# SECTION VI - CHAPTER 40 - Concept of Probablity

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### SECTION VI - CHAPTER 40 - Concept of Probablity

• 1. Module - 59 Introduction to Accounting CMT LEVEL - I
• 2. Probability • The total probabilities of an event occurring or not will always equal 100 percent. If you have a 10 percent probability that something may happen, then you have a 90 percent probability that it won’t. • The simplest example is the coin toss. You have a 50 percent probability that the coin will land on either side because only two options exist. • When you apply probability theory to the standard deviation, you end up with something called a normal distribution.
• 3. Probability • Probability: chance of an event taking place Rules relating to probabilities • If an out come is definitely certain to happen, its probability is 1 • The probability of an event happening would mostly lie between 0 and 1 • Sum of probabilities of an event happening must be equal to 1 • Probability can never be a negative number • Possible outcomes must be mutually exclusive
• 4. Probability Distribution • A probability Distribution is a table showing the various possible outcomes of an event along with their respective probabilities • Normal Probability Distribution is a bell shaped curve, and is perfectly symmetric around the expected rate of return which is in the middle Band Probability ± One standard derivation (ó) 68.3% ± Two standard Derivation (ó) 95.4% ± Three standard deviation (ó) 99.7%
• 5. Skewness • The third central moment is a measure of the lopsidedness of the distribution; any symmetric distribution will have a third central moment, if defined, of zero. •The normalised third central moment is called the skewness, often γ. • A distribution that is skewed to the left (the tail of the distribution is longer on the left) will have a negative skewness.
• 6. Skewness • A distribution that is skewed to the right (the tail of the distribution is longer on the right), will have a positive skewness. • For a calculated skew number (average cubed deviations divided by the cubed standard deviation), • For evaluate whether a return is positively skewed (skew > 0), negatively skewed (skew < 0) or symmetric (skew = 0).
• 7. Kurtosis •Kurtosis refers to the degree of peak in a distribution. More peak than normal (leptokurtic) means that a distribution also has fatter tails and that there are lesser chances of extreme outcomes compared to a normal distribution. •It is sometimes referred to as the "volatility of volatility.“
• 8. Kurtosis • A statistical measure used to describe the distribution of observed data around the mean. • Used generally in the statistical field, kurtosis describes trends in charts. • A high kurtosis portrays a chart with fat tails and a low, even distribution, whereas a low kurtosis portrays a chart with skinny tails and a distribution concentrated toward the mean.