RSquare: 0.655950. This represents the proportion of variation in the response variable (wind speed) that can be explained by the predictor variable (temperature). In this case, about 65.6% of the variation in wind speed can be explained by temperature.
RSquare Adj: 0.826027. This is a slightly adjusted version of RSquare that takes into account the number of predictor variables in the model. It provides a more accurate measure of how well the model fits the data. In this case, the adjusted RSquare suggests that the model is a good fit for the data.
Root Mean Square Error: 2.953. This is the standard deviation of the residuals (the differences between the predicted wind speeds and the actual wind speeds) and represents the typical distance between the predicted and actual values. In this case, the RMSE is 2.953 mph, meaning that the predicted wind speed is typically within 2.953 mph of the actual wind speed.
Mean of Response: 9.947. This is the average wind speed over the 365 days of data.
Observations (or Sum Wgts): 365. This is the number of data points in the sample.
Parameter Estimates: These are the coefficients of the linear regression equation, which is y = 11.897762 - 0.041077x, where y is the predicted wind speed and x is the average temperature. The intercept of 11.897762 represents the predicted wind speed when the temperature is 0 degrees Fahrenheit, and the slope of -0.041077 represents the change in wind speed for each 1 degree increase in temperature. The standard errors and t-ratios are also provided, which can be used to test the significance of the coefficients.