Seven Statistics Terms to Know | Marketing and Multiple Regression Analysis – Part 7

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Seven statistical terms to know | Marketing and multiple regression analysis - Part 7

Analysis of marketer's essential skills and promotionLet's try using multiple regression analysis. We will learn the basics of multiple regression analysis over five sessions. In the first session, we used diagrams to get a rough understanding of multiple regression analysis. Next, we will introduce seven commonly used statistical terms.

Multiple regression analysis is one of the analytical methods. It allows you to see the relationship and influence of multiple factors on results, so it is perfect for analyzing promotions. We have explained it using diagrams. When expressing multiple regression analysis as a formula, several technical terms appear. It will be beneficial to understand this not only for multiple regression analysis, but also for understanding statistics more fully. However, Marketing's Netacho aims to provide "statistical analysis that does not require formulas."

Even if you don't understand them now, just know that they are statistical terms. Here are the seven terms.

Multiple regression analysis is expressed as a formula like this

Seven terms you need to understand multiple regression analysis

1: Objective variable

A variable that is "explained" by other variables. In the multiple regression equation above, this refers to the variable to the left of the "=".

2: Explanatory variables

It is the variable that "explains" the dependent variable and is on the right side of the "=" in the multiple regression equation.

Note that the "objective variable and explanatory variable" can be called several different things, such as "explained variable and explanatory variable" or "dependent variable and independent variable," but they all mean the same thing.

3: Coefficient

This is a number that indicates the degree to which an explanatory variable affects a target variable; the larger the coefficient, the greater the influence.

However, because the magnitude of the coefficient is affected by the units of the explanatory variable data, it cannot be used to compare the influence of other explanatory variables in the same multiple regression equation. For example, if you change meter data to centimeters, the coefficient will be 100 times larger. To compare the influence of explanatory variables, you can use the t-value described below.

4: Constant term

The constant term, also known as the y-intercept, is a value that is not affected by variations in the explanatory variables.

5: Coefficient of determination

First, from the first stage coefficient of determination

The "coefficient of determination" in the upper row is an index showing the accuracy of the multiple regression equation, and is a number that indicates the extent to which the movement of the objective variable can be explained by the explanatory variables. The closer it is to 100%, the higher the accuracy. It is also written as R2, and in the case of multiple regression analysis, it is sometimes called the "multiple coefficient of determination." The coefficient of determination has the property that it increases as the number of explanatory variables increases. However, since this only improves the apparent accuracy, sometimes a coefficient of determination adjusted for the degrees of freedom is used.

6: t-value 7: p-value

t-values ​​and p-values

Next, we have the t-value and p-value in the lower row. In multiple regression analysis, the t-value indicates the magnitude of the influence that each explanatory variable has on the objective variable, with the larger the absolute value, the stronger the influence. As a guideline, if the absolute value of the t-value is less than 2, it is determined that the explanatory variable does not statistically affect the objective variable. The p-value indicates the significance probability of the coefficient of each explanatory variable. Generally, if the significance probability is below 5%, it is determined that the explanatory variable is "related" to the objective variable.

Just knowing these statistical terms makes a difference

Do you feel like it has become difficult all of a sudden? As I explained at the beginning, you don't need to understand it right away. Just knowing the words will make it easier to read statistics books and materials, and you should be able to understand them better. In the third installment, I will explain the failure patterns of multiple regression analysis.

A guide to multiple regression analysis using Excel

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A guide to multiple regression analysis in Excel that empowers marketers
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Marketing and Statistics Article Summary

Learning with diagrams makes it easier to understand! | Marketing and Multiple Regression Analysis - Part 1
We explain why multiple regression analysis is perfect for the marketing field and use diagrams to clearly explain multiple regression analysis.

Read on to understand. 10 common mistakes in multiple regression analysis | Marketing and multiple regression analysis - Part 3
What should you do in this situation? Here are 10 common cases where multiple regression analysis tends to fail, along with solutions.

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