Since 1 kilometre = 1000 metres, then if an error is 1 m in 1 km, then that would translate to 1/1000 = 0.001 x 100 = 0.1% error.
The error in its area is then 2 percent....
type1 error is more dangerous
The relative error puts the size of the error into context. An absolute error of 10, in a number whose value is 1 indicates a range of -9 to 11 for the true value. This means that telling you that the value is 1 is near enough pointless. On the other hand, an absolute error of 10 in a number whose value is 1 billion means that the true value is somewhere in the range 999,999,990 and 1,000,000,010. I suggest that the discrepancy is not significant. The relative error in the first case is 1000% and in the second, it is 1 millionth of 1%.
1 error of omission 2 error of compensation 3 error of original entry 4 error of principle 5 error of commission
Accucheck error E7
Studies suggest that the accuracy of AccuChek falls into the 'acceptable' range, but not ideal. Prices for AccuChek range from $10-$20.
This is a meter error. Contact Customer Service at the telephone number you can find on the back of the meter or in your paperwork.
That is the correct spelling of "glucometer" (blood glucose measuring device)
It depends on the kind of glucometer you have...often you just have to enter the code that comes with each new package of test strips. The manual that comes with the glucometer will explain how to enter that code.
how many times should you need to calibrate your glucometer in a week
Glucometer
Depends on the meter
This sounds more of a homework question than an 'out of interest' one, haha :).. So they can check their blood glucose levels and make sure they are within the normal, healthy target range. If the BG level is low, they take sugar and if the BG level is high they take more insulin. :)
Since 1 kilometre = 1000 metres, then if an error is 1 m in 1 km, then that would translate to 1/1000 = 0.001 x 100 = 0.1% error.
Change Battery!
In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.