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The cause of skewed data distributions are extreme values, also know as outliers. For example imagine taking the weights of people you see on the street. If you have 9 cheerleaders' weights and then the weight of a sumo wrestler mixed into the averages this skews the data. This makes the mean much higher because of the one extreme value. Instead of the data being distributed normally, it is distributed with a positive skew. If there is a really small extreme value instead of a really large one, then the data has a negative skew. This could be the heights of people on the street, but one of them would be a midget. The mean is made lower by that one extreme value.
Perhaps, little person is a more politically correct term in our day.
What else can I help you with?
Is it true some normal probability distributions are positively skewed?
No. The Normal distribution is symmetric: skewness = 0.
What is the math definition of experimental?
The word "experimental" is usually used to describe data that have come from an actual test or experiment. These data are opposite to "theoretical" data, which are only educated guesses at what the data should look like. In statistics, theoretical probability is used a lot. For example, if I flip a coin, in theory, it would land on each side half of the time. Perform some trials, however, and this percentage may be skewed. The experimental data that you collect probably wouldn't exactly match the theoretical probability.
All graphs must have what?
Some data.
Mode is 100 and mean is less than the median?
Well, isn't that just a happy little coincidence! When the mode is 100, it means that value appears most frequently. And when the mean is less than the median, it suggests that the data is skewed to the left. Just remember, every piece of data adds to the beauty of the whole picture.
When some arithmetic or logical operations are performed on data it is called?
I believe the term "data processing" is appropriate in this case.
Is it true some normal probability distributions are positively skewed?
No. The Normal distribution is symmetric: skewness = 0.
How to identify the the shape of probability distribution?
You cannot. There are hundreds of different distributions. The shapes of the distributions depend on their parameters so that the same distribution can be symmetric when the parameters have some specific value, but is highly skewed - in either direction - for other values.
What are some benefit of using graphs of frequency distributions?
Graphs of frequency distributions provide a clear visual representation of data, making it easier to identify patterns, trends, and outliers. They facilitate quick comparisons between different data sets and help in understanding the overall distribution shape, such as normal, skewed, or bimodal. Additionally, these graphs enhance communication of statistical findings, making complex data more accessible to a broader audience. Overall, they serve as effective tools for both analysis and presentation of data.
What are some benefits of representing data sets using frequency distributions?
Organizing the data into a frequency distribution can make patterns within the data more evident.
Why do you not always use the median?
The median is not always used because it may not accurately represent the data distribution in certain contexts. For example, in skewed distributions, the median can provide a better measure of central tendency than the mean, but in normally distributed data, the mean may be more informative. Additionally, in some analyses, the mean can be more sensitive to changes in data, making it more useful for specific statistical tests. Ultimately, the choice between median and mean depends on the nature of the data and the analysis goals.
Which is the most stable measure of central tendency?
Stability means that there will be less variation between random samples drawn on the same population. With categorical data, you may not have a choice, the mode is the only measure of central tendency that will be meaningful. With measureable, numerical data, the mean may be the only meaningful measure of central tendency, even though the median may show less variation. Some data may be assumed to have a skewed distribution, such as the price of homes, or incomes. The more stable and meaningful value for skewed distributions is the median, as a few high numbers can have a large impact on the estimate. See related links. You can find more information on central tendency by doing a search on the internet.
Why do data distributions take a particular shape?
If you plot data they must take some shape (or another)! Data distributions can take all kinds of shapes. The only constraints are thatthey cannot be negative andthe integral (sum) over all possible values is 1.The shapes can be flat (uniform distribution), symmetric (uniform or Gaussian), asymmetric with one peak somewhere in the middle (Poisson), asymmetric with a peak at an end (exponential). These are examples of different shapes that are attained by common continuous data distributions.
Is it sometimes always or never true that if the mean of a set of data is greater then the mode n?
It is not always true that if the mean of a set of data is greater than the mode, the mean will consistently be greater than the mode across all data sets. While in some distributions, particularly those that are positively skewed, the mean can be greater than the mode, this relationship can vary based on the distribution's characteristics. For example, in a symmetric distribution, the mean and mode can be equal. Thus, the statement can be true in certain cases but not universally applicable.
Why are intervals used to organize some data?
Intervals are used to organize data to simplify analysis and interpretation by grouping continuous data into manageable ranges. This helps highlight trends, patterns, and distributions within the data, making it easier to identify relationships and draw conclusions. Additionally, using intervals reduces the complexity of data presentation, allowing for clearer visual representation, such as histograms or frequency distributions. Overall, intervals facilitate more effective communication of information derived from large datasets.
What does probalility distribution mean?
In parametric statistical analysis we always have some probability distributions such as Normal, Binomial, Poisson uniform etc.In statistics we always work with data. So Probability distribution means "from which distribution the data are?
What are some benefits of using graphs of frequency distributions?
Graphs of frequency distributions provide a clear visual representation of data, making it easier to identify patterns, trends, and outliers. They simplify complex data sets, allowing for quick comparisons between different groups or categories. Additionally, such graphs can enhance understanding and communication of statistical concepts, making them accessible to a broader audience. Overall, they serve as valuable tools for data analysis and interpretation.
Why some Linux distributions not free?
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