Master Course in Statistics and Econometrics (101 level)
Statistics, Econometrics, Regression Analysis , Time Series Analysis, Hypothesis Testing, Research Methodology, SPSS
What you'll learn
- Define and explain key concepts in statistics, such as measures of central tendency, variability, and probability.
- Demonstrate the ability to organize, summarize, and visualize data using appropriate statistical techniques.
- Apply the principles of probability to solve practical problems and make informed predictions.
- Interpret the results of probability distributions and understand their relevance in statistical analysis.
- Formulate hypotheses and conduct hypothesis tests for population parameters.
- Interpret p-values and confidence intervals to make informed decisions about statistical significance.
- Construct confidence intervals for population parameters and understand the precision of estimates.
- Evaluate the impact of sample size and variability on the width of confidence intervals.
- Develop skills in building and interpreting simple and multiple regression models.
- Understand how to identify and interpret the coefficients, including assessing their statistical significance.
- Apply diagnostic techniques to assess the assumptions of regression models.
- Address issues like multicollinearity and heteroscedasticity to enhance the reliability of regression analysis.
- Apply various time series forecasting methods, such as moving averages and exponential smoothing.
- Evaluate the accuracy and reliability of time series forecasts in different contexts.
- Apply regression analysis techniques to estimate parameters and test economic hypotheses.
- Critically assess the implications, limitations, and policy relevance of econometric results in economic applications.
