In this video we look at how to use statistical tables to calculate probabilities in a Poisson distribution. This includes an example of using the table for the probability density function to determine the probability the random variable is equal to a particular value and an example of using the table for the cumulative distribution function to determine the probability the random variable is less than a certain value and an example determining the probability it is greater than or equal to a certain value.

In this video we look at how to decide for a given scenario (worded problem) if the distribution described is a Binomial distribution or Poisson distribution and whether its probability distribution function or its cumulative distribution function is required to calculate a specified probability.

Example of carrying out a one-sample t-test including hypotheses, signficance level, finding calculated value of t and comparing to critical value of t, making a decision and conclusion.

Examples of writing null hypothesis and alternative hypothesis both in words and symbols, for a one-sample hypothesis test for the mean. Notion of one-tailed and two-tailed tests are introduced.

Example of carrying out a one-sample z-test including hypotheses, signficance level, finding calculated value of t and comparing to critical value of z, making a decision and conclusion.

An introduction to and example of using an inverse t distribution table to find the critical value of t for a one-tailed t-test.

An introduction to and example of using an inverse t distribution table to find the critical value of t for a two-tailed t-test.

An introduction to reading a standard normal distribution table, with an example of how to use it to find the area under the standard normal curve to the left of a specified value of z.

An example of using a standard normal distribution table to find the area under the standard normal curve to the right of a specified value of z and an example of using it to find the area under the standard normal curve between two values of z.

An example of the step by step process used to conduct a z test for the mean, with emphasis on the interpretation of the test statistic (z) and the p-value.