Statistics for Data Analysis Proficiency (Advanced Level)

Topics covered

• analyze a uniform distribution by using cumulative distribution and probability density functions
• compute marginal probabilities
• define a Bayesian model in Python
• describe and compare skewness and kurtosis
• describe the Poisson distribution and its applications
• estimate a population's mean with confidence intervals
• explore cumulative distribution, probability mass, and survival functions with binomial data
• explore the probability tables of nodes in a Bayesian network
• invoke functions available in SciPy to work with Poisson distributions
• outline one-way ANOVA and linear regression
• outline the relationship between type I errors and alpha levels
• outline the relationship between type II errors and alpha levels
• predict values with Bayesian models
• recall the central limit theorem and recognize its applications
• recognize the use of the Mann-Whitney U-test
• simulate the rolling of two die to test joint probability
• use SciPy to generate uniformly distributed samples
• use the cumulative distribution function (CDF) of a normal distribution and recognize how the mean and standard deviation (SD) influence it
• use two-way ANOVA with interaction between the independent variables
• visualize the cumulative distribution function (CDF) for different standard deviations