
The error in two readings of A are 0.01 and -0.03, then mean absolute error is ?
Answer
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Hint :The mean absolute error is one of several methods for comparing forecasts to their final results. The mean absolute scaled error (MASE) and the mean squared error are two well-known alternatives. These all describe performance in ways that ignore the direction of over- or under-prediction; the mean signed difference, on the other hand, does.
Complete Step By Step Answer:
The disparity between an exact value and some estimate to it is known as approximation error in some data. Because of this, an estimate mistake can arise.
Because of the equipment, the data measurement is not exact. (For example, a piece of paper's precise reading is 4.5 cm, but because the ruler does not utilise decimals, you round it to 5 cm.) or approximations. The numerical stability of an algorithm reveals how the mistake is spread by the algorithm in the mathematical subject of numerical analysis.
Absolute error is the amount of the difference between the mean value and each individual value. The real quantity of measurement mistake is referred to as absolute error in measurement. The measurement's absolute error indicates how great the mistake is. Absolute mistake is insufficient since it provides no information about the significance or seriousness of the error.
Given that A has an inaccuracy of 0.01 and -0.03 in two measurements.
The ultimate mistake must be found.
Solution.
The absolute value of the mistakes is first determined.
0.01 minus (-0.03) equals 0.04.
To get the mean, divide the absolute error by two.
As a result, the mean absolute error equals $ \dfrac{{\left( {0.04} \right)}}{2}{\text{ }} = {\text{ }}0.02 $ .
Note :
There are two characteristics of relative inaccuracy to keep in mind. For starters, relative error is undefinable when the real value appears in the denominator as zero. Second, relative error makes sense only when measured on a ratio scale (i.e., a scale with a real meaningful zero), as it would be sensitive to the measurement units otherwise.
Complete Step By Step Answer:
The disparity between an exact value and some estimate to it is known as approximation error in some data. Because of this, an estimate mistake can arise.
Because of the equipment, the data measurement is not exact. (For example, a piece of paper's precise reading is 4.5 cm, but because the ruler does not utilise decimals, you round it to 5 cm.) or approximations. The numerical stability of an algorithm reveals how the mistake is spread by the algorithm in the mathematical subject of numerical analysis.
Absolute error is the amount of the difference between the mean value and each individual value. The real quantity of measurement mistake is referred to as absolute error in measurement. The measurement's absolute error indicates how great the mistake is. Absolute mistake is insufficient since it provides no information about the significance or seriousness of the error.
Given that A has an inaccuracy of 0.01 and -0.03 in two measurements.
The ultimate mistake must be found.
Solution.
The absolute value of the mistakes is first determined.
0.01 minus (-0.03) equals 0.04.
To get the mean, divide the absolute error by two.
As a result, the mean absolute error equals $ \dfrac{{\left( {0.04} \right)}}{2}{\text{ }} = {\text{ }}0.02 $ .
Note :
There are two characteristics of relative inaccuracy to keep in mind. For starters, relative error is undefinable when the real value appears in the denominator as zero. Second, relative error makes sense only when measured on a ratio scale (i.e., a scale with a real meaningful zero), as it would be sensitive to the measurement units otherwise.
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