Statistical Methods & Time Series Analysis
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Follow on Instagram for UpdatesStatistical Methods & Time Series Analysis Syllabus — Unit-wise Content
The official GTU curriculum for Statistical Methods & Time Series Analysis (subject code ME02023041) as prescribed for IT · Semester 2 divides the course into 5 units, covering 41 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02023041)Unit 1 Fundamental Probability Concepts for Statistical Analysis
- Definitions and Rules of Probability
- Sample Spaces and Events
- Conditional Probability and Independence
- Discrete Random Variables and Probability Distributions
- Continuous Random Variables and Probability Distributions
Unit 2 Sampling Distributions and the Central Limit Theorem, General
- Concepts of Point Estimation
- Methods of Point Estimation-Method of Moments and Method of Maximum Likelihood
- Statistical Intervals Based on a Single Sample: Basic Properties of Confidence Intervals
- Large-Sample Confidence Intervals for a Population Mean and Proportion
- Confidence Intervals for the Variance and Standard Deviation of a Normal Population
Unit 3 Hypothesis Testing
- Hypotheses and Test Procedures
- Large Sample Hypothesis Tests for Population Mean and Population Proportion
- Small Sample Test for Population Mean
- Large Sample Tests for a Difference Between Two Population Means and for a Difference Between Two Population Proportions
- Small Sample Test for a Difference Between Two Population Means
- Single-Factor ANOVA
- Multiple Comparisons in ANOVA
Unit 4 Fundamentals of Time Series
- Definition and characteristics of time series data
- Applications of time series analysis
- Components of a time series: Trend
- Seasonal variations
- Cyclic variations
- Irregular components
- Decomposition of Time Series
- Additive and multiplicative Program Name: Master of Engineering Level: PG Subject Code: ME02023041 Subject Name: Statistical Methods & Time Series Analysis models
- Trend Analysis
- Methods for estimating trend
- Case studies in trend estimation
Unit 5 Trend Analysis and Seasonal Components, Advanced Trend Analysis,
- Method of moving averages for trend estimation
- Seasonal Components
- Definition and significance of seasonality in time series
- Estimation of seasonal components
- Autoregressive Integrated Moving Average (ARIMA): Understanding the components (AR, I, MA)
- Model identification (ACF
- Parameter estimation and diagnostic checks
- Seasonal ARIMA (SARIMA): Incorporating seasonality into ARIMA models
- Identifying seasonal components and periodicity
- Model building and validation
- Real-world applications of ARIMA and SARIMA in Forecasting demand
- Examples: Predicting resource utilization in engineering systems
- Analyzing environmental or climatic time series
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