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In this course, you will :
- Discover unusual pitfalls and how to assess the effectiveness of such tests.
- Learn the definition of probability as well as the basic probability rules that you will need to solve both simple and difficult problems.
- Learn about examples of how simple probability rules are utilized to generate solutions for real-world complex problems.
- The empirical rule and normal approximation for data, a strategy utilized in many statistical techniques, are covered.
- Discover the binomial distribution and the fundamentals of random variables.
- Discover the Central Limit Theorem and the Law of Large Numbers.
- Learn how to tell the difference between the various types of histograms found in statistical research.
- Discover inference, regression, and regression diagnostics.
- Learn how to build and evaluate confidence intervals in common situations.
- Examine the reasoning behind testing and learn how to conduct the right statistical tests for various samples and conditions. You will also learn about frequent testing misunderstandings and mistakes.
- Learn about the theoretical ideas underlying these procedures and how they are used in various circumstances, such as regression and confidence interval construction.
- focuses on three critical statistical tests for categorical data: the Chi-Square Goodness of Fit test, the Chi-Square test of Homogeneity, and the Chi-Square test of Independence.
- covers the basics of ANOVA and how F-tests work on one-way ANOVA examples.
- Learn about two critical challenges that have emerged in the big data era: data spying and the multiple testing fallacy.
- Investigate the causes of data reproducibility and applicability concerns, as well as how to avoid them in your own work.
Syllabus:
- Introduction and Descriptive Statistics for Exploring Data
- Producing Data and Sampling
- Probability
- Normal Approximation and Binomial Distribution
- Sampling Distributions and the Central Limit Theorem
- Regression
- Confidence Intervals
- Tests of Significance
- Resampling
- Analysis of Categorical Data
- One-Way Analysis of Variance (ANOVA)
- Multiple Comparisons
