Here are the slides for the lectures. The slides are in pdf format. You can download them and use them offline.
Slides : Ch1 - Descriptive Statistics
DataSets Following are the data sets used in Ch1 Slides, most of the data sets are from Anderson et al. (2020). You can download them and use them for practice.
Key concepts are: What is Statistics? What is data? Different types of data, Different Classifications of Variables (Categorial vs Quantitative, Discrete vs Continuous, Nominal, Ordinal, Interval, Ratio), What is Population and Sample? How to do Sampling? Tabular and Graphical Measures - Frequency Distribution, Bar Chart, Histogram, Box-Plot, Scatter-Plot, and so on. Numerical Measures - Mean, Pecentile, Median, Variance and Standard Deviation, Covariance and Correlation, Geometric Mean, and so on.
Recap Slides : Ch0 - Math Recap
Key concepts are: Definitions of Random Experiment, Sample Space, Event and Probability? Different approaches to define Probability - Classical, Relative Frequency, and Axiomatic. Basic Probability Rules - Addition Rule, Multiplication Rule, Conditional Probability, Bayes’ Theorem, and so on.
Note: The math recap slides are only to brush up your old math knowledge. You can skip them if you are comfortable with basic math concepts like set theory, functions, permutations, combinations, and so on.
Key concepts are: Random Variables, Discrete and Continuous Random Variables, Probability Distribution, Probability Mass Function (PMF), Cumulative Distribution Function (CDF), Probability Density Function (PDF), Expected Value, Variance, LOTUS, Linearity of Expectation. Some Parametric Distributions - Binomial, Poisson, Uniform, Normal, Exponential, and so on.
Slides : Ch4 - Joint, Marginal and Conditional Distributions
Key concepts are: Joint Distributions, Marginal Distributions, Conditional Distributions, Independence of Random Variables, Covariance and Correlation of Random Variables, Law of Total Probability, Law of Iterated Expectation and so on. Some examples of Joint Distributions - Joint Binomial and Joint Normal, and so on.
Slides : There are no slides here but you should look at the Lecture Notes and Read Newbold, Carlson, and Thorne (2020) and Anderson et al. (2020)
Lecture Notes : Ch5 - Sampling Distributions and Central Limit Theorem
Key concepts are: Sampling Distribution of Sample Means, Sample Proportions, Sampling Distribution Under Normality and Central Limit Theorem (CLT), and so on.