I didn't see the listed results before, but I used confidence. Norm function, and this result is the confidence interval, that is, the 95% confidence interval is [0.206052-0.018, 0.206052+0.0038+038+.

Regression results show that the coefficient k=0 is very impossible. If we look at the significance f, which is very small, we know that the regression is significant, but the regression result is not good. After adjustment, the R square is 0.902 1348 15, which shows that the model to be made is not a good linear model. Generally speaking, this value should be greater than 0.95 or even 0.99.

Extended data:

If someone's support rate in the general election is 55% and the confidence interval is (50%, 60%) at the confidence level of 0.95, then there is a 95% chance that his true support rate falls between 50% and 60%, and the probability that his true support rate is less than half is less than 2.5% (assuming the distribution is symmetrical).

As shown in the example, the confidence level is generally expressed as a percentage, so the confidence interval when the confidence level is 0.95 can also be expressed as a 95% confidence interval. The two ends of the confidence interval are called confidence limits. For the estimation of a given situation, the higher the confidence level, the larger the corresponding confidence interval.

The calculation of confidence interval usually requires assumptions about the estimation process (so it belongs to parameter statistics), such as assuming that the estimation error is normally distributed.

Baidu Encyclopedia-Confidence