SpL708: Difference between revisions
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| credit_structure = 3-0-0 | | credit_structure = 3-0-0 | ||
| pre_requisites = | | pre_requisites = | ||
| overlaps = MSL721, HUL315/HSL719 | | overlaps = [[MSL721]], [[HUL315]]/[[HSL719]] | ||
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== SpL708 : Statistics for public policy == | == SpL708 : Statistics for public policy == | ||
Probability and statistics Basics (Random variables, Population and sample, Parameters and Estimates, Normal distribution, Sampling distributions of estimates, Confidence intervals); Hypothesis testing (z-test, t-tests, chi square tests, p-values, comparison of means tests); Bivariate linear regression model; Multivariate regression model (Gauss Markov Theorem, Inference and Prediction, ANOVA); Model Specification Errors (Omitted Variables, Irrelevant Variables); Dealing with real-world issues (Multicollinearity, Heteroskedasticity, Outliers); Categorical variables in linear regression (Dummy Variables, Logit/ Probit models); Introduction to Time Series Analysis; Introduction to Panel Data Analysis; Correlation vs Causation. | Probability and statistics Basics (Random variables, Population and sample, Parameters and Estimates, Normal distribution, Sampling distributions of estimates, Confidence intervals); Hypothesis testing (z-test, t-tests, chi square tests, p-values, comparison of means tests); Bivariate linear regression model; Multivariate regression model (Gauss Markov Theorem, Inference and Prediction, ANOVA); Model Specification Errors (Omitted Variables, Irrelevant Variables); Dealing with real-world issues (Multicollinearity, Heteroskedasticity, Outliers); Categorical variables in linear regression (Dummy Variables, Logit/ Probit models); Introduction to Time Series Analysis; Introduction to Panel Data Analysis; Correlation vs Causation. | ||
Latest revision as of 16:46, 14 April 2026
| SpL708 | |
|---|---|
| Statistics for public policy | |
| Credits | 3 |
| Structure | 3-0-0 |
| Pre-requisites | |
| Overlaps | MSL721, HUL315/HSL719 |
SpL708 : Statistics for public policy
Probability and statistics Basics (Random variables, Population and sample, Parameters and Estimates, Normal distribution, Sampling distributions of estimates, Confidence intervals); Hypothesis testing (z-test, t-tests, chi square tests, p-values, comparison of means tests); Bivariate linear regression model; Multivariate regression model (Gauss Markov Theorem, Inference and Prediction, ANOVA); Model Specification Errors (Omitted Variables, Irrelevant Variables); Dealing with real-world issues (Multicollinearity, Heteroskedasticity, Outliers); Categorical variables in linear regression (Dummy Variables, Logit/ Probit models); Introduction to Time Series Analysis; Introduction to Panel Data Analysis; Correlation vs Causation.