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Posted: September 24th, 2023
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Probability Definitions and Properties: Understand the quantifiable value denoting the likelihood of an event, expressed as percentages or proportions, and delve into the ten properties of probability.
Common Discrete and Continuous Distributions: Explore equations with specific or continuous data values, and understand the nuances of discrete and continuous distributions.
Bivariate Distributions: Gain insights into probability combinations between two variables, their calculation based on occurrence probabilities, and examples of bivariate distributions.
Conditional Probability: Delve into the likelihood of events based on prior outcomes, the multiplication of probabilities, and the precise calculation method.
Random Variables, Expectations, and Variance: Learn how to compute expectations and variances from random variables, and comprehend their significance in describing averages and deviations.
Univariate and Bivariate Transformations: Uncover the transformation of random variables and their subsequent distribution, differentiating univariate and bivariate transformations.
Convergence of Random Variables: Explore the essential concept of random variable convergence and its applications in probability theory and statistics.
Estimation: Grasp the art of inferring unknown population values from incomplete data through point estimation.
Hypothesis Testing: Understand the process of drawing conclusions about population parameters or probability distributions using sample data.
Confidence Intervals: Discover the concept of confidence intervals, their computation, and their role in measuring uncertainty in sample variables.
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Advanced Statistics
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Bivariate linear regression and correlation
Chi-square, student's t, and Snedecor's F distributions
Goodness of fit testing
Continuous parametric probability distributions
Bivariate probability distributions
Descriptive statistical techniques
Sampling and sampling distribution of a statistic
Discrete parametric probability distributions
Interval estimation and confidence intervals
Probability theory
Nonparametric statistical techniques
Point estimation and properties of estimators
Parametric statistical hypothesis testing
Random variables and probability distributions
Goodness of fit testing: Contingency tables
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