Python Statistics

1. Measures of Central Tendency: • Mean, Median, and Mode: Definitions, calculation methods, and interpretation. • Comparing the measures and understanding when each is appropriate to use. • Explaining the impact of outliers on these measures.

2. Measures of Dispersion: • Range: Understanding how to calculate and interpret the range. • Variance and Standard Deviation: Definitions, calculation methods, and interpretation. • Coefficient of Variation: Understanding how to use this measure to compare variability in different datasets.

3. Data Visualization: • Histograms: Creating and interpreting histograms to display the distribution of numerical data. • Bar Graphs: Using bar graphs to represent categorical data. • Boxplots: Constructing and analyzing boxplots to summarize and compare data.

4. Probability Distributions: • Normal Distribution: Understanding the characteristics of the normal distribution, including the mean, standard deviation, and empirical rule. • Binomial Distribution: Defining and working with binomial distributions, including calculating probabilities and understanding the parameters involved. • Poisson Distribution: Introduction to the Poisson distribution, its applications, and calculating probabilities.

5. Correlation and Covariance: • Pearson's Correlation Coefficient: Explaining the concept of correlation and interpreting correlation coefficients. • Covariance: Understanding covariance as a measure of the relationship between two variables.

6. Sampling and Sampling Distributions: • Simple Random Sampling: Defining and understanding the process of simple random sampling. • Central Limit Theorem: Explaining the central limit theorem and its importance in statistical inference.