Create an instance of the IExcel.Application interface.NET applications? It’s actually quite simple: Okay, so how can we leverage this built-in functionality in Excel in our own. Interpreting the data, of course, is really a whole new topic and outside the scope of this blog post. The results of this function show us the relationship between the dependent and one or more independent variables. Check out this article for a good example on using Excel to do this. The LINEST function takes a set of known y and x points and calculates an equation for the best-fit line given the data using the least squares method. I’ll leave it at that for now as I want to jump into code, but there’s a lot more information regarding linear regression scattered across the Web.įortunately for many of us, Excel has a built-in function called LINEST that does all the hard work for us. In this particular example, I have two independent variables that taken together may affect my dependent variable. Perhaps Singapore property prices, for instance, are a function of both GDP and interest rates. Furthermore, I can use “multiple” regression analysis to test the relationship between a dependent variable and multiple independent variables. For example, I could test whether property prices in Singapore (my y or dependent variable) is dependent on GDP (my x or independent variable). In other words, if you had a hypothesis that a particular variable (denoted by y) was linearly related to one or more other variables (denoted by x i), you could use linear regression to test your hypothesis. The results are subject to statistical analysis. However, note that "linear" does not refer to this straight line, but rather to the way in which the regression coefficients occur in the regression equation. the dependent variable from the regression equation) is plotted against the independent variable: this is called a simple linear regression. A linear regression equation with one independent variable represents a straight line when the predicted value (i.e. This function is a linear combination of one or more model parameters, called regression coefficients. Linear regression is a form of regression analysis in which the relationship between one or more independent variables and another variable, called the dependent variable, is modelled by a least squares function, called a linear regression equation. to test whether and how a given variable is related to another variable or variables.to construct a simple formula that will predict a value or values for a variable given the value of another variable.In statistics, linear regression is used for two things