Statistical Inference & Decision Making
Inferential Statistics involves drawing conclusions and making predictions about a population based on sample data. It includes hypothesis testing, where null and alternative hypotheses are evaluated using p-values and confidence intervals. Key techniques include t-tests, ANOVA, and chi-square tests to compare groups or relationships. Sampling methods ensure representativeness, while probability distributions like normal, binomial, and Poisson underpin analyses. Regression analysis and correlation measure relationships between variables, while the Central Limit Theorem supports the generalization of sample results. Inferential statistics are essential for making data-driven decisions in uncertain scenarios, bridging the gap between sample insights and population conclusions.
Key Topics for Certifications
- Introduction to Inferential Statistics
- Population and Sample
- Probability Fundamentals
- Parametric Tests
- Non-Parametric Tests
- Confidence Intervals
- Correlation and Regression Analysis
- Sampling Distribution
- ANOVA and MANOVA
- Goodness-of-Fit Tests
- Model Validation Techniques
- Application of Inferential Statistics
Certifications Details
| Certification Exam Code | : | DA-CL-STS-23 |
| Certification Name | : | Associate Certificate in Statistical Inference & Decision Making |
| Difficulty Level | : | Associate |
| Mode | : | Online |
| Exam Proctoring | : | AI Proctored |
| No. of Questions | : | 35-45 |
| Online Exam Duration | : | 35-50 Minutes |