Predictive Regression Modelling

Predictive Regression Modelling

To understand predictive regression modeling, you must first be well-versed with predictive modeling. The process of taking down known results and developing a model that can predict values for new occurrences is called predictive modeling. It is a process that uses historical data to predict future events.

There are a plethora of predictive modeling techniques including linear regression, ANOVA, logistic regression, decision trees, neural networks, and many more. You must choose the correct predictive modeling technique. This will not only save you time but also guarantee accurate predictions.

Regression Modeling

Regression analysis can be used to predict a continuous target variable from one or multiple independent variables. It is typically used with naturally occurring variables instead of variables that have been manipulated through experimentation. Regression equations can be used to make predictions and are a crucial part of the statistical output after you fit a model. The relationship between each independent variable and the dependent variable is defined by the coefficients in the equation.

If we use the regression approach to make predictions, it doesn’t necessarily mean predicting the future. Instead, we are predicting the mean of the dependent variable(s). As we stated above, there are a variety of regression models. We have discussed some of them below: Remember, you can seek our help with predictive regression modeling homework if you are facing any assignment hurdle with the models below.

• ANOVA

Analysis of Variance or simply ANOVA is used when the target variable is continuous and the dependent variables are categorical. In this type of analysis, the null hypothesis is that there is no significant difference between the different groups

Analyses performed in ANOVA must take into account the following assumptions:

• The population should be normally distributed
• All sample cases should be independent of each other
• The variance should be approximately equal among the groups

• Linear Regression

This type of regression analysis is used when:

• The target variable is continuous and the dependent variable(s) is continuous or a mixture of continuous and categorical
• The relationship between the dependent and independent variable is linear
• All the predictor variables should be normally distributed with constant variance. They should demonstrate little to no multicollinearity nor autocorrelation with one another.

• Logistic Regression

It doesn’t require any linear relationship between the target and the dependent variable(s). A target variable is dichotomous or binary and assumes a value of either 0 or 1. The residuals or errors of a logistic regression does not have to be distributed normally. Also, they do not need to be constant. On the other hand, dependent variables are binary, observations must be independent of each other, the data must have no multicollinearity nor autocorrelation, and lastly, the sample size should be large.

Logistic regression does not require independent and dependent variables to be linearly related. However, the independent variable must be linearly related to the log odds.

• Ridge Regression

This is a technique used to analyze multiple regression variables that experience multicollinearity. It takes the ordinary least squares approach. Ridge regression also ensures that residuals experience high variances by adding a degree of bias to the regression estimates in order to reduce the standard errors. The assumptions made in ridge regression include:

• The scatter plots should be linear
• There must be constant variance with no outliers
• The dependent variable must exhibit independence 

• Time Series Regression

Time series regression predicts future responses based on history. The data for this analysis should be a set of observations on the values that a variable takes at different instances. The series should also be normally distributed or stationary. This means that the mean and variance of the series are constant over a long period. Besides, a normal distribution should be exhibited by the residuals. A time series should not have any outliers Random shocks (if present) should be randomly distributed with a mean of 0 and a constant variance.

How can we use regression to make a good prediction?

1. Conduct in-depth research on the subject area so you can build on the work of others. The research will help you with subsequent steps.
2. Gather relevant data for the variables
3. Specify and evaluate your regression model
4. Use the model that adequately fits your data

Our predictive regression modeling tutors can help you with assignments in this area.