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In this course content area we will discuss probability, random variables, probability mass functions (PMFs), hypotheses, alpha level, and p-values. Some of these concepts, e.g., conditional probability and conditional expectation, are the lingua franca of the Rubin causal model and are used to elaborate the research strategy of experimentation, which is the next topic.
At the conclusion of this module (Mathematics, Probability & Statistical Prerequisites) participants will be able to
i. understand and manipulation summation notation
ii. compute probabilities
iii. evaluate random variables
iv. understand independence between random variables, compute expectation and measures of location,
v. compute variance and measure of dispersion
vii. understand and evaluate condition means
viii. understand Normal Distribution
ix. compute and understand estimation and sampling distributions. understand and execute test of statistical
significance: P-value approach (hypothesis testing)