The 95% confidence interval is a random interval, which means that the endpoint is a random variable. This random variable is usually a statistic. When different samples are drawn, it corresponds to different values, and thus corresponds to different intervals. For some samples, the corresponding interval contains the true value of the parameter, while others do not. If 100 such intervals are constructed in 100 random sampling as early as 100 times, if 95 times contain the true value of the parameter, then the confidence level is 95%.
Point estimate: the probability of a person suffering from new coronary pneumonia
District estimate: the probability of a district suffering from new coronary pneumonia
The 95% confidence interval is a method of area estimation provided by probability statistics.
Give an example:
Assuming that the population’s new coronary pneumonia obeys: X~N(u, o2)
Where u is unknown and o is known.
For population sampling, the size of the sample is n, and the mean of the sample:
M=（X1+X2+x3+……+Xn）/n
According to the uncle's law and the central limit law, M obeys:
M ~ N （u ， o2 / n）
It is possible to calculate the interval with an area of 95% centered on u.
P(u-1.96*(u/root number n)<= M<=u+1.96*(u/root number n))=0.95
That is, M has a 95% chance of falling into this range:
Average absolute relative error value (MARD%)
The mean absolute error is the average of the absolute values of the deviations of all individual observations from the arithmetic mean. The average absolute error can avoid the problem of mutual cancellation of errors, so it can accurately reflect the size of the actual forecast error. (Online Liezi)
