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The main measures of central tendency are the mean, the median and the mode. For a normal distribution, they are identical. For other distributions, they can vary quite a bit. Since the mode is the most-frequent element of the distribution, you can have more than one mode, which is not particularly helpful in most probability computations. The median is the level which 50% of the values are below (also known as the 50th percentile). The mean is the sum of the values divided by the number of values.
Between the median and the mode, the median is less variable, and so is generally a better measure of overall central tendency. However, when computing statistical probabilities, the mean is often more useful in the mathematical formulas, which are generally oriented toward computing the probability that a given value is different from a different value.
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Why are three different measures needed in math measures of central tendency and dispersion?
There are more than three measures. Some are better than others in some situations but not as good in other situations.
Why is arithmetic mean considered as the best measure of central tendency?
The arithmatic mean is not a best measure for central tendency.. It is because any outliers in the dataset would affect its value thus it is considered not a robust measure.. The mode or median however would be better to measure central tendency since outliers wont affect it value.. Consider this example : Arithmatic mean dan mode from 1, 5, 5, 9 is 5.. If we add 30 to the dataset then the arithmatic mean will be 10 but the mode will still same.. Mode is more robust than arithmatic mean..
Is median better indicator of central tendency of skewed distribution?
Yes. Central tendency is the way data clusters around a value. Even if the distribution of the value is skewed, the median would be the best indicator of central tendency because of the way the data is clustered.
What is the difference between range and spread in mathematics?
In Statistics, the measure of spread tells us how much adata sample is spread out or scattered. We can use the range and the interquartile range (IQR) to measure the spread of a sample. Measures of spread together with measures of location (or central tendency) are important for identifying key features of a sample to better understand the population from which the sample comes from. The range is the difference between a high number and the low number in the samples presented. It represents how spread out or scattered a set of data. It is also known as measures of dispersion or measures of spread.
Is the mean or median more accurate?
None of them is "more accurate". They are answers to two different questions.
Is the central trendency measured in Mean or Median?
Both are measures of central tendency. But, the mean has mathematical properties that are better understood.
How do you find the best measure of central tendency when using outliers?
A weighted mean is probably best. Certainly better than a median which throws away information from most of the observations.
Why are three different measures needed in math measures of central tendency and dispersion?
There are more than three measures. Some are better than others in some situations but not as good in other situations.
Why are there three measures of Central Tendency What if anything makes one better than the others?
Answer with an exampleIf you have a data series like: 1,2,2,3 What is the mean, median and mode of this thing:Mean= 2Median = 2Mode = 2Now if you have 1,2,2,3,1000Then we gotta have three measures asMode remains = 2Median shifts slightly toward threeMean becomes 201.6So you can see that three measures are required to capture the tendency of a data set in a more complete fashion.Although the mean does have this problem of being distorted by outliers (extreme values), it is the best measure of central tendency if more sophisticated analyses are required. This is because the properties of the mean, itself, are better understood. This means there is a wide range of powerful statistical techniques that can be applied to data using the mean.
Why is arithmetic mean considered as the best measure of central tendency?
The arithmatic mean is not a best measure for central tendency.. It is because any outliers in the dataset would affect its value thus it is considered not a robust measure.. The mode or median however would be better to measure central tendency since outliers wont affect it value.. Consider this example : Arithmatic mean dan mode from 1, 5, 5, 9 is 5.. If we add 30 to the dataset then the arithmatic mean will be 10 but the mode will still same.. Mode is more robust than arithmatic mean..
What is the appropriate measures of central tendency for ratio data?
iDK! do your homework! or even better! pay attention in class! i learned that the hard way.
For a skewed distribution what is a better indicator of the central tendency?
Mode
What is the purpose of using measures of central tendency and dispersion?
Given that the study manager wants the QC efforts to be focused on selecting outlier values, whose method is a better way of selecting the sample
Is median better indicator of central tendency of skewed distribution?
Yes. Central tendency is the way data clusters around a value. Even if the distribution of the value is skewed, the median would be the best indicator of central tendency because of the way the data is clustered.
Which one is better from all the measures of central tendencies?
mean
Is range is not considered to be an index of despersion?
The sample range could be used as an index of dispersion. However, there are objections. One is that this statistic is obviously sensitive to outliers. Another is that for many population distributions there are measures with much better characteristics, even ignoring the problem of outliers.
What is a number that helps describe all of the data in a data set?
There is no single number. There are several different measures of central tendency - different ones are better in different circumstances. Then there are several measures of spread or dispersion, skewness and so on. All of these are characteristics of the data and they cannot all be summarised by a single number.
