From the course: Practical Python for Time Series Analysis

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Regression diagnostics and assumptions

Regression diagnostics and assumptions - Python Tutorial

From the course: Practical Python for Time Series Analysis

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Regression diagnostics and assumptions

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- [Speaker] Now that you have understood the most important measures to interpret the model, which is the R-squared and the p-value, we go to the following table, which is in the results, the second one in the list, and here, we get the constant and the CPI in the rows and the type of values in the columns. The most important is the coefficient because they establish the mathematical equation of the formula, which is calculating the mortgage rates being equal to the constant value plus the CPI value. And because the CPI is a variable from the data set that we pass to the model, it's being multiplied to a number that we must pass, which is the CPI. Now, if we want to put this as a function, we can use Lambda at the beginning with the CPI as the variable, and this function will be called the mortgage rate. We execute. And now if we pass, for example, a CPI equal to one and we execute, we get 3.21. If we pass a CPI of two, we…

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