Predictive regression modeling can be defined as the process of forecasting future events using historical data. This technique analyzes past trends and patterns to predict values for future occurrences.
To come up with the right predictive model the data analysts have to choose the appropriate modeling technique. Selecting the wrong technique will not only produce erroneous predictions but also result in inaccurate residual plots that give rise to non-constant mean and/or variance.
The significance of predictive regression modeling
As stated above, there are different types of predictive modeling methods. However, regression modeling has grown in popularity these days because it helps data analysts predict the relationships between multiple variables. Regression modeling indicates the relationship between an independent variable and a dependent variable. It also determines the impact of two or more independent variables on one dependent variable. Additionally, regression models enable comparison of the effects of variables on different events, for instance, the effect of promotional activities on the customer’s decision to buy a product or service. This helps data analysts, market researchers, and data scientists to determine the most effective variables for developing predictive models. For more information on the significance of predictive regression modeling, collaborate with our predictive regression modeling online tutors.
Types of predictive regression techniques
There are several regression techniques and methods used to make predictions today. Each of these techniques is driven by three major metrics:
Number of independent variables
The shape of the regression line
Type of dependent variable
Below mentioned are the most commonly used regression techniques: -
Linear regression: This is one of the most popular modeling techniques. In linear regression, the dependent variable is continuous and the nature of the regression line is linear. Independent variables in this technique can be either continuous or discrete. Linear regression creates a link between the dependent variable (Y) and multiple independent variables (X) using a regression line usually referred to as the ‘best fit straight line’. There are two major categories of linear regression - simple linear regression and multiple linear regression. Simple linear regression differs from multiple linear regression in that the former has less than one independent variable while the latter has more than one independent variable.
Logistic regression: The logistic regression technique is used to determine the probability of the success or failure of an event. It is used when the dependent variable is binary (yes/no, true/false, or 1/2). This type of regression does not require a linear relationship between independent and dependent variables.
Polynomial regression: A regression equation is said to be polynomial if the independent variable has a power that is more than one, usually denoted as y=a+b*x^2. In polynomial regression, the best fit is not drawn as a straight line. Rather it is usually represented in a curve that connects, or rather, fits into the given data points. When creating a prediction model using polynomial regression it is important to check the curve towards the end to see if the trends and shapes make sense. Sometimes polynomials can produce weird results especially if they are extrapolated.
Stepwise regression: This method is used when there is more than one independent variable in a data set. In stepwise regression, independent variables are selected automatically without human intervention. The technique fits the predictive model by adding or removing one covariate at a time using a specified criterion. The most commonly used stepwise regression techniques are:
Standard stepwise regression
Ridge regression: The ridge regression technique is used when the data being observed suffers from multicollinearity. In other words, it is used where independent variables are significantly correlated. In multicollinearity, the values observed usually deviates largely from the true value. Ridge regression adds some degree of bias to the values reducing the standard errors.
Lasso regression: Short for Least Absolute Shrinkage & Selection Operator, Lasso regression is used to reduce the variability of linear regression models and improve their accuracy.
ElasticNet regression: This is a combination of ridge regression and Lasso regression techniques. It is utilized when there are groups of features that are correlated. ElasticNet regression enhances group effect in data sets that have highly correlated variables.
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