# Measures of dispersion for grouped data. Statistics Calculator 2019-01-06

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## Measures of Dispersion Bassart Electronics is considering employing one of two training programs. The population standard deviation, σ, is obtained by taking the square root of the population variance. Accordingly, measures of dispersion is related to determining whether the distribution of data vary or differ from one another. Similarly, sometimes we calculate percentile range, say, 90th and 10th percentile as it gives slightly better measure of dispersion in certain cases. The Range The range is the most obvious measure of dispersion and is the difference between the lowest and highest values in a dataset. The value may be given in the series or may not be given in the series.

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## Statistics Calculator It has a higher standard deviation because it is more spread out. When mean is greater than median, which is further greater than mode, then the distribution of data is considered to be positively skewed and points in the positive direction i. . The thing is, if we divide by N in the sample variance formula above, our estimate will, on average, be too low. Measures of dispersion for ungrouped data In statistics, in order to describe the data set accurately statisticians must know more than measures of central tendency.

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## Measures of Dispersion Steps to calculate σ i Take deviations from the assumed mean of the calculated mid-points and divide all deviations by a common factor i and represent these values by dx. The frequency distribution of weights in grams of mangoes of a particular variety is given below. We call an estimate like this biased, since it consistently under-estimates the parameter it's trying to predict. Generally, the measure of central tendency from which the deviations arc taken, is specified in the problem. Hence, the real limits shall be : 9.

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## Measures of Dispersion Find the mean absolute deviation. Similarly, if there arc negative items in a series, the value of mean will come down and the value of standard deviation shall not be affecied because of squaring the deviations. So what can we tell from the standard deviation? It is a method of measuring the spread of the data. Other measures of dispersion are. In such cases the sample standard deviation should be used. The range is the simplest measure of variability to calculate but can be misleading if the dataset contains extreme values.

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## 1.5. Measures of dispersion for ungrouped data Within the dataset the actual values usually differ from one another and from the average value itself. The interquartile range summarizes the spread or variation of values in a data set especially around the median. The relationship between mean median mode can also be considered to determine whether the data is asymmetric or skewed. Key Terms o Range o Measure of position o Percentile o Quartile Objectives o Determine the range of a data set o Know how to interpret and determine measures of position percentiles and quartiles While measures of central tendency, dispersion, and skewness are used often in statistics, there are other methods of characterizing or describing data distributions or portions that are commonly used as well. So what do we do? It is easy to calculate and understand. Deviation is not based on all observation therefore it is affected by extreme observations. In many datasets the values deviate from the mean value due to chance and such datasets are said to display a normal distribution.

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## Measures of variability: the range, inter However, the dispersion of the observations in the two samples differs greatly. Find and record the signed differences 3. On the basis of this average, we can say that the series are alike. The Inter-quartile Range The inter-quartile range is a measure that indicates the extent to which the central 50% of values within the dataset are dispersed. If the data is multiplied by the constant k, the mean and standard deviation will both be multiplied by k. Later it was discovered that the score 54 was misread as 64 in obtaining frequency distribution. But the real limit of this class is 150 — 155.

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## Measures of Dispersion Example : We continue with the same example to understand the computation of Standard Deviation. For example, if the series is expressed as Marks of the students in a particular subject; the absolute dispersion will provide the value in Marks. The prime advantage of this measure of dispersion is that it is easy to calculate. {1, 3, 3, 4, 6, 7, 7, 7, 8, 9, 9, 10, 12, 15, 16, 17} Solution: We want to find the data value N p for which 75% of the data set is less than or equal to N p. In the gjven problem if we take into consideration 99. Only one observation 67 is closer than 13 units to the mean of the second sample, and some are as far away as 30 units.

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## Measures of Dispersion Calculate the mean price of shoes 5. Similarly, symmetric data in statistics is considered to be reflective of complete information without any differences, fluctuations or changes. We can say that algebra is an extension of arithmetic. That is, take the absolute value of each of the differences. Typically, the 0th and 100th percentiles are not discussed, because these values are simply the minimum and maximum respectively of the data set. Thus to describe data, one needs to know the extent of variability.

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## 1.5. Measures of dispersion for ungrouped data The standard deviation is explored in the next section of this guide. Which formula you use depends upon whether the values in your dataset represent an entire population or whether they form a sample of a larger population. In order to reduce the problems caused by outliers in a dataset, the inter-quartile range is often calculated instead of the range. Like the range however, the inter-quartile range is a measure of dispersion that is based upon only two values from the dataset. Statistically, the standard deviation is a more powerful measure of dispersion because it takes into account every value in the dataset. In particular, if the distribution is bell-shaped, we can be fairly precise about what percentage of the data should lie within 1, 2, or 3 standard deviations. Such types of variability are also seen in the arena of manufactured products; even there, the meticulous scientist finds variation.

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