Statistical Analysis for business

What is Probability Distribution?

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What do you understand about Probability Distribution? Explain the characteristics and applications of Normal Distribution.

1. Meaning of Probability Distribution

(1) Random Variable

• A random variable is a variable whose value is determined by chance.
  • Example:
    • Number of heads in 3 coin tosses
    • Marks obtained by a student
    • Height of a person

(2) What is Probability Distribution?

• A probability distribution is a systematic description of how the probabilities are assigned to different possible values of a random variable.
• It tells us:
  • Which values the variable can take
  • How likely (what probability) each value is

So, in simple words:

Probability distribution = a rule / function that shows the pattern of probabilities for all possible outcomes of a random variable.

(3) Types of Probability Distribution

(a) Discrete Probability Distribution

• Random variables take finite or countable values (0, 1, 2, 3, …).
• Probabilities are assigned to each distinct value.
• Examples:
  • Binomial distribution
  • Poisson distribution
• Example in simple numbers:
  Toss one fair coin
  • P(Head) = 0.5
  • P(Tail) = 0.5
    This is a discrete probability distribution.

(b) Continuous Probability Distribution

• Random variables can take any value in an interval (infinitely many values).
• We do not talk about probability at a point, but probability over an interval.
• Examples:
  • Normal distribution
  • Exponential distribution
  • t-distribution

(4) Example to Understand

Consider the heights of students in a class.

• Everyone’s height is slightly different
• Values are not countable like 1, 2, 3 – they are continuous (e.g., 165.2 cm, 165.8 cm, etc.)
• Their distribution usually forms a bell-shaped curve → this is a normal distribution, which is a type of continuous probability distribution.

2. Normal Distribution – Meaning

(1) Definition

• The Normal Distribution is a continuous probability distribution that is:
  • Bell-shaped
  • Symmetrical
  • Unimodal (one peak)
• It is also called the Gaussian distribution.

(2) Parameters

A normal distribution is completely determined by two parameters:

1. Mean (μ) → central location
2. Standard deviation (σ) → spread or dispersion

Different values of μ and σ change the position and shape of the curve.

3. Characteristics of Normal Distribution (Step-wise)

(1) Bell-shaped Curve

• The graph of a normal distribution is bell-shaped.
• Most observations are around the center, fewer in the tails.

(2) Symmetry about the Mean

• The curve is perfectly symmetrical about the mean (μ). What is Probability Distribution
• The left half is a mirror image of the right half.
• Therefore:

Mean = Median = Mode

(3) Mean and Standard Deviation Decide Shape

• Mean (μ): fixes the centre of the curve.
• Standard deviation (σ): fixes the spread of the curve. What is Probability Distribution
  • Large σ → curve is wider and flatter
  • Small σ → curve is narrower and sharper (more peaked)

(4) Total Area Under the Curve = 1

• The normal curve is a probability density function.
• The total area under the curve (from −∞ to +∞) is equal to 1, meaning total probability = 1.
• Probability of a range of values = area under the curve over that range.

(5) Asymptotic to X-axis

• The two tails of the curve extend indefinitely in both directions (towards −∞ and +∞).
• They approach the X-axis but never touch it.
• This means extreme values are possible but have very small probabilities.

(6) Unimodal

• The curve has only one peak (one mode).
• Maximum frequency occurs at the mean.

(7) Empirical Rule (68%–95%–99.7% Rule)

In a normal distribution:

1. About 68% of observations lie within ±1σ of the mean (μ − σ to μ + σ)
2. About 95% lie within ±2σ of the mean (μ − 2σ to μ + 2σ)
3. About 99.7% lie within ±3σ of the mean (μ − 3σ to μ + 3σ)

This is very useful in practice to understand how data is spread around the mean. What is Probability Distribution

(8) Mathematical Form

The probability density function (PDF) of the normal distribution is:

f(x) = 1 (2-μ)2 202 σν2π

You don’t always need to derive it in exams, but you should know that:

• It depends on μ and σ
• It ensures total area = 1

(9) Standard Normal Distribution

• If we convert any normal variable X to:

Z = \frac{X – \mu}{\sigma}

• Mean = 0
• Standard deviation = 1
• For this, Z-tables are used to find probabilities.

4. Applications of Normal Distribution (Step-wise)

Normal distribution is extremely important in statistics and real life.

(1) Natural and Biological Measurements

Many natural phenomena are approximately normally distributed, such as:

• Heights and weights of people
• Blood pressure, pulse rate
• Scores in intelligence (IQ) tests
• Measurement errors in experiments

Because of this, normal distribution is often called a “natural law of errors”. What is Probability Distribution

(2) Basis of Statistical Inference

Normal distribution plays a central role in:

(a) Estimation

• Used in constructing confidence intervals for means and proportions.

(b) Hypothesis Testing

• Many tests (Z-test, t-test approximations, etc.) assume that the population or sample is normally distributed. What is Probability Distribution

When sample size is large, even non-normal data leads to approximately normal distribution of sample means (by Central Limit Theorem).

(3) Central Limit Theorem (CLT)

• CLT states:
  
  
  When we take large samples from any population (not necessarily normal), the distribution of sample means tends to become approximately normal, with mean = μ and standard deviation = σ/√n.
  
  
• This is why the normal distribution becomes the backbone of sampling theory and many advanced statistical methods. What is Probability Distribution
  

(4) Quality Control and Industrial Applications

• In industries, normal distribution is used for:
  
  • Control charts
  • Monitoring production quality
  • Detecting whether a process is under control
• Many quality characteristics (like dimensions, weights of products, etc.) are assumed to follow a normal distribution. What is Probability Distribution

(5) Finance and Economics

• Used in modelling stock returns, asset prices, etc.
• Helps in risk analysis, portfolio management, and forecasting.
• Many financial models initially assumed returns to be normally distributed (though in reality they may be slightly different, but normal is used as an approximation). What is Probability Distribution

(6) Education and Psychology

• Test scores (e.g., aptitude tests, IQ tests) are often near-normal.
• Normal distribution helps in:
  • Setting cut-off marks
  • Grading on a curve
  • Comparing performance of students

Example:
If marks in an exam are normally distributed with mean 50 and σ = 10, then:

• Students scoring above 70 are in the top few percent
• Students below 30 are in the bottom few percent. What is Probability Distribution

(7) Probability Calculations with Z-Table

• For a normally distributed variable, we often need to find:
  • P(X ≤ a), P(X ≥ b), P(a ≤ X ≤ b), etc.
• We convert X to Z using: z = x – u/ (μ).
• This is very common in exam questions and practical problems. What is Probability Distribution

5. Conclusion

1. A probability distribution describes how probabilities are assigned to different values of a random variable.
2. The normal distribution is the most important continuous probability distribution in statistics. What is Probability Distribution
3. It is bell-shaped, symmetrical, and fully defined by its mean and standard deviation.
4. Many real-life variables follow approximately normal distribution, and due to the Central Limit Theorem, it becomes the foundation of statistical inference, quality control, finance, education, and scientific research.

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