Solved Assignments Help NMIMS Calculate the Variance and the Standard Deviation Table Performance S

For answersheets contactinfo.answersheets@gmail.com91 95030-94040Decision Science1. Identify the type of the variable in the following tableTABLE GIVEN BELOW2. Following data of performance scores is available of employees working with a company. You are required to perform the following:a. Make the frequency distribution, Calculate the frequency and the Cumulative frequencyb. Calculate the mean, median, quartiles and Modec. Calculate the variance and the standard deviation Table: Performance score of the employees:TABLE BELOW3. a. In continuation with the data of performance scores of employees in previous example, perform the following: a. Calculate the range and interquartile rangeb. Calculate the z scoresc. Calculate the skewness and Kurtosis (using excel)d. Comment on the distribution of the data (5 Marks)3. b. In continuation with the data of performance scores of employees in previous example, perform the following:a. Make the histogramb. Plot the box-plot diagramc. Plot the frequency polygond. Plot the O give diagram (5 Marks)For answersheets contactinfo. answersheets@gmail. com91 95030-94040.

Math peoples, what is standard deviation?

The statistical 'normal' distribution for intelligence tests (and many other data) form a bell curve (shaped like a bell). The average (by the tester's definition) is the top center of the bell. A standard deviation (deviates from the center) by a certain amount (15) both to the left and right of center. Two standard deviations (30) would deviate from center (average) to points on the bell containing the vast majority of individuals. The geniuses will be to the far right and those mentally challenged will be to the far left (like politics...ha ha?)

Trig question on standard deviation?

Typically you can use a calculator or computer program such as Excel. If you have to do this by hand here are the steps: 1) First find the mean as the sum of all the numbers divided by their head count (in this case 10) 2) Find all the differences (x_i - m), where x_i is an individual number (i = 1, 2, ..., 10) and m is the fixed mean found in step 1. 3) Add up all the 10 squared differences (x_i - m)^2 4) Divide the result in step 3 by 10, (or by 9 which gives you the SD for the sample data.) 5) Take square root of the number in step 4 and round to the desired accuracy. I am sure you can do the number-crunching yourself. Here is a neat rule of thumb: The difference between the largest and mallest numbers in any data set should be about 4 times the standard deviation for that data set. In this case the rule of thumb gives us a standard deviation "approximately" equal to (94 - 78)/4 = 4, basically without any calculation.

why do women avoid this question or if they do they deviate from the point?

If a woman chooses to be a prostitute, and a man chooses to pay her, there is nothing inherently wrong

Question about an article by Nassim Nicholas Taleb about the problems with standard deviation.

Although as pointed by user452 MAD is a less sensitive statistic to outliers than the standard deviation, I think that N. Taleb has a different perspective. In fact, quite opposite to it.First, within the domain of Robust Statistics, outliers have always been considered as negative artifacts. Robust Statistics, like the median, avoid such distortions. Instead, N. Taleb considers outliers as providing information about the tails of the probability distributions (e.g. the crash of 1929, Black Monday of 1987, Dot-com bubble, etc.). So, N. Taleb is not advocating to use the MAD to reduce the effect of outliers, but precisely to be able to consider them as part of the analysis. Second, the existence of the standard deviation depends on the tail of the distribution to decrease at least as $O(x^-(2epsilon))$, and the variance and so the convergence of the corresponding estimator depends on the tail of the distribution to decrease at least as $O(x^-(4epsilon))$ (i.e. the kurtosis of the distribution should exist). In fact, there are many known natural and man-made processes that generate distributions with low exponents in the tails, also known fat-tailed or heavy-tailed. Therefore, the use of the standard deviation is technically dubious in many cases (and the same apply to the correlation, PCA, etc.).Third, from a epistemologically perspective, when the underlying process generating the data is not well-known, it is incorrect to assume that the process wo not be fat-tailed (i.e. absence of evidence is not the same as evidence of absence). Finally, in a decision-making scenario involving unbounded risks (e.g. financial ruin, wars, etc.), when the underlying process generating the data is not well-known, then we should take actions that avoid these unbounded risks. It does not make sense to take actions to reduce the probability of occurrence, because any repeated exposure to a small probability will certainly end in catastrophe (i. e. probability of catastrophe after N attempts $p_N$ grows as $(1-p)^N-1$, exponentially fast with the number of attempts).Given the above, MAD is a better estimator than standard deviation. However, for decisions involving unbounded risk with with not well-known processes, N. Taleb advocates even for the incorrectness of such estimates. Instead he advocates for precautionary principle.Although in draft form, a more mathematically oriented exposition of his ideas can be found in the Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications.

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