Definitions [6]
The arithmetic mean (or, simply, mean) of a set of numbers is obtained by dividing the sum of the numbers in the set by the number of numbers.
\[\mathbf{Mean}=\frac{\left(x_1+x_2+x_3+\ldots+x_n\right)}{n}=\frac{\Sigma x_i}{n}\]
Median is the value of the middle-most observation(s). The median is a measure of central tendency which gives the value of the middle-most observation in the data.
The mode is the value of the observation that occurs most frequently; i.e., the observation with the maximum frequency is called the mode.
Probability measures the degree of certainty of the occurrence of an event.
A sequence of dichotomous experiments is called a sequence of Bernoulli trials if it satisfies the following conditions:
- The trials are independent.
- The probability of success remains the same in all trials.
A discrete random variable X is said to have the Poisson distribution with parameter m > 0, if its p.m. is given by
\[P(X=x)=\frac{e^{-m}m^x}{x!}\] = 0, 1, 2, ....
Formulae [4]
Direct Method:
\[\bar{x}=\frac{\sum f_ix_i}{\sum f_i}\]
where xi = class mark, fi = frequency
Short-cut (Assumed Mean) Method:
\[\bar{x} = A+\frac{\sum f_id_i}{\sum f_i}\]
where di = xi - A
A is the assumed mean
Step-deviation Method:
\[\bar{x}=a+h\frac{\sum f_iu_i}{\sum f_i}\]
where \[u_i=\frac{x_i-a}{h}\]
h is the class width / common factor
If the number of data points (n) is odd, the median is,
Median = `((n+1)/2)^(th)` term
If n is even, the median is the average of the values at positions
Median = Average of `(n/2)^(th)` and `(n/2+1)^(th)` values
G.M. between a and b
G2 = ab
G =\[\sqrt{ab}\]
G is the geometric mean between a and b.
Key Points
| No. | Term | Definition |
|---|---|---|
| 1 | Probability | A measure of the chance of occurrence of an event. |
| 2 | Random Experiment | An experiment in which all possible outcomes are known, but the exact outcome cannot be predicted with certainty. |
| 3 | Outcome | The result of a random experiment. |
| 4 | Sample Space (S) | The set of all possible outcomes of a random experiment. |
| 5 | Sample Point | Each element of the sample space. |
| 6 | Number of Sample Points | The number of elements in the sample space is denoted by n(S). |
| 7 | Equally Likely Outcomes | Outcomes which have the same chance of occurring. |
| No. | Term | Definition |
|---|---|---|
| 1 | Probability | A measure of the chance of occurrence of an event. |
| 2 | Random Experiment | An experiment in which all possible outcomes are known, but the exact outcome cannot be predicted with certainty. |
| 3 | Outcome | The result of a random experiment. |
| 4 | Sample Space (S) | The set of all possible outcomes of a random experiment. |
| 5 | Sample Point | Each element of the sample space. |
| 6 | Number of Sample Points | The number of elements in the sample space is denoted by n(S). |
| 7 | Equally Likely Outcomes | Outcomes which have the same chance of occurring. |
Playing Cards – Key Facts
-
Total cards = 52
-
Red cards = 26 (Hearts, Diamonds)
-
Black cards = 26 (Clubs, Spades)
-
Each suit has 13 cards
-
Face cards = King, Queen, Jack (Total = 12)
| Type of Event | Meaning | Probability |
|---|---|---|
| Sure (Certain) Event | An event that is certain to occur | P(E) = 1 |
| Impossible Event | An event that cannot occur | P(E) = 0 |
| Simple (Elementary) Event | An event having only one outcome | P(E) = 1 / n(S) |
| Complementary Event (E̅) | An event that occurs when E does not occur | P(not E) = 1 − P(E) |
| Mutually Exclusive Events | Two events that cannot occur together | P(A ∩ B) = 0 |
| Exhaustive Events | Events which together cover all outcomes of S | P(A₁) + P(A₂) + … = 1 |
| Equally Likely Events | All outcomes have the same chance of occurring | P(E) = n(E) / n(S) |
| General Rule | Probability of any event | 0 ≤ P(E) ≤ 1 |
Concepts [24]
- Measures of Discretion
- Arithmetic Mean
- Mean of Grouped Data
- Basic Concept of Median
- Basic Concept of Mode
- Standard Deviation
- Variance
- Mean Deviation
- Geometric Mean
- Harmonic Mean (H.M.)
- Coefficient of Variation
- Addition Theorem of Probability
- Multiplication Theorem on Probability
- Bayes’ Theorem
- Binomial Distribution
- Concept of Probability
- Elementary Types of Events and Properties of Probability
- Odds in Favour and Against
- Boole's Inequality
- Demorgan's Law
- Independent Events
- Conditional Probability
- Probability Distribution of Discrete Random Variables
- Poisson Distribution
