Diploma in Statistics

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

Course Access