Sampling and sampling distributions. Introduction to inference. Point and interval estimation. Hypothesis testing. Small sample distributions (t, X2, F). Introduction to analysis of variance, regression and distribution free methods. Applications using statistical computer programs.
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| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 2.89 | 6 sec · 97 öğr |
| 2024-2025 Fall | 2.07 | 6 sec · 120 öğr |
| 2024-2025 Spring | 1.97 | 11 sec · 249 öğr |
| 2023-2024 Fall | 2.16 | 6 sec · 138 öğr |
| 2023-2024 Spring | 2.05 | 10 sec · 213 öğr |
| 2022-2023 Fall | 1.89 | 6 sec · 112 öğr |
| 2022-2023 Spring | 2.27 | 10 sec · 208 öğr |
| 2021-2022 Fall | 2.06 | 5 sec · 98 öğr |
| 2021-2022 Spring | 2.28 | 9 sec · 179 öğr |
| 2020-2021 Fall | 2.31 | 6 sec · 150 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
There is no compulsory or bonus-bearing attendance for any course activity other than the final exam. Failing to take the final exam returns an FX.
Topics of ECON 221 deferred to ECON 222 if any, Point estimators: Derivation, Properties Confidence intervals: One population Confidence intervals, Hypothesis testing: One population Hypothesis testing: One population Confidence intervals: Two populations Confidence intervals, Hypothesis testing: Two populations Hypothesis testing: Two populations Recap: Confidence intervals and Hypothesis testing Extents of Linear Regression Analysis; econometric modeling; Occam's razor and the principle of parsimony Model of mean, Simple Linear Regression (SLR) model, Multiple Linear Regression (MLR) model; functional forms and elasticity calculations Deriving the estimators of the linear regression parameters, in the model of mean, in SLR and in MLR Goodness of fit; Modeling examples Inference in Linear Regression Analysis: relating the t tests and F tests Model building, reduction and inference ECTS - Workload Table: Activities Number Hours Workload Preparation for Midterm exam 1 12 12 Preparation for Final exam 1 12 12 Laboratory (including preparation) 10 2 20 Course hours 14 3 42 Final exam 1 2,5 2.5 Homework 5 4 20 Midterm exam 1 2,5 2.5 Individual or group work 14 3 42 Total Workload: 153 Total Workload / 30: 153 / 30 5.1 ECTS Credits of the Course: 5 Type of Course: Lecture Course Material: PC - PP - Written Teaching Methods: Lecture