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Do outliers make some measures of central tendency better than others?
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.
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..
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.
How the values of mean and median relate to shape?
The question is how do the mean and median affect the distribution shape. In a normal curve, the mean and median are both in the same point. ( as is the mode) If a distribution is skewed, its tail is either on the right or the left. If a distribution is skewed the median may be a better value to use than the mean since it has less effect on the shape. Also is there are large outliers, the median has less effect and is better to use. So the mean has a bigger effect on the shape many times than the median.
What is the purpose of a stem and leaf plot?
to simply organise your numbers.ajm If you can make a histogram, a dotplot, or even a boxplot; there is no reason to do a steam and leaf plot. It's the worst graph. With a stem and leaf graph, you can see the distribution of data points, and determine whether it's normal distribution or not. As mentioned above, there are better graphs for doing that, though.
For a skewed distribution what is a better indicator of the central tendency?
Mode
Is Mean is more sensitive to skewness than the median?
If the distribution is positively skewed , then the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency (If it is a uni-modal distribution). If the distribution is negatively skewed then mean will always be the lowest estimate of central tendency and the mode will be the highest estimate of central tendency. In both positive and negative skewed distribution the median will always be between the mean and the mode. If a distribution is less symmetrical and more skewed, you are better of using the median over the mean.
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.
Do outliers make some measures of central tendency better than others?
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.
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.
When is median a better indicator of central tendency?
Because it is less influenced by occasional numbers which are very far from the middle. For example, take the series: 1, 1, 10, 10 10, 10, 10, 10, 10, 10, 1099999 You would think that the 'middle' is about 10. The median is 10 but the mean is about 100000 !
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 Normal distribution is better then other distributions in statistics?
The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.
Explain how you could create a distribution of means by taking a large number of samples of four individuals each?
As the sample size increases, and the number of samples taken increases, the distribution of the means will tend to a normal distribution. This is the Central Limit Theorem (CLT). Try out the applet and you will have a better understanding of the CLT.
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..
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.
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
