Introductory statistics Part1: Descriptive Statistics

Course Curriculum

Chapter 1: About the course: What are going to learn ?

Lecture 1: What you need to know about the class

Chapter 2: Data files for the test and comprehensive test to check for mastery.

Lecture 1: Download the lecture datafiles first

Chapter 3: Concepts of populations, samples, variables and type of sampling

Lecture 1: Definitions of statistics and type of statistical studies

Lecture 2: Concepts of populations, samples, variables and type of sampling

Lecture 3: Concept of sampling: probability and non-probability samples

Lecture 4: Type of data or variables, measurement levels and type of studies

Chapter 4: Describing qualitative and quantitative data in statistics

Lecture 1: Frequency distributions, bar graphs, pie charts and stem-and-leafs plots

Lecture 2: Defining lower and upper class limits as well as class midpoints

Lecture 3: Techniques for constructing a histogram

Lecture 4: Frequency polygons, ogive and Pareto Vilfredo charts

Lecture 5: Identifying and avoiding bad graphs in statistics

Chapter 5: Statistical methods for describing the center, variation or spread of the data

Lecture 1: Computing the mean and the weighted mean of the data

Lecture 2: Computing the geometric and harmonic means, median, and midrange of the data

Lecture 3: Classifying statistical distributions

Lecture 4: Sample variance, standard deviation and coefficient of variation of the data

Lecture 5: Interpreting the standard deviation: Empirical and Chebyshev's rules

Chapter 6: Descriptive statistics measures for grouped datasets

Lecture 1: Computing the mean, median, variance and standard deviation of grouped data

Chapter 7: Numerical measures of relative standing

Lecture 1: Computing and interpreting the Z-scores

Lecture 2: Percentiles rank, quartiles, boxplots and identifying outliers in the data

Chapter 8: Relationships between two variables:Correlations and simple linear regression.

Lecture 1: Scatterplots and correlations coefficients

Lecture 2: Computing the sample correlation coefficient

Lecture 3: Concepts and terminologies of linear regression

Lecture 4: Linear regression equation with practical examples

Chapter 9: Real world applications of descriptive statistics using EXCEL

Lecture 1: Installing the Analysis ToolPak Library for EXCEL

Lecture 2: Computing descriptive statistics of US baseball players weights with EXCEL

Lecture 3: Computing the correlation coefficient in EXCEL

Lecture 4: Computing the Z scores of the corruption data using Excel

Lecture 5: Creating a scatter plot and fitting a regression line using EXCEL

Chapter 10: Conclusion and upcoming courses

Lecture 1: Concluding remarks