# Become a Probability & Statistics Master

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 148 lectures (15h 11m) | 2.60 GB

Learn everything from Probability & Statistics, then test your knowledge with 600+ practice questions

HOW BECOME A PROBABILITY & STATISTICS MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:

This course includes video and text explanations of everything from Probability and Statistics, and it includes quizzes (with solutions!) and an additional workbooks with extra practice problems, to help you test your understanding along the way. Become a Probability & Statistics Master is organized into the following sections:

• Visualizing data, including bar graphs, pie charts, Venn diagrams, histograms, and dot plots
• Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots
• Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
• Probability, including union vs. intersection and independent and dependent events and Bayes’ theorem
• Discrete random variables, including binomial, Bernoulli, Poisson, and geometric random variables
• Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals
• Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values
• Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, and chi-square

What you’ll learn

• Visualizing data, including bar graphs, pie charts, venn diagrams, histograms, and dot plots
• Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots
• Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
• Probability, including union vs. intersection and independent and dependent events and Bayes’ theorem
• Discrete random variables, including binomial, Bernoulli, Poisson, and geometric random variables
• Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals
• Hypothesis testing, including inferential statistics, significance level, type I and II errors, test statistics, and p-values
• Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, and chi-square
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