Probability

Summary Two events that have nothing in common are known as mutually exclusive events Two events that have elements in common are known as non-mutually exclusive events If two events are independent event meaning they both do not depend on one another If two events are dependent events they both are (conditional probability) It is … Read more

The Uniform Distribution

Summary In uniform distribution the random variable is a continuous random variable The probability density function is calculated as: Mean Variance The cumulative distribution function is calculated by integrating the probability density function f(x) to give  Standard deviation is the under root of variance   In uniform distribution you should know that random variable is … Read more

The Product Moment Correlation Coefficient

Summary Formula for correlation r >0 positive correlation r <0 negative correlation r =0 no/zero correlation r =+1 r = -1 perfect positive and negative correlation respectively A good relation between the variables means that the line of best fit will pass through maximum points The interdependence of the two variables is known as as … Read more

The Poisson Distribution

Summary The probability function of the poisson distribution is Both the mean and variance the same in poisson distribution. When calculating poisson distribution the first thing that we have to keep in mind is the if the random variable is a discrete variable. If however, your variable is a continuous variable e.g it ranges from … Read more

The Normal Distribution

Summary The normal distribution has a bell shaped curve It is symmetric about the mean Each normal distribution is affected by the either the mean() or standard deviation() The total area under the normal curve is equal to 1 To find the normal distribution we use the following formula: The normal distribution is a theoretical … Read more

The Geometric Distribution

Summary The geometric distribution has a single parameter (p) = X ~ Geo(p) Geometric distribution can be written as , where q = 1 – p The mean of the geometric distribution is: The variance of the geometric distribution is: The standard deviation of the geometric distribution is: The geometric distribution are the trails needed … Read more