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To understand what statistical error is, in an extremely simple way, we can say that it is the difference between measured quantity and its real value. Assessing the performance of the regression error measure is also done with the help of Statistical methods. We can also assume that random errors are always expected in any experiment or calculation of mathematical estimations, thus considered in measurement error. For example, let us say, your body weight was 80kg yesterday with wet clothes and a chocolate bar in your pocket and the next day you are dry and have nothing in your pocket and you weigh 78kg, 2kg of measurement error.
How specific errors are to be expected in mathematical formulas
When we use mathematical formulas like velocity calculation or practical implementation of Pythagoras theorem or calculating the kinetic energy the measurement errors can quadruple easily because of the multiplication and exponential values present in the formulas.
To make the final value more accurate, we should use an error propagation method (One of the statistical methods of assessing the performance of the regression error measure in machine learning) which can help to overcome the errors.
Using machine learning algorithms will always have a degree of error in the result value because it is a prediction based on the previous data which cannot be exactly what is the actual result. So to rectify that error and to get the most accurate result as compared to the real value we perform the Statistical methods of assessing measurements error.
Performance of the regression measure in machine learning is based on the statistics and prediction model on the basis of previous data. If the data does not have precise value when assessing the performance of the regression measure, a machine learning algorithm will always provide the wrong or error oriented values.
For assessing the performance of the regression measure in machine learning we need to correctly apply the error propagation and statistical methods after getting more than 10 results.
What type of different errors can you expect?
When doing a calculation there can be some of the most common but distressing errors which are:
Absolute error
Instrument error
Margin error
Measurement location error
Operator error/human error
Relative error: E absolute / E measured
To calculate a better value in dealing with the error we perform the percentage error formula to get the percentage of error in the value.
(Measured value – Actual value) / Actual value
Some of the most common physical ways to reduce the measurement errors
Double Checking all the formulas used
Checking all the calculations again on the calculator as well as manually
Double-checking zero errors of all the instruments
Make sure that all the persons doing experiments are well trained to measure values
Take multiple measures to perform the same construct.
Statistical methods to assess the measurement error
To assess the measurements’ absolute reliability, we need to perform the statistical measures and methods which can keep out the most relevant error from the equation with just more calculations.
Below are some of the statistical measures and methods
Standard error of measurement: It shows how much-measured values are spread around our actual equation value. It is relevant because it takes the same unit as the actual value and applies to a single test value.
SEM= S(1-rxx)1/2
rxx= S2t/S2x
S2t = variance of the true scores/values
S2x= variance of the observed scores/values
Coefficient of variability: The ratio of standard deviation to the mean of the calculated values.
(Standard deviation/Mean) *100
Limits of agreement: It estimates the interval where part of the difference lies between measurements. It is easier to use and a less expensive method, but it also gives the inconclusive data available reliability.
Data is analyzed with the Bland Altman limits of agreement method.
These statistical analyses and measures are fundamental and beneficial in removing the errors from the mathematical measurements and formulas performed.
Using standard error of measurement is the best method for assessing the performance of the regression error measures in machine learning because it will calculate the mean of the most-closest values to the actual result.
Conclusion
The statistical method of measurement error is a must process to provide the last result with accurate values. Using the statistical method alone will not work because human error or instrument error cannot be resolved with any formula. Still, Statistical measures for the measurement error are a must use case. In experimental formulas and mathematical equations using statistical measures is the most important thing to do because without it the answer we are expecting will not come and the tension around the number will increase which will ultimately result in more human errors and inaccuracy.
