Introduction to Statistical Distribution

Grasp the concept of a statistical distribution, a function that describes the probability of a random variable.
Understand the difference between discrete and continuous distributions. Discrete distributions have distinct values whereas continuous distributions have an infinite number of possible values.

Discrete Distributions

Familiarise with the Binomial distribution, used when there are exactly two mutually exclusive outcomes of a trial.
Recognise Poisson distribution which represents the number of events in a fixed interval of time or space.
Get grip on the aspects of the Geometric distribution, used to represent the number of trials required for the first success.

Comprehend the Uniform distribution model where all outcomes are equally likely.
Delve into the Normal distribution, also known as Gaussian distribution. It’s used in statistics for a complex random variable whose distribution is not known.
Build understanding around the Exponential distribution, used to model the time between the occurrence of events.

Understand the difference between probability density function (pdf) and cumulative distribution function (cdf). Pdf gives the probability of a range of outcomes, while the cdf gives the cumulative probability.
Be comfortable working with and interpreting histograms, frequency polygons, or cumulative frequency graphs generated from pdfs and cdfs.

Grasp the concepts of mean, median, and mode as measures of central tendency in statistical distributions.
Recognise the range, variance, and standard deviation as measures of dispersion around the mean.
Appreciate the concept of skewness as a measure of the asymmetry of a distribution, and understand kurtosis as a measure of whether the data are heavy-tailed relative to the normal distribution.

Frequent practice with calculations related to statistical distributions. This can range from simple calculations to more advanced problems.
Practise interpreting and analysing data represented by various distributions.
Keep learning to solve statistical distribution problems both algebraically and graphically.
Consolidate understanding through worked examples. Always look over completed problems again to ensure full comprehension and accuracy.
Stay persistent when working on statistical distributions. This consistent practice will sharpen problem-solving skills and foster a better understanding of the subject.