Checking - Experts look for issues like multicollinearity, overfitting, residual analysis that lead to inaccurate predictions
• Insight - Statisticians interpret outputs and help translate results into meaningful, actionable insights for decision making