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The regression model in data analysis is a powerful statistical analysis tool that helps unlock relevant insights from data and make the right decision. From finance to healthcare and market research, it serves as a cornerstone to predict future trends and mitigate risk in business decisions.
In this article, we will take a deeper look at the regression model and its types.
A regression model determines a relationship between an independent variable and a dependent variable, by providing a function. Formulating a regression analysis helps you predict the effects of the independent variable on the dependent one.
Example: we can say that age and height can be described using a linear regression model. Since a person’s height increases as age increases, they have a linear relationship.
Regression models are commonly used as statistical proof of claims regarding everyday facts.
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There are four different types of regression models:
Let’s look at them in detail.
A linear regression model is used to depict a relationship between variables that are proportional to each other. This means that the dependent variable increases/decreases with the independent variable.
In the graphical representation, it has a straight linear line plotted between the variables. Even if the points are not exactly in a straight line (which is always the case) we can still see a pattern and make sense of it.
For example, as the age of a person increases, the level of glucose in their body increases as well.
A non-linear regression model allows for a more complex and flexible relationship between variables. The relationship is described by a non-linear function rather than a straight line. This function can have multiple parameters which you can estimate from the gathered data by using statistical analysis.
This model is useful when you cannot capture the relationship between the variables using a linear mode. It provides a powerful tool to analyze data and uncover complex relationships between your dependent and independent variables.
A multiple regression model is used when there is more than one independent variable affecting a dependent variable. While predicting the outcome variable, it is important to measure how each of the independent variables moves in their environment and how their changes will affect the output or target variable.
For example, the chances of a student failing their test can be dependent on various input variables like hard work, family issues, health issues, etc.
Unlike the above-mentioned regression model types, stepwise regression modeling is more of a technique used when various input variables are affecting one output variable. The analyst will automatically proceed to measure the variable that is directly correlated input variable and build a model out of it. The rest of the variables come into the picture when he decides to perfect the model.
The analyst may add the remaining inputs one after the other based on their significance and the extent to which they affect the target variable.
For example, vegetable prices have increased in a certain areas. The reason behind the event can be anything from natural calamities to transport and supply chain management. When an analyst decides to put it out on a graph, he will pick up the most obvious reason, heavy rainfall in the agricultural regions.
Once the model is built, he can then add the rest of the affecting input variables into the picture based on their occurrence and significance.
Also read: A Rundown of Top Market Research Analysis Tools – Essential Tool for Global Brands and Enterprises
In this data analysis model, you are essentially aiming to establish and understand the relationship between two types of variables. So, let’s look at how the process works.
As we know, there are two types of variables we focus on in this data analysis model. Start by defining the variables:
After defining the variables, you collect data for each variable and polit it on a graph according to the number of variables. For example, use a scatter plot if you have one independent and one dependent variable. If you have multiple independent variables you may need to creat separate scatter plots for each pair of variables.
Once you have plotted your collected data, you can evaluate the relationship between the independent and dependent variables. Look for patterns or trends that suggest a correlation. For example, if there’s a strong linear relationship, it has a potential for regression analysis.
If your data shows linear regression, a straight line will represent the relationship between the two variables. This line is called regression line which you can determine by statistfical techniques to minimize the difference between the actual data points and the predicted values on the line.
The regression model formula helps represent the relationship between independent and dependent variables in a mathematical manner. The formula for simple linear regression is: Y=β0+β1X+ε
It is also important to acknowledge that there will be some level of error in the relationship of variables understanding and assessing this error is crucial to ensure rhe reliability and accuracy of the regression analysis result.
Also read: A Guide to Survey Analysis Methods: Uncover Hidden Insights
Market researchers widely use regression models for data analysis. Let’s look at some ways you can leverage it in your next market research.
Let’s say your company wants to understand how changing the price of some products will impact the sales volume. You can use a regression model to analyze the historical sales data along with the corresponding changes in price structure to estimate the impact. This will help you see the relationship and measure the responsiveness of product demand to a price change.
Assume your organization is aiming to identify factors influencing customer satisfaction with its services. By using surveys, you can collect feedback on aspects such as product quality, pricing, features, and customer service. The regression model can help you analyze the gathered data and quantify the impact of each factor on customer satisfaction.
The regression model is a powerful tool that enables you to identify and understand various types of relationships between your target variables.
The regression model gives you the advantage of establishing a statistically significant relation between variables such as important business outcomes. This allows you to make key business decisions which may help you drive business growth and win the market.
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