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Data analysis lies at the heart of business strategies. It enables businesses to understand consumer behavior, explore market trends, and analyze the impact of marketing strategies. In today’s data-driven world, data analysis is essential to stay relevant in the marketplace.
Multiple regression analysis helps examine the relationship between dependent variables and two or more independent variables. In this blog, we’ll explore its procedure and how this analysis model helps you uncover underlying factors influencing consumer behavior and market demand.
As the name suggests, multiple regression is a statistical technique applied to datasets dedicated to drawing out a relationship between one response or dependent variable and multiple independent variables. From the definition, it is obvious that the study of an event or phenomenon will have various factors causing its occurrence.
Multiple regression works by considering the values of the available multiple independent variables and predicting the value of one dependent variable.
A researcher decides to study students’ performance at a school over a period of time. He observed that as the lectures proceeded to operate online, the performance of students started to decline as well. The parameters for the dependent variable “decrease in performance” are various independent variables like “lack of attention, more internet addiction, neglecting studies,” and much more.
Multiple regression analysis demonstrates several key assumptions. Let’s look at them in detail.
We will start the discussion by first taking a look at the linear regression equation:
y = bx + a
Where,
y is a dependent variable we need to find, x is an independent variable. The constants a and b drives the equation.
But according to our definition, as the multiple regression takes several independent variables (x), so for the equation, we will have multiple x values too:
y = b1x1 + b2x2 + … bnxn + a
Here, to calculate the value of the dependent variable y, we have multiple independent variables x1, x2, and so on. The number of independent variables can grow till n and the constant b with every variable denotes its numeric value. The purpose of constant a is to denote the dependent variable’s value when all the independent variable values turn to zero.
Example: So for the above example, the multiple regression equation would be:
y = b1 * attention + b2 * internet addiction + b3 * technology support + … bnxn + a
Also read: Regression Analysis.
In case of linear regression, although it is used commonly, it is limited to just one independent and one dependent variable. Apart from that, linear regression restricts to the training dataset and does not predict a non-linear regression.
We use multiple regression to cover the same limitations. It focuses on overcoming one particular limitation, and that is allowing the analysis of more than one independent variable.
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There are four steps you need to follow to ensure your gathered data is in right format for the multiple regression model.
Leverage reliable survey software to collect relevant and accurate data from a large audience. This is the first step in conducting multiple regression analysis.
In the next step, it is essential to clean and process the data to remove any errors or inconsistencies that can skew the data analysis. This step may include data standardization or transformation, so utilize a data analytics tool that automates this process.
Carefully select the independent variables to ensure the accuracy and validity of the analysis. Choose the variables based on practical considerations and empirical evidence.
Missing data is a common challenge in data analysis. You can use various techniques to remove missing data from your gathered data to minimize bias and preserve the integrity of the regression analysis.
Let’s look at some of the ways businesses leverage this regression model to make informed decisions.
Let’s assume a beverage company wants to understand the preference of their target consumer for the newly launched soft-drinks. The company uses multiple regression analysis to identify key variables like age, consumption habits, and lifestyle to predict the preferences of different consumer segments.
This allows them to tailor marketing efforts and product offerings based on the segments.
In this use case, let’s assume an electronic company wants to predict demand for its latest tablet. Using multiple regression, the company examines historical sales data and economic indicators to develop a demand forecasting model.
This helps the company to anticipate market trends, adjust production levels, and allocate resources efficiently.
Multiple regression analysis allows you to uncover relationships, make predictions, and gain valuable insights into your target consumer behavior and market dynamics. By understanding the applications, assumptions, and needs of this regression model, you can leverage the analysis model and make more informed decisions, optimize strategies, and stay ahead of the competition.
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