Question

1. EXPLORE the UF data variable ‘salary’ (3 pts). Copy & Paste the following information:
   1. Descriptives
   2. Outliers
   3. A stem-and-leaf plot

Answer 1

Answer:-

Now we have to say :

A) Descriptives:-

• A descriptive (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features of a collection of information, while descriptive statistics in the mass noun sense is the process of using and analyzing those statistics.
• Descriptive statistics is distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aims to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.
• This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently nonparametric statistics.
• Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented.
• For example, in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics
• Such as the average age, the proportion of subjects of each sex, the proportion of subjects with related comorbidities, etc.
• Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion.
• Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness.

B)  Outliers:-

• An Outliers is a perception point that is removed from other observations.
• An exception might be because of inconstancy in the estimation or it might show exploratory blunder; the last are some of the time avoided from the information set.
• An exception can cause difficult issues in measurable examinations.
• Anomalies can happen by chance in any appropriation, however they frequently demonstrate either estimation blunder or that the populace has an overwhelming followed circulation.
• In the previous case one wishes to dispose of them or use insights that are powerful to exceptions,
• while in the last case they demonstrate that the conveyance has high skewness and that one ought to be extremely careful in utilizing apparatuses or instincts that expect a typical dispersion.
• A regular reason for anomalies is a blend of two conveyances, which might be two unmistakable sub-populaces, or may designate 'right preliminary' versus 'estimation mistake'; this is demonstrated by a blend display.
• In most bigger samplings of information, a few information focuses will be further far from the example mean than what is esteemed sensible.
• This can be because of coincidental methodical blunder or imperfections in the hypothesis that produced an accepted group of likelihood appropriations, or it might be that a few perceptions are a long way from the focal point of the information.
• Anomaly focuses can in this manner demonstrate defective information, incorrect strategies, or territories where a specific hypothesis probably won't be legitimate.
• Be that as it may, in expansive examples, few exceptions is not out of the ordinary (and not because of any peculiar condition).
• Exceptions, being the most extraordinary perceptions, may incorporate the example greatest or test least, or both, contingent upon whether they are amazingly high or low.
• Be that as it may, the example most extreme and least are not generally exceptions since they may not be uncommonly a long way from different perceptions.

C) A stem-and-leaf plot:-

• Stem-and-leaf plots are a technique for demonstrating the recurrence with which certain classes of qualities happen.
• You could make a recurrence circulation table or a histogram for the qualities, or you can utilize a stem-and-leaf plot and let the numbers themselves to indicate basically a similar data.
• For example, assume you have the accompanying rundown of qualities: 12, 13, 21, 27, 33, 34, 35, 37, 40, 40, 41.
• You could make a steam and leaf plot of this data:

• stem-and-leaf plot

• 
• The "stem" is the left-hand section which contains the tens digits.
• The "leaves" are the rundowns in the right-hand segment, demonstrating every one of the ones digits for every one of the tens, twenties, thirties, and forties
• As should be obvious, the first qualities can even now be resolved; you can tell, from that base leaf, that the three qualities in the forties were 40, 40, and 41.

Note:- That the even leaves in the stem-and-leaf plot compare to the vertical bars in the histogram, and the leaves have lengths (regarding quantities of passages) that meet the numbers in the "Recurrence" segment of the recurrence table.