Predictive regression modeling.
Forecasting is an important element in today’s business. It’s an important decision-making tool. However, it doesn’t give an accurate prediction of what will happen. Experts refer to forecasts as an educated guess. Sometimes the deviation from the actual could be significant.
Forecasting can be classified depending on the data used for analysis. Normally, there are two kinds of data, qualitative and quantitative. In qualitative data, experts make the prediction of the market or business using their knowledge. Such a method of prediction is only suited for a short period of time. Quantitative forecasting employs the use of mathematical techniques for prediction. Data is integral to the success of this method. It eliminates human emotions and is preferred most by many organizations. This article concentrates on this kind of analysis.
If we are to make a prediction using quantitative methods, we must develop a model. Regression models are often the solution to the model that we need. But when we talk of regression, there are various regression models that we can use. What are these models?
1. Linear regression.
Most people are probably familiar with this form of regression analysis. Linear regression is perfectly suited for a dataset that is continuous i.e., both the dependent and the independent variables should be continuous. It can also be used where either of the dependent or independent variables is categorical. Before using linear regression, there are several assumptions that must hold. These are: the normality of the datasets, the data should have a constant variance, and have no multicollinearity.
2. Analysis of variance.
Analysis of variance is a regression technique used to test whether there is a significant difference between groups. Note that we are using the term groups. This indicates the usage of categorical data in its analysis. Here the dependent variable is always categorical while the independent variable is continuous. Its assumptions are, the dataset is normally distributed, has an equal variance, and the points are independent.
3. Logistic regression.
Logistic regression is used to test the probability of success or failure for a given dataset. It’s normally used where the dependent variable is categorical and the independent variable is continuous. The dependent variable should be binary i.e., yes/no, male/female, and true/false. It does not assume that there is linearity in the dataset.
A recap of the data analysis steps
In data analysis, there is a predefined systematic way of handling a particular problem. First, it starts with defining the problem. A common example is, suppose, a company experienced losses in the past financial year. They want to know the root course of the problem. Such a problem should be measurable. Once we know what we are researching about, we can go on and collect the data needed for analysis. Today, finding the right data is not a problem.
The third step is to prepare the data for analysis. Here errors such as missing values or typographical errors are removed. Then we proceed to the data analysis stage, which can be divided into two, exploratory data analysis and fitting of the model. The final step of the data analysis process is the predictive stage.
The accuracy of the model in prediction
Well, the goal of any prediction analysis is to ensure that the difference between the Predictions and the observed values are small. How will you know that your model produces accurate results? One result that often arrives in the regression analysis table is the r-squared. What does it mean and how can someone interpret it
R-squared is sometimes known as the coefficient of determination. It’s a measure of how close the data is to the regression line. It ranges from 0 to 1. As an analyst, you would prefer a value that is closer to one. A value closer to one implies that the regression line best fits the data, while a lower value implies that it does not fit the data.
The r-squared is not the only measure you can use to adjudge the accuracy of your model. You can use the mean squared error or the adjusted r-squared.
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