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Step Deviation Method

The step-deviation method is a statistical technique used to calculate the mean of grouped data. It simplifies complex calculations by scaling down large class marks and frequencies using an assumed mean and a common class size.

Key Concepts

First, find the class mark for each class interval, which is the exact midpoint or average of the upper and lower class limits.

Select an assumed mean from the middle of the class marks to minimize the size of the calculations.

Calculate the step-deviation for each class by subtracting the assumed mean from the class mark, then dividing that result by the uniform class size.

Formula & Equation

\bar{x} = a + \left( \frac{\sum f_i u_i}{\sum f_i} \right) \times h

\bar{x} is the mean, a is the assumed mean, f_i is the frequency of the i-th class, u_i = \frac{x_i - a}{h} is the step-deviation, x_i is the class mark, and h is the class size.

Common Misconceptions

Myth: The step-deviation method provides an approximate mean, while the direct method gives the exact mean.

Fact: The step-deviation method is mathematically equivalent to the direct method and provides the exact same mean. It only scales the numbers down temporarily for easier manual calculation.

Real World Applications

Calculating the average daily wages of thousands of factory workers grouped into equal wage brackets.

Determining the average monthly electricity consumption of households in a large city where data is grouped into uniform intervals.

Frequently Asked Questions

When is it best to use the step-deviation method for finding the mean?

It is most useful when dealing with grouped data that has large frequencies and large class marks, provided the class intervals are of equal size.

Can the step-deviation method be used if the class sizes are unequal?

No, the standard step-deviation formula requires a uniform class size across all class intervals to scale the deviations correctly.

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