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If outliers are added to a dataset how would the variance and standard deviation change?
Wiki User
∙ 6y ago
They would both increase.
Wiki User
∙ 6y ago
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Which is more consistency arthematice mean is 110 and standard deviation is 25 and arthematic mean is 90 and standard deviation is 15?
The standard deviation is a number that tells you how scattered the data are centered about the arithmetic mean. The mean tells you nothing about the consistency of the data. The lower standard deviation dataset is less scattered and can be regarded as more consistent.
What is 1standard deviation below 100?
The standard deviation varies from one data set to another. Indeed, 100 may not even be anywhere near the range of the dataset.
What is paramtric and non-paramtreic?
In statistics, an underlying assumption of parametric tests or analyses is that the dataset on which you want to use the test has been demonstrated to have a normal distribution. That is, estimation of the "parameters", such as mean and standard deviation, is meaningful. For instance you can calculate the standard deviation of any dataset, but it only accurately describes the distribution of values around the mean if you have a normal distribution. If you can't demonstrate that your sample is normally distributed, you have to use non-parametric tests on your dataset.
A box plot is also known as a box-and-whisker plot.?
A box plot is a visual representation of the distribution of a dataset. It displays the minimum, first quartile, median, third quartile, and maximum values of the dataset. The "box" in the plot represents the interquartile range, while the "whiskers" represent the range of the data excluding outliers.
Math what is the range of the dataset-?
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Which is more consistency arthematice mean is 110 and standard deviation is 25 and arthematic mean is 90 and standard deviation is 15?
The standard deviation is a number that tells you how scattered the data are centered about the arithmetic mean. The mean tells you nothing about the consistency of the data. The lower standard deviation dataset is less scattered and can be regarded as more consistent.
What is 1standard deviation below 100?
The standard deviation varies from one data set to another. Indeed, 100 may not even be anywhere near the range of the dataset.
What determines the standard deviation to be high?
Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.
What is paramtric and non-paramtreic?
In statistics, an underlying assumption of parametric tests or analyses is that the dataset on which you want to use the test has been demonstrated to have a normal distribution. That is, estimation of the "parameters", such as mean and standard deviation, is meaningful. For instance you can calculate the standard deviation of any dataset, but it only accurately describes the distribution of values around the mean if you have a normal distribution. If you can't demonstrate that your sample is normally distributed, you have to use non-parametric tests on your dataset.
How would you describe a variance?
A variance is a statistical measure that quantifies the spread or dispersion of data points in a dataset. It indicates how much each data point differs from the mean of the dataset. A higher variance value suggests a wider spread of data points, while a lower variance value indicates a more clustered data distribution.
Why do we study the median?
The median is a more robust measure than the average, which means it is more resilient to the effects of outliers in your dataset.
What is an observation in a dataset?
It depends on the word usage (and what is being asked for). Usually, observation is the results of the experiment. In other words, experimental data. It can also refer to what the dataset shows you. For example, is there a significant deviation between the observed and expected results?
Why use standard deviation In what situations is it special?
I will restate your question as "Why are the mean and standard deviation of a sample so frequently calculated?". The standard deviation is a measure of the dispersion of the data. It certainly is not the only measure, as the range of a dataset is also a measure of dispersion and is more easily calculated. Similarly, some prefer a plot of the quartiles of the data, again to show data dispersal.t Standard deviation and the mean are needed when we want to infer certain information about the population such as confidence limits from a sample. These statistics are also used in establishing the size of the sample we need to take to improve our estimates of the population. Finally, these statistics enable us to test hypothesis with a certain degree of certainty based on our data. All this stems from the concept that there is a theoretical sampling distribution for the statistics we calculate, such as a proportion, mean or standard deviation. In general, the mean or proportion has either a normal or t distribution. Finally, the measures of dispersion will only be valid, be it range, quantiles or standard deviation, require observations which are independent of each other. This is the basis of random sampling.
Why need to remove outlier?
See related link. Outliers are defined as: In statistics, an outlier is an observation that is numerically distant from the rest of the data. As stated in the related link, outliers should not necessarily be removed from a dataset. If analyses is done, and certain values that are a part of the collected dataset are not included, then the reasons for excluding them from the analyses should be discussed along with the results. Erroneous values that are the result of human or measurement error, such as a particular sensor malfunctioning, may be excluded from analysis, as they are not representative numbers.
A box plot is also known as a box-and-whisker plot.?
A box plot is a visual representation of the distribution of a dataset. It displays the minimum, first quartile, median, third quartile, and maximum values of the dataset. The "box" in the plot represents the interquartile range, while the "whiskers" represent the range of the data excluding outliers.
What is the normal distribution for clothing sizes?
You can find regulations about clothing sizes in the EN 13402 standard, and a series of physical measurements in the SIRI-dataset. Reading the standard, I see that t-shirt sizes (men), for example, are mainly based on chest circumferences. Size 'M' is suitable for chest circumferences between 94 and 102 cm. Size S is 8 cm smaller, size L is 8 cm bigger, XL is 16 cm bigger and XS is 16 cm smaller than size M. When I calculate the median and standard deviation of all the chest circumferences (adult males) I find in the SIRI-dataset, I find a median of 99.6 cm, and - surprise - a standard deviation of 8.4 cm. So, I tend to believe that clothing sizes follow, in some way, the normal distribution. Size M refers to the median size, and the intervals between the size codes have about the same value as the standard deviation. So, size S is one standard deviation smaller than size M, and XL is two standard deviations bigger than size M. If haven't checked other types of clothes and other physical sizes, so I cannot guarantee that my conclusion is correct for any type of garment.
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..
