Tag: Forecasting Techniques

Normal distribution, or the Gaussian distribution, is a fundamental probability distribution that describes how data values are distributed symmetrically around a mean. Its graph forms a bell-shaped curve, with most data points clustering near the mean and fewer occurring as they deviate further. The curve is defined by two parameters: the mean (μ) and the standard deviation (σ), which determine its center and spread. Normal distribution is widely used in statistics, natural sciences, and social sciences for analysis and inference.

The general form of its probability density function is:

The parameter μ is the mean or expectation of the distribution (and also its median and mode), while the parameter σ is its standard deviation. The variance of the distribution is σ^2. A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known. Their importance is partly due to the central limit theorem. It states that, under some conditions, the average of many samples (observations) of a random variable with finite mean and variance is itself a random variable whose distribution converges to a normal distribution as the number of samples increases. Therefore, physical quantities that are expected to be the sum of many independent processes, such as measurement errors, often have distributions that are nearly normal.

A normal distribution is sometimes informally called a bell curve. However, many other distributions are bell-shaped (such as the Cauchy, Student’s t, and logistic distributions).

Importance of Normal Distribution:

1. Foundation of Statistical Inference

The normal distribution is central to statistical inference. Many parametric tests, such as t-tests and ANOVA, are based on the assumption that the data follows a normal distribution. This simplifies hypothesis testing, confidence interval estimation, and other analytical procedures.

2. Real-Life Data Approximation

Many natural phenomena and datasets, such as heights, weights, IQ scores, and measurement errors, tend to follow a normal distribution. This makes it a practical and realistic model for analyzing real-world data, simplifying interpretation and analysis.

3. Basis for Central Limit Theorem (CLT)

The normal distribution is critical in understanding the Central Limit Theorem, which states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population’s actual distribution. This enables statisticians to make predictions and draw conclusions from sample data.

4. Application in Quality Control

In industries, normal distribution is widely used in quality control and process optimization. Control charts and Six Sigma methodologies assume normality to monitor processes and identify deviations or defects effectively.

5. Probability Calculations

The normal distribution allows for the easy calculation of probabilities for different scenarios. Its standardized form, the z-score, simplifies these calculations, making it easier to determine how data points relate to the overall distribution.

6. Modeling Financial and Economic Data

In finance and economics, normal distribution is used to model returns, risks, and forecasts. Although real-world data often exhibit deviations, normal distribution serves as a baseline for constructing more complex models.

Central limit theorem

In probability theory, the central limit theorem (CLT) establishes that, in many situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a bell curve) even if the original variables themselves are not normally distributed. The theorem is a key concept in probability theory because it implies that probabilistic and statistical methods that work for normal distributions can be applicable to many problems involving other types of distributions. This theorem has seen many changes during the formal development of probability theory. Previous versions of the theorem date back to 1810, but in its modern general form, this fundamental result in probability theory was precisely stated as late as 1920, thereby serving as a bridge between classical and modern probability theory.

Characteristics Fitting a Normal Distribution

Poisson distribution is a probability distribution used to model the number of events occurring within a fixed interval of time, space, or other dimensions, given that these events occur independently and at a constant average rate.

Importance

1. Modeling Rare Events: Used to model the probability of rare events, such as accidents, machine failures, or phone call arrivals.
2. Applications in Various Fields: Applicable in business, biology, telecommunications, and reliability engineering.
3. Simplifies Complex Processes: Helps analyze situations with numerous trials and low probability of success per trial.
4. Foundation for Queuing Theory: Forms the basis for queuing models used in service and manufacturing industries.
5. Approximation of Binomial Distribution: When the number of trials is large, and the probability of success is small, Poisson distribution approximates the binomial distribution.

Conditions for Poisson Distribution

1. Independence: Events must occur independently of each other.
2. Constant Rate: The average rate (λ) of occurrence is constant over time or space.
3. Non-Simultaneous Events: Two events cannot occur simultaneously within the defined interval.
4. Fixed Interval: The observation is within a fixed time, space, or other defined intervals.

Constants

1. Mean (λ): Represents the expected number of events in the interval.
2. Variance (λ): Equal to the mean, reflecting the distribution’s spread.
3. Skewness: The distribution is skewed to the right when λ is small and becomes symmetric as λ increases.
4. Probability Mass Function (PMF): P(X = k) = [e^−λ*λ^k] / k!, Where $k$ is the number of occurrences, $e$ is the base of the natural logarithm, and λ is the mean.

Fitting of Poisson Distribution

When a Poisson distribution is to be fitted to an observed data the following procedure is adopted:

The binomial distribution is a probability distribution that summarizes the likelihood that a value will take one of two independent values under a given set of parameters or assumptions. The underlying assumptions of the binomial distribution are that there is only one outcome for each trial, that each trial has the same probability of success, and that each trial is mutually exclusive, or independent of each other.

In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes, no question, and each with its own Boolean-valued outcome: success (with probability p) or failure (with probability q = 1 − p). A single success/failure experiment is also called a Bernoulli trial or Bernoulli experiment, and a sequence of outcomes is called a Bernoulli process; for a single trial, i.e., n = 1, the binomial distribution is a Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significance.

The binomial distribution is frequently used to model the number of successes in a sample of size n drawn with replacement from a population of size N. If the sampling is carried out without replacement, the draws are not independent and so the resulting distribution is a hypergeometric distribution, not a binomial one. However, for N much larger than n, the binomial distribution remains a good approximation, and is widely used

The binomial distribution is a common discrete distribution used in statistics, as opposed to a continuous distribution, such as the normal distribution. This is because the binomial distribution only counts two states, typically represented as 1 (for a success) or 0 (for a failure) given a number of trials in the data. The binomial distribution, therefore, represents the probability for x successes in n trials, given a success probability p for each trial.

Binomial distribution summarizes the number of trials, or observations when each trial has the same probability of attaining one particular value. The binomial distribution determines the probability of observing a specified number of successful outcomes in a specified number of trials.

The binomial distribution is often used in social science statistics as a building block for models for dichotomous outcome variables, like whether a Republican or Democrat will win an upcoming election or whether an individual will die within a specified period of time, etc.

Importance

For example, adults with allergies might report relief with medication or not, children with a bacterial infection might respond to antibiotic therapy or not, adults who suffer a myocardial infarction might survive the heart attack or not, a medical device such as a coronary stent might be successfully implanted or not. These are just a few examples of applications or processes in which the outcome of interest has two possible values (i.e., it is dichotomous). The two outcomes are often labeled “success” and “failure” with success indicating the presence of the outcome of interest. Note, however, that for many medical and public health questions the outcome or event of interest is the occurrence of disease, which is obviously not really a success. Nevertheless, this terminology is typically used when discussing the binomial distribution model. As a result, whenever using the binomial distribution, we must clearly specify which outcome is the “success” and which is the “failure”.

The binomial distribution model allows us to compute the probability of observing a specified number of “successes” when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure. We must first introduce some notation which is necessary for the binomial distribution model.

First, we let “n” denote the number of observations or the number of times the process is repeated, and “x” denotes the number of “successes” or events of interest occurring during “n” observations. The probability of “success” or occurrence of the outcome of interest is indicated by “p”.

The binomial equation also uses factorials. In mathematics, the factorial of a non-negative integer k is denoted by k!, which is the product of all positive integers less than or equal to k. For example,

• 4! = 4 x 3 x 2 x 1 = 24,
• 2! = 2 x 1 = 2,
• 1!=1.
• There is one special case, 0! = 1.

Conditions

• The number of observations n is fixed.
• Each observation is independent.
• Each observation represents one of two outcomes (“success” or “failure”).
• The probability of “success” p is the same for each outcome

Constants

Fitting of Binomial Distribution

Fitting of probability distribution to a series of observed data helps to predict the probability or to forecast the frequency of occurrence of the required variable in a certain desired interval.

To fit any theoretical distribution, one should know its parameters and probability distribution. Parameters of Binomial distribution are n and p. Once p and n are known, binomial probabilities for different random events and the corresponding expected frequencies can be computed. From the given data we can get n by inspection. For binomial distribution, we know that mean is equal to np hence we can estimate p as = mean/n. Thus, with these n and p one can fit the binomial distribution.

There are many probability distributions of which some can be fitted more closely to the observed frequency of the data than others, depending on the characteristics of the variables. Therefore, one needs to select a distribution that suits the data well.