Academic Master

Business and Finance

Regression Analysis of Amazon

To determine the success of Amazon, its sales and income data for 17 years is given. The data is represented in a scatter plot with income as an independent variable and sales as a dependent variable. Income is considered independent as we can see from the year 1996 to 2002 the company shows losses or a negative income even when there are sales. This establishes that income is independent of sales. The scatter plot shows a positive trend from the year 2003 and onwards.

The regression analysis helps us establish which factors or variables that are considered independent in a study have an actual impact on the dependent variable. It can also help establish how these variables interact and which of the given factors can be ignored (Gallo, 2015). The regression line on the scatter plot shows a positive relationship between the independent and dependent variables. The multiple R-value is the coefficient of correlation with a value close to 1, it suggests a linear relation. The value given as R square shows how many data points fall on the line. The higher percentage range falls between 85 to 100%. The results show that the R square is 0.93 or 93%. As the data set has one independent variable therefore we will not use the adjusted R square value (Glen, n.d.).

For regression analysis, the standard error helps in understanding the distance of the data point from the regression line. This means that the smaller value of the standard error shows that the data points are close to the trend line. Values ranging between ±2 are considered a good range in predicting less deviation from the given line. For our data set the standard error is high with a value of 3.7. The linear equation for the data set is represented as y = 0.0039x + 4.7305.


Gallo, A. (2015, November 4). A Refresher on Regression Analysis. Harvard Business Review.

Glen, S. (n.d.). Excel Regression Analysis Output Explained. Statistics How To. Retrieved March 11, 2022, from



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