2.2 Measures of the Location of the Data
i=(k100)(n+1)
where i = the ranking or position of a data value,
k = the kth percentile,
n = total number of data.
Expression for finding the percentile of a data value: (x + 0.5yn)(100)
where x = the number of values counting from the bottom of the data list up to but not including the data value for which you want to find the percentile,
y = the number of data values equal to the data value for which you want to find the percentile,
n = total number of data
2.3 Measures of the Center of the Data
μ=∑fm∑f Where f = interval frequencies and m = interval midpoints.
The arithmetic mean for a sample (denoted by ˉx) is ˉx = Sum of all values in the sampleNumber of values in the sample
The arithmetic mean for a population (denoted by μ) is μ=Sum of all values in the populationNumber of values in the population
2.5 Geometric Mean
The Geometric Mean: ˜x=(n∏i=1xi)1n=n√x1·x2···xn=(x1·x2···xn)1n
2.6 Skewness and the Mean, Median, and Mode
Formula for skewness: a3=∑(xi−ˉx)3ns3
Formula for Coefficient of Variation:CV=sˉx·100conditioned uponˉx≠0
2.7 Measures of the Spread of the Data
sx=√∑fm2n−–x2 where sx= sample standard deviation–x = sample mean
Formulas for Sample Standard Deviation s=√Σ(x−–x)2n−1 or s=√Σf(x−–x)2n−1 or s=√(n∑i=1x2)-nˉx2n-1 For the sample standard deviation, the denominator is n - 1, that is the sample size - 1.
Formulas for Population Standard Deviation σ = √Σ(x−μ)2N or σ = √Σf(x–μ)2N or σ=√N∑i=1x2iN-μ2 For the population standard deviation, the denominator is N, the number of items in the population.