This example shows how to fit a linear regression model for data in your ThingSpeak™ channel and calculate the regression coefficients in the data.
ThingSpeak channel 12397 contains data from the MathWorks® weather station, located in Natick, Massachusetts. The data is collected once every minute. Field 3 and 4 contain humidity and temperature data, respectively. Read the data over the last day from channel 12397 using the
data = thingSpeakRead(12397,'NumDays',1,'Fields',[3 4],'outputFormat','table');
Describe linear relationship between a response (humidity) and one or more predictive terms (temperature). For example, 'Humidity ~ 1 + TemperatureF' describes a two-variable linear model relating humidity with temperature along with an intercept.
mdl = fitlm(data, 'Humidity~TemperatureF')
mdl = Linear regression model: Humidity ~ 1 + TemperatureF Estimated Coefficients: Estimate SE tStat pValue ________ ________ _______ ___________ (Intercept) 49.448 1.7916 27.6 2.1811e-134 TemperatureF 0.038851 0.045941 0.84567 0.39788 Number of observations: 1410, Error degrees of freedom: 1408 Root Mean Squared Error: 4.39 R-squared: 0.000508, Adjusted R-Squared: -0.000202 F-statistic vs. constant model: 0.715, p-value = 0.398
The values show the estimated regression coefficients for the linear model along with other statistical parameters.