The study on regression analysis as a powerful statistical method allows us to examine the relationship between two or more variables of interest.
While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable.
It also alluded that regression analysis can be used to provide detailed insight that can be applied to further improve products and services.
What is Regression Analysis? It is a reliable method of identifying which variables have impact on a topic of interest. This process of performing a regression will allows one to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.
For us to understand regression analysis fully, it is important to understand the following terms:
- Dependent Variable: This is the main factor that we are trying to understand or predict.
- Independent Variables: These are the factors that we are suggesting as having an impact on the dependent variable.
How to understand the workings of a regression analysis
To conduct a regression analysis, we will need to define a dependent variable that we suggest is being influenced by one or several independent variables.
We would then need to establish a comprehensive list of data information to work with. To carry out a survey to one’s audiences could be a good way to establish this data information. Such survey should include questions addressing all the independent variables that one is interested in.
The use of historical data information on the relationship of sales, advert and volume was carried and the effect as well as any information possible regarding the independent variables. In other to begin the investigation on whether there is a relationship between these two variables, we would begin by plotting these data points on a chart, which would look like the following theoretical example.
(Plotting the data is the first step in figuring out if there is a relationship between your independent and dependent variables)
Our dependent variable (in this case, the Sales) should be plotted on the y-axis, while our independent variable (the advert and volume) should be plotted on the x-axis.
After the data is plotted, we may begin to see correlations. If the theoretical chart above did indeed represent the impact of sales prices, then we would be able to confidently say that the higher the sales price, the higher the impact on advert cost.
It is equally important to find out the relationship or degree to which sales price affect the advert and volume.
To get to understand it, to draw a line through the middle of all the data points on the chart. The line is referred to as a regression line, and it can be calculated by using a standard statistics programme such as excel.
Having to use a theoretical chart once more to understand what a regression line should look like.
The regression line represents the relationship between one independent variable and one dependent variable.
To derive the formula in excel of the slope of the line, which adds further context to the relationship between the independent and dependent variables.
The formula for a regression line was highlighted as Y = 100 + 7X + error term.
It tells us that if there is no “X”, then Y = 100. If X is our increase in sales price, this informs us that if there is no increase in sales price, advert cost will still increase by 100 points.
The slope formula calculated using excel includes an error term. Regression lines always consider an error term because, independent variables are never precisely perfect predictors of dependent variables. This makes sense while looking at the impact of sales prices on advert and volume — there are clearly other variables that are contributing to it.
Our regression line is simply an estimate based on the data available to us. So, the larger our error term, the less definitively certain our regression line is.
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