|Objective:||This course introduces basic probability and statistics to students. In probability, students are taught the basic concepts of probability and introduced to discrete and continuous random variables, including some special probability distributions. In statistics, students will be introduced to some of the basic terms in statistics and exposed to tabular as well as pictorial presentation of data. Students will be taught sampling distribution for mean, population and variance.|
|Student Learning Time:||120 hours|
Final Examination: 50%
Basic terms, types of statistics, population, sample, types of variables. Tabular presentation – frequency, relative frequency, percentage and cumulative
frequency distributions, pictorial presentation – bar chart, pie chart, histogram, frequency polygon and ogive. Measures of central tendency for ungrouped and grouped data. Measures of dispersion for ungrouped and grouped data.
Events and Probability
Experiment and sample space, events and their occurrences, multiplication rule, combinations, permutations, set operations, venn diagram, tree
diagram, probability of an event, additive and multiplicative rules, conditional probability, independent events, mutually exclusive events, complement of an event.
Probability distributions of discrete random variables and continuous random variables. Cumulative distributions of discrete random variables and
continuous random variables. Mean, variance, and standard deviation of discrete random variables and continuous random variables.
Discrete - Binomial and Poisson Distributions. Probability formula, probability table, mean, variance, and standard deviation of Binomial and Poisson
distributions. Continuous - Normal Distribution, standard normal distributions. Probability table. Applications to real problems.
Mean, variance, and standard deviation of the sample mean, probability of the sample mean. Sampling error and non-sampling error. Sampling distributions of a sample mean when the population has a normal distribution, Sampling distributions of a sample mean when the population is not from a normal distribution (Central Limit Theorem). Sampling distribution for sample proportion and sample variance.
At the completion of the subject, students should be able to: