# Why Regression analysis important

Regression analysis is a statistical technique used to model the relationships between different variables (dependent and independent).

On the one hand, it is used to describe and analyze relationships in data. On the other hand, regression analysis can also be used to make predictions.

The relationships in the data are used as the basis for forecasts.

Regression analysis is counted among the multivariate analysis methods and is used in many different fields:

Science, statistics, finance and now also in online marketing, for example the cost and sales of products, campaigns, channels and advertising materials, analysis and forecast.

### Summary

• Origin of the regression analysis
• Importance in Online Marketing
• How it works
• types of regression analysis
• Regression Analysis Process
• benefits of regression analysis
• personal tests

### Origins of regression analysis

Mathematical tools involving regression have already been used to determine the orbits of planets using data from astronomical observations.

The least squares method, published by Carl Friedrich Gauss in 1809, is considered a precursor to regression analysis.

The tools were further developed and initially used in biology and geology. Regression methods are still an area of research that encompasses many different scientists.

### The importance of online marketing

Regression analysis is used in online marketing, for example, to understand customer journeys using web analytics data or to support multichannel marketing with reliable data.

In practice, such analyzes are complex and require specialist knowledge. But depending on the model, the results can be very clear and tangible:

If, for example, the attribution model is used to look at multiple channels such as direct, exposure, affiliates, social media, emails or referrals, the regression analysis can clearly show which of these channels invests and there is a good balance between sales.

At an enterprise level and with specialized partners who can conduct such analysis, the results should be extremely useful and can significantly increase the ROI of individual digital assets.

To make the results of regression analysis tangible, so-called “marketing decision support systems” are usually developed.

They allow insights from the regression function to flow directly into marketing decisions. This includes decisions like increasing your advertising budget, restructuring your ads, or adopting a product category.

### Work capacity

A regression is based on the idea that a dependent variable is determined by one or more independent variables.

Assuming there is a causal relationship between two variables, the value of the independent variable affects the value of the dependent variable.

On this basis it is possible to construct a regression function. In a regression function, the regression coefficients play a role, resulting in a regression line.

An example: If you wanted to find out how advertising investments affect sales, you would use a regression analysis to examine the relationship between investments and sales.

If this relationship is clear, it can be used to make predictions

Therefore regression analysis has two central objectives. you should do:

Quantify relationships and describe them using measured values and their graphical representation.
Enable forecast and forecast.

Types of regression analysis

## Various regression analyzes at a glance:

In Simple regression: Their is only one explanatory variable is used to explain the dependent variable.

Multiple Regression: Multiple explanatory variables are related to a dependent variable.

Linear regression: There is a linear relationship between multiple explanatory variables and multiple dependent variables. There is also talk of parameters that are linear and translate into a structure.

Non-linear regression: if there is no linear relationship between dependent and independent variables, we speak of

non-linear regression. These models can be very complex, as the relationships between variables cannot be mapped using simple mathematical tools.

Process for regression analysis
There are various regression methods, but the structure of these methods is often similar in terms of steps:

Data processing:

In order to examine the evolution and trends of the variables, the positioning of the data with the corresponding data points should be as complete and accurate as possible. To check the data, for example, rough calculation and Pl

## Verified usability.

If data records are missing, so-called missing data techniques, also known as imputation, can be used.

If the data and their relationships are to be represented graphically, this can also be taken into account during processing.

Some regression models require very special data formats to which they must first be converted. This is the case, for example, of linear regression, in which a linear relationship between two variables is assumed.

### Model optimization:

Each regression model works with statistical error correction to keep track of any deviations.

Actions aimed at minimizing deviations are sometimes defined by the model. In linear regression, it is also a linear function used to handle divergence.

Here the error values and estimates are calculated and integrated into the regression model from the start.

Validation of the model used: It is now checked whether the regression model describes the relationship between the independent and dependent variables and how well this description fits.

There are various methods and approaches to check the validity of the regression analysis used. For example, particularly influential data nodes that influence relationships between variables are analyzed.

Ultimately, a function must describe this relationship – however, if the function fits, it must be ascertained using the regression method.

Forecast values: If the model adequately describes the relationship, it can be used for forecasting purposes. Here, too, precision plays a central role.

If claims are provided that go beyond the actual dataset, this is referred to as extrapolation. In the case of predictions within a data set, this is referred to as interpolation.

The latter is less problematic than extrapolation: the deductions made here should be carefully examined.