The fact that the p-value for the MonSq variable is near 0 also confirms that the quadratic coefficient is significant. The Adjusted R Square value of 95% and p-value (Significance F) close to 0 shows that the model is a good fit for the data. We now run the Regression data analysis tool using the table on the right (quadratic model) in columns I, J and K as the input. We next create the table on the right in Figure 1 from this data, adding a second independent variable (MonSq) which is equal to the square of the month. Determine whether a quadratic regression line is a good fit for the data.įigure 1 – Data for polynomial regression in Example 1 A sample of 5 people is chosen at random and the number of hours of Internet use is recorded for 6 months, as shown in the table on the upper left side of Figure 1. Įxample 1: A group of senior citizens who have never used the Internet before are given training. Studied in Multiple Regression Analysis where. This is equivalent to the usual multiple regression model We look at a quadratic model, although it is straightforward to extend this to any higher-order polynomial.
Click here to learn more about Real Statistics capabilities that support polynomial regression.
#Excel data analysis add in quick stats how to
On this webpage, we explore how to construct polynomial regression models using standard Excel capabilities.
Sometimes data fits better with a polynomial curve. In Method of Least Squares for Multiple Regression, we review how to fit data to a straight line.
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