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Probability and Statistics ( BTech CSE )
- Ungrouped Data
- Grouped Data
- Relation between Mean, Median and Mode
- Relation between raw and central moments
- Skewness and Kurtosis
- Basic Probability
Ungrouped Data
Ungrouped data is data that has not been arranged in any way.So it is just a list of observations
\[ x_1, x_2, x_3, ... x_n \]
Mean
\[ \bar{x} = \frac{x_1 + x_2 + x_3 + ... + x_n}{n} \]
\[ \bar{x} = \frac{ \sum_{i = 1}^{n} x_i }{n} \]
Mode
The observation which occurs the highest number of time. So the x_i which has the highest count in the observation list.
Median
The median is the middle most observations. After ordering the n observations in observation list in either Ascending or Descending order (any order works). The median will be :
- n is even
\[ Median = \frac{ x_\frac{n}{2} + x_{(\frac{n}{2}+1)} }{2} \]
- n is odd
\[ Median = x_\frac{n+1}{2} \]
Variance and Standard Deviation
\[ Variance = \sigma^2 \] \[ Standard\ deviation = \sigma \]
\[ \sigma^2 = \frac{\sum_{i=1}^{n} (x_i - Mean)^2 }{n} \]
\[ \sigma^2 = \frac{\sum_{i=1}^n x_i^2}{n} - (Mean)^2 \]
Moments
About some constant A
\[ r^{th}\ moment = \frac{1}{n} \Sigma(x_i - A)^r \]
About Mean (Central Moment)
When A = Mean, then the moment is called central moment.
\[ \mu_r = \frac{1}{n} \Sigma(x_i - Mean)^r \]
About Zero (Raw Moment)
When A = 0, then the moment is called raw moment.
\[ \mu_r^{'} = \frac{1}{n} \Sigma x_i^r \]
Grouped Data
Data which is grouped based on the frequency at which it occurs. So if 9 appears 5 times in our observations, we group as x(observation) = 9 and f (frequency) = 5.
| x (observations) | f (frequency) |
| 2 | 5 |
| 1 | 3 |
| 4 | 5 |
| 8 | 9 |
If we store it in data way, i.e. the observations are of form 10-20, 20-30, 30-40 … then we will get $x_i$ by doing
\[ x_i = \frac{lower\ limit + upper\ limit}{2} \]
i.e,
$x_i$ for 20-30 will be $\frac{20 + 30}{2}$
So for data
| f (frequency) | |
| 0- 20 | 2 |
| 20-40 | 6 |
| 40-60 | 1 |
| 60-80 | 3 |
the $x_i$'s will become.
| f_i | x_i | |
| 0- 20 | 2 | 10 |
| 20-40 | 6 | 30 |
| 40-60 | 1 | 50 |
| 60-80 | 3 | 70 |
Mean
\[ \bar{x} = \frac{ \Sigma f_i x_i}{\Sigma f_i } \]
Mode
The modal class is the record with the row with the highest f_i
\[ Mode = l + (\frac{f_1 - f_0}{2f_1 - f_0 - f_2}) \times h \]
In the formula :
l → lower limit of modal class
f_1 → frequency(f_i) of the modal class
f_0 → frequency of the row preceding modal class
f_2 → frequency of the row after the modal class
h → size of class interval (upper limit - lower limit)
Median
The median for grouped data is calculated with the help of cumulative frequency. The cumulative frequency (cf_i) is given by:
\[ cf_i = f_1 + f_2 + f_3 + ... + f_i \]
The median class is the class whose cf_i is just greater than or is equal to $\frac{\Sigma f}{2}$
\[ Median = l + (\frac{(n/2) - cf}{f}) \times h \]
In the formula :
l → lower limit of the median class
h → size of class interval (upper limit - lower limit)
n → number of observations
cf → cumulative frequency of the median class
f → frequency of the median class
Variance and Standard Deviation
\[ Variance = \sigma^2 \] \[ Standard\ deviation = \sigma \]
\[ \sigma^2 = \frac{\sum_{i=1}^{n} f_i(x_i - Mean)^2 }{\Sigma f_i} \]
\[ \sigma^2 = \frac{\sum_{i=1}^n f_ix_i^2}{\Sigma f_i} - (Mean)^2 \]
Moments
About some constant A
\[ r^{th}\ moment = \frac{1}{\Sigma f_i} [\Sigma f_i (x_i - A)^r] \]
About Mean (Central Moment)
When A = Mean, then the moment is called central moment. \[ \mu_r = \frac{1}{\Sigma f_i} [\Sigma f_i (x_i - Mean)^r] \]
About Zero (Raw Moment)
When A = 0, then the moment is called raw moment. \[ \mu_r^{'} = \frac{1}{\Sigma f_i} [\Sigma f_i x_i^r] \]
Relation between Mean, Median and Mode
\[ 3Median = 2Mean + Mode \]
Relation between raw and central moments
\[ \mu_0 = \mu_0^{'} = 1 \] \[ \mu_1 = 0 \] \[ \mu_2 = \mu_2^{'} - \mu_1^{'2} \] \[ \mu_3 = \mu_3^{'} - 3\mu_1^{'}\mu_2^{'} + 2\mu_1^{'3} \] \[ \mu_4 = \mu_4^{'} - 4\mu_3^{'}\mu_1^{'} + 6\mu_2^{'}\mu_1^{'2} - 3\mu_1^{'4} \]
Skewness and Kurtosis
Skewness
- If Mean > Mode, then skewness is positive
- If Mean = Mode, then skewness is zero (graph is symmetric)
- If Mean < Mode, then skewness is zero
/Documents/ProbabilityAndStatistics/src/commit/8552f74f917912f9a5161145728b72fdfd9a7130/skewness.PNG
Pearson's coefficient of skewness
The pearson's coefficient of skewness is denoted by SKP
\[ S_{KP} = \frac{Mean - Mode}{Standard\ Deviation} \]
- If SKP is zero then distribution is symmetrical
- If SKP is positive then distribution is positively skewed
- If SKP is negative then distribution is negatively skewed
Moment based coefficient of skewness
The moment based coefficient of skewness is denoted by β_1. The μ here is central moment.
\[ \beta_1 = \frac{\mu_3^2}{\mu_2^3} \]
The drawback of using β_1 as a coefficient of skewness is that it can only tell if distribution is symmetrical or not ,when $\beta_1 = 0$. It can't tell us the direction of skewness, i.e positive or negative.
- If β_1 is zero, then distribution is symmetrical
Karl Pearson's γ_1
To remove the drawback of the β_1 , we can derive Karl Pearson's γ_1
\[ \gamma_1 = \sqrt{\beta_1} \] \[ \gamma_1 = \frac{\mu_3}{\mu_2^{3/2}} \]
- If μ_3 is positive, the distribution has positive skewness
- If μ_3 is negative, the distribution has negative skewness
- If μ_3 is zero, the distribution is symmetrical
Kurtosis
Kurtosis is the measure of the peak and the curve and the "fatness" of the curve.
/Documents/ProbabilityAndStatistics/src/commit/8552f74f917912f9a5161145728b72fdfd9a7130/kurtosis.PNG
The kurtosis is calculated using β_2
\[ \beta_2 = \frac{\mu_4}{\mu_2^2} \]
The value of β_2 tell's us about the type of curve
- Leptokurtic (High Peak) when β_2 > 3
- Mesokurtic (Normal Peak) when β_2 = 3
- Platykurtic (Low Peak) when β_2 < 3
Karl Pearson's γ_2
γ_2 is defined as:
\[ \gamma_2 = \beta_2 - 3 \]
- Leptokurtic when γ_2 > 0
- Mesokurtic when γ_2 = 0
- Platykurtic when γ_2 < 0
Basic Probability
Conditional Probability
If some event B has already occured, then the probability of the event A is:
\[ P(A \mid B) = \frac{P(A \cap B)}{P(B)} \]
$P(A \mid B)$ is read as A given B. So we are given that B has occured and this is probability of now A occuring.
Law of Total Probability
The law of total probability is used to find probability of some event A that has been partitioned into several different places/parts.
\[ P(A) = P(A|B_1)P(B_1) + P(A|B_2)P(B_2) + P(A|B_3)P(B_3) + ... + P(A|B_i)P(B_i) \] \[ P(A) = \Sigma P(A|B_i)P(B_i) \]
Example, Suppose we have 2 bags with marbles
- Bag 1 : 7 red marbles and 3 green marbles
- Bag 2 : 2 red marbles and 8 green marbles
Now we select one bag at random (i.e, the probability of choosing any of the two bags is equal so 0.5). If we draw a marble, what is the probability that it is a green marble?
Sol. The green marbles are in parts in bag 1 and bag 2.
Let G be the event of green marble.
Let B_1 be the event of choosing the bag 1
Let B-2 be the event of choosing the bag 2 \\
Then, $P(G|B_1) = \frac{3}{7 + 3}$ and $P(G|B_2) = \frac{8}{2 + 8}$ \\ Now, we can use the law of total probability to get
\[ P(G) = P(G|B_1)P(B_1) + P(G|B_2)P(B_2) \]
Example 2, Suppose a there are 3 forests in a park.
- Forest A occupies 50% of land and 20% plants in it are poisonous
- Forest B occupies 30% of land and 40% plants in it are poisonous
- Forest C occupies 20% of land and 70% plants in it are poisonous
What is the probability of a random plant from the park being poisonous.
Sol. Since probability is equal across whole area of the park. Event A is plant being from Forest A, Event B is plant being from Forest B and Event C is plant being from Forest C. If event P is plant being poisonous, then using law of total probability,
\[ P(P) = P(P|A)P(A) + P(P|B)P(B) + P(P|C)P(C) \]
And we know P(A) = 0.5, P(B) = 0.3 and P(C) = 0.2. Also P(P|A) = 0.20, P(P|B) = 0.40 and P(P|C) = 0.70
Some basic identities
- Probabilities follow law of inclusion and exclusion
\[ P(A \cup B) = P(A) + P(B) - P(A \cap B) \]
- DeMorgan's Theorem
\[ P(\overline{A \cap B }) = P(\overline{A} \cup \overline{B}) \] \[ P(\overline{A \cup B }) = P(\overline{A} \cap \overline{B}) \]
- Some other Identity
\[ P(\overline{A} \cap B) + P(A \cap B) = P(B) \] \[ P(A \cap \overline{B}) + P(A \cap B) = P(A) \]