# Basic Statistics and Data Analysis

## Standard Deviation

The standard deviation is a widely used concept in statistics and it tells how much variation (spread or dispersion) is in the data set. It can be defined as the positive square root of the mean (average) of the squared deviations of the values from their mean.
To calculate the standard deviation one have to follow these steps:

1. First, find the mean of the data.
2. Take the difference of each data point from the mean of the given data set (which is computed in step 1). Note that, the sum of these differences must be equal to zero or near to zero due to rounding of numbers.
3. Now computed the square the differences that were obtained in step 2, It would be greater than zero, that it, I will be a positive quantity.
4. Now add up all the squared quantities obtained in step 3. We call it the sum of squares of differences.
5. Divide this sum of squares of differences (obtained in step 4) by the total number of observation (available in data) if we have to calculate population standard deviation ($\sigma$). If you want t to compute sample standard deviation ($S$) then divide the sum of squares of differences (obtained in step 4) by the total number of observation minus one ($n-1$) i.e. the degree of freedom. Note $n$ is the number of observations available in the data set.
6. Find the square root (also known as under root) of the quantity obtained in step 5. The resultant quantity in this way known as the standard deviation for given data set.

The sample standard deviation of a set of $n$ observation, $$X_1, X_2, \cdots, X_n$$ denoted by $S$ is
\begin{aligned}
\sigma &=\sqrt{\sum_{i=1}^n \frac{X_i-\overline{X}}{n}}; Population\, Standard\, Deviation\\
S&=\sqrt{\sum_{i=1}^n \frac{X_i-\overline{X}}{n-1}}; Sample\, Standard\, Deviation
\end{aligned}
The standard deviation can be computed from variance too as $S= \sqrt{Variance}$.

The real meaning of the standard deviation is that for a given data set 68% of the data values will lie within the range $\overline{X} \pm \sigma$ i.e. within one standard deviation from mean or simply within one $\sigma$. Similarly, 95% of the data values will lie within the range $\overline{X} \pm 2 \sigma$ and 99% within $\overline{X} \pm 3 \sigma$.

Examples of Standard Deviation and Variance

A large value of standard deviation indicates more spread in the data set which can be interpreted as the inconsistent behaviour of the data collected. It means that the data points tend to away from the mean value. For the case of smaller standard deviation, data points tend to be close (very close) to mean indicating the consistent behaviour of data set.
The standard deviation and variance both are used to measure the risk of a particular investment in finance. The mean of 15% and standard deviation of 2% indicates that it is expected to earn a 15% return on an investment and we have 68% chance that the return will actually be between 13% and 17%. Similarly, there are 95% chance that the return on the investment will yield an 11% to 19% return.

## Classical Probability: Example, Definition, and Uses in Life

Classical probability is the statistical concept that measures the likelihood (probability) of something happening. In a classic sense, it means that every statistical experiment will contain elements that are equally likely to happen (equal chances of occurrence of something). Therefore, the concept of classical probability is the simplest form of probability that has equal odds of something happening.

## Classical Probability Examples

Example 1: The typical example of classical probability would be rolling of a fair dice because it is equally probable that top face of die will be any of the 6 numbers on the die: 1, 2, 3, 4, 5, or 6.

Example 2: Another example of classical probability would be tossing an unbiased coin. There is an equal probability that your toss will yield either head or tail.

Example 3: In selecting bingo balls, each numbered ball has an equal chance of being chosen.

Example 4: Guessing a multiple choice quiz (MCQs) test with (say) four possible answers A, B, C or D. Each option (choice) has the same odds (equal chances) of being picked (assuming you pick randomly and do not follow any pattern).

## Formula for Classical Probability

The probability of a simple event happening is the number of times the event can happen, divided by the number of possible events (outcomes).

Mathematically $P(A) = \frac{f}{N}$,

where, $P(A)$ means “probability of event A” (event $A$ is whatever event you are looking for, like winning the lottery, that is event of interest), $f$ is the frequency, or number of possible times the event could happen and $N$ is the number of times the event could happen.

For example,  the odds of rolling a 2 on a fair die are one out of 6, (1/6). In other words, one possible outcome (there is only one way to roll a 1 on a fair die) divided by the number of possible outcomes.

Classical probability can be used for very basic events, like rolling a dice and tossing a coin, it can also be used when occurrence of all events is equally likely. Choosing a card from a standard deck of cards gives you a 1/52 chance of getting a particular card, no matter what card you choose. On the other hand, figuring out will it rain tomorrow or not isn’t something you can figure out with this basic type of probability. There might be a 15% chance of rain (and therefore, an 85% chance of it not raining).

## Other Examples of classical Probability

There are many other examples of classical probability problems besides rolling dice. These examples include flipping coins, drawing cards from a deck, guessing on a multiple choice test, selecting jellybeans from a bag, and choosing people for a committee, etc.

## Classical Probability cannot be used:

Dividing the number of events by the number of possible events is very simplistic, and it isn’t suited to finding probabilities for a lot of situations. For example, natural events like weights, heights, and test scores need normal distribution probability charts to calculate probabilities. In fact, most “real life” things aren’t simple events like coins, cards, or dice. You’ll need something more complicated than classical probability theory to solve them.

## Skewness: Measure of Asymmetry

The skewed and askew are widely used terminologies that refer to something that is out of order or distorted on one side. Similarly, when referring to the shape of frequency distributions or probability distributions, the term skewness also refers to asymmetry of that distribution. A distribution with an asymmetric tail extending out to the right is referred to as “positively skewed” or “skewed to the right”, while a distribution with an asymmetric tail extending out to the left is referred to as “negatively skewed” or “skewed to the left”. The range of skewness is from minus infinity ($-\infty$) to positive infinity ($+\infty$). In simple words skewness (asymmetry) is measure of symmetry or in other words skewness is the lack of symmetry.

Karl Pearson (1857-1936) first suggested measuring skewness by standardizing the difference between the mean and the mode, such that, $skewness=\frac{\mu-mode}{\text{standard deviation}}$. Since, population modes are not well estimated from sample modes, therefore Stuart and Ord, 1994 suggested that one can estimate the difference between the mean and the mode as being three times the difference between the mean and the median. Therefore, the estimate of skewness will be: $skewness=\frac{3(M-median)}{\text{standard deviation}}$. Many of the statisticians use this measure but after eliminating the ‘3’, that is, $skewness=\frac{M-Median}{\text{standard deviation}}$. This statistic ranges from $-1$ to $+1$. According to Hilderand, 1986, absolute values of skewness above 0.2 indicate great skewness.

Skewness has also been defined with respect to the third moment about the mean, that is $\gamma_1=\frac{\sum(X-\mu)^3}{n\sigma^3}$, which is simply the expected value of the distribution of cubed $Z$ scores. Skewness measured in this way is also sometimes referred to as “Fisher’s skewness”. When the deviations from the mean are greater in one direction than in the other direction, this statistic will deviate from zero in the direction of the larger deviations. From sample data, Fisher’s skewness is most often estimated by: $g_1=\frac{n\sum z^3}{(n-1)(n-2)}$. For large sample sizes ($n > 150$), $g_1$ may be distributed approximately normally, with a standard error of approximately $\sqrt{\frac{6}{n}}$. While one could use this sampling distribution to construct confidence intervals for or tests of hypotheses about $\gamma_1$, there is rarely any value in doing so.

Arthur Lyon Bowley (1869-19570, has also proposed a measure of skewness based on the median and the two quartiles. In a symmetrical distribution, the two quartiles are equidistant from the median but in an asymmetrical distribution, this will not be the case. The Bowley’s coefficient of skewness is $skewness=\frac{q_1+q_3-2\text{median}}{Q_3-Q_1}$. Its value lies between 0 and $\pm1$.

The most commonly used measures of skewness (those discussed here) may produce some surprising results, such as a negative value when the shape of the distribution appears skewed to the right.

It is important for researchers from the behavioral and business sciences to measure skewness when it appears in their data. Great amount of skewness may motivate the researcher to investigate the existence of outliers. When making decisions about which measure of location to report and which inferential statistic to employ, one should take into consideration the estimated skewness of the population. Normal distributions have zero skewness. Of course, a distribution can be perfectly symmetric but may far away from normal distribution. Transformations of variables under study commonly employed to reduce (positive) skewness. These transformation may include square root, log, and reciprocal of variable.

## Creating Formula in Excel: Operators Order of Precedence

Creating customized (user defined) formulas in Microsoft Excel is not too difficult. For creating formulas just combine the references of your data with the correct mathematical operator (such as -, +, /, * and ^).

Microsoft Order of Precedence

The order of mathematical operations determines in which order the mathematical operations are carried out. If more than mathematical operators are used in formula, there is a specific order (sequence) that Microsoft Excel will follow to perform (compute) these mathematical operations. However, to change the order of operations, brackets (parenthesis) are used in the Excel formula. The easy way to remember the order of operations (precedence) is to remember the acronym: BEDMAS (PEDMAS), that i.e.,

The order of operations (precedence) is:

Bracket or Parenthesis
Exponents (^)
Division (/)
Multiplication (*)
Subtraction (-)

Suppose, following is the screenshot of an Excel sheet. The formula is also shown in formula bar. As an example, addition (+), division (/) and multiplication (*) operators are used.

The formula in screenshot performs the computation in the following order,

• E1/F1 will be computed (answer is 1.5),
• the answer of E1/F1 will be multiplied by value of G1 (answer is 1.5*2 = 3)

Any operation(s) enclosed in brackets (parenthesis) will be carried out first followed by any exponents. After that, Excel will consider division or multiplication operations to be of equal importance. The operations are performed in the order they occur left to right in the formula. Similar sequence is also performed for addition and subtraction. Both (addition and subtraction) are considered equal in the order of operations. The operator which appears first will be computed first.

For example, see the screenshot The sequence of operation is

• First bracket will be computed, that is, multiplication will be performed (2 *2 = 4)
• E1 will be divided by the answer from multiplication of F1 and G1 (3/4 = 0.75)
• Lastly D1 will be added to the answer 0.75 (4 + 0.75 = 4.75)

Now check the sequence in the following screenshot

For Creating formula in Excel, see the link Creating Excel Formula

## Convert PDFs to Editable File Formats in 3 Easy Steps

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One huge part of modern technology are digital documents that are a legacy of digital revolution. Paper documents have been replaced by digital files at one point, since they are easier to use, edit and share between colleagues and friends.

One of the most used and known digital file formats is Portable Document Format, better known as the PDF. Developed and published in the nineties, the PDF is still a number one format for managers, students, accountants, writers and many others. For more than 20 years it has been building up supporters, who use it for 3 main reasons:

1. It’s universal — it can be opened on any device (including mobile devices).
2. It’s shareable — documents are easily shared across all platforms.
3. It’s standardized — the files always maintain original formatting.

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