0
median
Wiki User
∙ 11y ago
Add your answer:
Earn +20 pts
How is frequency distribution useful to us?
I suspect you are referring to a sample frequency distribution.Providing that the sample size is sufficiently large there are various kinds of information that can be gleaned from one:the approximate range of values in the populationthe location of the population as measured by the value that appears most often in the frequency distribution-known as its modethe likely shape of the population's distribution, in particular whether it is symmetric or skewedobviously how values of the population variable are distributedwhether there are any curious peaks or valleys, even when the sample size is largethe amount of variation around the central value
What central tendency should be used when the item being studied is constantly fluctuating for example daily temperature?
That depends entirely upon what is varying. For instance - in your example of daily temperature - average would be meaningless and be of little use, but daily maximum and daily minimum would be useful. To use the average of anything that is fluctuating is not easy as you have to define a sample rate and be sure that it is meaningful.
Why do we study dispersion?
It is not enough to know only the mean or some other measure of the central tendency: it is useful to know the dispersion. If, in a test, the average score is 50 and you score 52 you have clearly scored better than average but how much better? If the scores range from 0 to 100, you are pretty close to average whereas if they range from 48 to 52 you are amongst the top of the class!
What is the mean of the sampling distribution of the sample mean?
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
Why is it a useful value to report standard deviation?
The standard deviation of a distribution is the average spread from the mean (average). If I told you I had a distribution of data with average 10000 and standard deviation 10, you'd know that most of the data is close to the middle. If I told you I had a distrubtion of data with average 10000 and standard deviation 3000, you'd know that the data in this distribution is much more spread out. dhaussling@gmail.com
When is finding the average not useful?
There is no meaningful average wen data are categorical (qualitative). Also, the arithmetic mean is not a good measure of central tendency when the data distribution is skewed.
What measure of central tendency would be most useful in describing the gender of a sample?
Since gender is a qualitative variable, the mode is the only one of the main measures of central tendency.
Why is the mode not used very much as a measure of central tendency?
The mode is the value with the most data. Unfortunately it tells you nothing about the rest of the distribution. The mean and median provide more useful information in most situations.
When is each measure of central tendency most useful?
Mode: Data are qualitative or categoric. Median: Quantitative data with outliers - particularly if the distribution is skew. Mean: Quantitative data without outliers, or else approx symmetrical.
The Pearson's coefficient of skewness is a measure of distribution's symmetry?
It is a descriptive statistical measure used to measure the shape of the curve drawn from the frequency distribution or to measure the direction of variation. It is a measure of how far positively skewed (below the mean) or negatively skewed (above the mean) the majority (that's where the mode comes in) of the data lies. Useful when conducting a study using histograms. (mean - mode) / standard deviation. or [3(Mean-Median)]/Standard deviation
What is descriptive statistics?
Descriptive Statistics analyzes the quantitative data in detail in measuring the outcome. It eliminate all that is secondary and highlights the main statistics. It is a single indicator put in summary of the individual data. It provides consistent summary useful for the comparison and is very helpful for the future planning. In indicates the data in units. Each single unit of descriptive is reduced into a simple summary indicating the units of frequency. The data is measured under units of frequency such as the distribution , the central tendency , and the dispersion The distribution chart is analyzed through the bar chart, The central tendency is an analysis into mean, median and mode form Mean is the average of most probably used frequency, the median is the central point of the set values, Mode is the most frequently occurring value in the set of scores. Dispersion indicates the spread of the values regarding the central tendency.
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.
How do we obtain useful information from a set of data?
You calculate summary statistics: measures of the central tendency and dispersion (spread). The precise statistics would depend on the nature of the data set.
What are some characteristics mode?
Mode is the most frequent value in a dataset. It is a measure of central tendency along with mean and median. Mode is useful for representing the typical value or category in a dataset.
What is Central tendecy?
There are three statistical measures of "central tendency" - mean, median and mode. Combined, they give a picture of how close the data values cluster around a single "average" value. Normally, when someone talks of AVERAGE they are talking about the MEAN - where you add all the values and divide by the number of data points. But that value can be greatly affected by extreme values (e.g., the Mean of the following numbers: 3, 4, 4, 5, 4, 3, 27, 4, and 4 would be skewed by that one value that is not close to most of the others). The MODE of the numbers I gave, however, is the value that occurs most frequently - 4. The MEDIAN, the point where half the values are higher and half are lower, would also be 4. So, you see, the Central Tendency would be toward the value 4 and there is a strong Central tendency in this set of values. You could have a different set of numbers (e.g., 3, 27, 118, 11, 2, 963, 48) and while you could calculate an arithmetic mean, you could see that it wouldn't be too useful since there is no real Central Tendency of the data.
What are the advantages and disadvantages of presenting a relationship between two variables in a graph?
Advantages:The nature of the relationship is generally easier to see: linear, polynomial, exponential, power etc;The range and domain of the data sets are immediately apparent,Estimation for intermediate values (interpolation) is relatively straightforward.Disadvantages:Not very useful if the observations are unevenly spread over a large range or domain (very skewed distribution).Exact values not given,Exact parameters describing relationship not available,Tendency of naive users to extrapolate.
A bar graph is useful to?
show distribution of data.
