स्नातक (प्रारंभिक)Probability & Statisticsनियोजित
Statistical inference
Estimators and their properties, likelihood and maximum likelihood, confidence intervals, hypothesis tests and p-values, linear regression, and an introduction to Bayesian inference.
math.prob.statistics
इस विषय का पाठ अभी लिखा नहीं गया है। नीचे दी गई रूपरेखा पाठ्यक्रम-मानचित्र का हिस्सा है और इसकी समीक्षा नहीं हुई है।
इस पर आधारित
45 अनिवार्य पूर्वापेक्षाएँ (बिल्कुल नीचे तक)
आप सीखेंगे
- Derive maximum likelihood estimators and assess their bias and variance.
- Construct confidence intervals and perform hypothesis tests, interpreting p-values correctly.
- Fit linear regression models and check their assumptions using residuals.
परिभाषा
- An estimator is a function of the data used to estimate an unknown parameter.
- A p-value is the probability, computed assuming the null hypothesis, of a result at least as extreme as the one observed.
- The likelihood L(θ) is the probability or density of the observed data, regarded as a function of the parameter θ.
प्रमेय
- For an independent sample from a population with mean μ and variance σ², the sample mean is unbiased for μ and has variance σ²/n.
- Least-squares regression coefficients satisfy the normal equations XᵀXβ = Xᵀy.
- Performing many tests inflates the chance of false positives unless the analysis corrects for multiplicity.
आम भ्रांतियाँ
- Reading a p-value as the probability that the null hypothesis is true.
- Treating a non-significant result as proof that there is no effect.
वैकल्पिक खोजें
- Computational reproducibility Statistical analyses should be reproducible, with code, data and random seeds recorded so that others can rerun them.
- ← Statistics: centre, spread and distributions
- ← Conditional probability and independence
- ← Optimisation
अधिगम पथ
आगे पढ़ने के लिए
- George Casella and Roger L. Berger, Statistical Inference
- Larry Wasserman, All of Statistics (2004)