Whats Covered
 
Course Structure:
The course is divided into 8 modules, each building upon the previous one.
Module 1: Introduction to Statistics
What is statistics?
Importance of statistics in everyday life
Types of data (nominal, ordinal, interval, ratio)
Descriptive statistics (measures of central tendency, dispersion)
Data visualization (histograms, bar charts, scatter plots)
Module 2: Probability and Random Variables
Basic concepts of probability theory
Random variables and their distributions (discrete & continuous)
Expected value and variance
Introduction to probability distributions (normal, binomial, Poisson)
Module 3: Sampling and Estimation
Sampling techniques (random sampling, stratified sampling, cluster sampling)
Sampling distributions and their properties
Point estimation (sample mean, median, proportion)
Interval estimation (confidence intervals)
Module 4: Hypothesis Testing
Null and alternative hypotheses
Types of errors (Type I & Type II)
Levels of significance (p-value)
One-sample & two-sample hypothesis testing (z-tests, t-tests)
Module 5: Correlation and Regression Analysis
Measuring relationships between variables (correlation coefficient)
Simple linear regression model (least squares method)
Interpreting regression coefficients (slope & intercept)
Model evaluation (R-squared, goodness-of-fit)
Module 6: Chi-Square Tests
Testing for independence between categorical variables
Chi-square statistic and its applications
Goodness-of-fit tests (testing frequency distributions)
Module 7: Introduction to Statistical Software
Introduction to a popular statistical software (e.g., R, Python with libraries like pandas)
Data manipulation and cleaning
Performing statistical analyses using software
Module 8: Putting it All Together
Case studies: Applying statistical methods to real-world problems
Ethical considerations in data analysis
Communicating statistical findings effectively