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How standard deviation and Mean deviation differ from each other?
There is 1) standard deviation, 2) mean deviation and 3) mean absolute deviation. The standard deviation is calculated most of the time. If our objective is to estimate the variance of the overall population from a representative random sample, then it has been shown theoretically that the standard deviation is the best estimate (most efficient). The mean deviation is calculated by first calculating the mean of the data and then calculating the deviation (value - mean) for each value. If we then sum these deviations, we calculate the mean deviation which will always be zero. So this statistic has little value. The individual deviations may however be of interest. See related link. To obtain the means absolute deviation (MAD), we sum the absolute value of the individual deviations. We will obtain a value that is similar to the standard deviation, a measure of dispersal of the data values. The MAD may be transformed to a standard deviation, if the distribution is known. The MAD has been shown to be less efficient in estimating the standard deviation, but a more robust estimator (not as influenced by erroneous data) as the standard deviation. See related link. Most of the time we use the standard deviation to provide the best estimate of the variance of the population.
How do you calculate probability given mean and standard deviation?
The mean and standard deviation do not, by themselves, provide enough information to calculate probability. You also need to know the distribution of the variable in question.
What is the sample variance with the mean of 190.3?
The mean, by itself, does not provide sufficient information to make any assessment of the sample variance.
Why to take square in the formula of standard deviation?
The sum of deviations from the mean will always be 0 and so does not provide any useful information. The absolute deviation is one solution to tat, the other is to take the square - and then take a square root.
What is one standard deviation above the mean 700?
The mean alone is not enough to provide an answer.
What is the Disadvantage of means deviation?
The disadvantage is that the mean deviation of a set of data is always zero and so does not provide any useful information.
What does standard deviation provide when measuring the range of possible outcomes of a distribution?
It is a measure of the spread of the outcomes around the mean value.
Does the variance provide a succinct summary of raw data?
Your question is a bit difficult to answer, as "succinct" is usually a quality in reference to a description or explanation. It is defined by Webster's dictionary as "marked by compact precise expression without wasted words." See related link. For this reason, I have reworded your question as follows: Does the variance fully describe or summarize the raw data? The answer is no. For any set of data, many statistical measures can be calculated, including the mean and variance. The variance or more commonly the square of the variance (standard deviation) is a very useful in identifying the dispersion of data, but is incomplete in fully describing the data. The mean is also important. Graphs can improve the summarization of data in a more visual manner.
If the mean is 1050 and the standard deviation is 218 what is the conpsumption level separating the bottom 45 percent from the top 55 percent?
The answer will depend on what the distribution is! And since you have not bothered to share that crucial bit of information, I cannot provide a more useful answer.
How does a sample size impact the standard deviation?
If I take 10 items (a small sample) from a population and calculate the standard deviation, then I take 100 items (larger sample), and calculate the standard deviation, how will my statistics change? The smaller sample could have a higher, lower or about equal the standard deviation of the larger sample. It's also possible that the smaller sample could be, by chance, closer to the standard deviation of the population. However, A properly taken larger sample will, in general, be a more reliable estimate of the standard deviation of the population than a smaller one. There are mathematical equations to show this, that in the long run, larger samples provide better estimates. This is generally but not always true. If your population is changing as you are collecting data, then a very large sample may not be representative as it takes time to collect.
How are the Wechsler Intelligence Scales scored?
The Wechsler Intelligence Scales are scored by comparing an individual's raw scores on various subtests to a normative sample of the same age group. These raw scores are then converted into standard scores (with a mean of 100 and a standard deviation of 15) for each subtest, as well as composite scores such as the Full Scale IQ score. The final scores can provide valuable information about an individual's cognitive abilities in comparison to their peers.
What is the purpose of calculating the mean and the variance?
Calculating the mean helps to understand the central tendency of a data set, while calculating the variance provides information about the spread or dispersion of the data points around the mean. Together, the mean and variance provide a summary of the data distribution, enabling comparisons and making statistical inferences.
