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| LBJ School of Public Affairs - Camp LBJ | |
| Entering Students' Home > Camp LBJ Home > Quantitative Review Schedule |
Modules |
Module 1: Modeling (7/14 - 7/17) |
| Module 2: Differential Calculus (7/19 - 7/27) | |
| Module 3: Probability Theory (7/28 - 7/31) | |
| Module 4: Statistical Inference (8/2 - 8/6) |
Module 1: Modeling |
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| The meaning of
empirical logic in post-modernism; general approach to problem-solving;
relationships between visual image, algebraic thinking, database management,
and statistical analysis. |
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| Concepts: |
Data structure and unit of analysis Types of relationships between variables Dealing with incompleteness & uncertainty of information |
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| Review: | Kachigan
Ch. 1 (Fundamental Concepts) Budnick Ch. 1 - 7 if needed (electronic reserves) |
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| Handout: | Modeling Problem Set |
Session 2 July 16 Quantitative Modeling |
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Concepts of
mathematical models and their applications.
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| Concepts: |
The model-building process Rate of change between variables Linearity, nonlinerarity, and piece-wise linerarity Visualizing algebraic structures in space Average vs marginal concepts |
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| Policy Examples: | Federal
Income Tax Production and cost models |
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| Review: | Budnick
Ch. 1 - 7 if needed (electronic
reserves) |
July
17
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Saturday Problem-Solving Session |
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| Recap of mathematical modeling | ||
| Discussion of modeling Problem Set | ||
Module 2: Differential Calculus |
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Session 3 July 19 Introduction to Differential Calculus |
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| Slope as rate
of change; derivative as slope; and rules of differentiation as shortcuts
for finding derivative. |
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| Concepts: | From
slope of line to slope of curve Limit and continuity Difference quotient vs. rules of differentiation Interpreting derivatives |
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| Review: | Budnick
15.3 - 15.7 |
Session 4 July 21 Simple Optimization |
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| Second and higher derivatives; maximization and minimization;
graphical interpretation. |
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| Concepts: | Determining
optimal outcome 1st and 2nd order conditions: mechanics and intuition Constrained optimization—local & global solutions |
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| Review: | Budnick
15.8; 16.1-16.3 |
Session 5 July 23 Partial Derivatives and Applications |
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| What a partial
derivative is and how to find it;
Lagrange multiplier. |
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| Concepts: | From
slope of curve to slope of surface (and hypersurface) Partial differentiation: Mechanics and intuition Multivariate constrained optimization |
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| Review: | Budnick 20.1-20.2 |
July
24
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Saturday Problem-Solving Session |
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| Recap on differential calculus | ||
| Discussion of Calculus Problem Set | ||
Session 6 July 26 Eureka on Calculus |
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| The review of
quantitative analytic concepts in deterministic models. |
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| Concepts: | Review
of Lagrange Method Summary of differential calculus Intuition on integration (Topic not covered on validation exam) |
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| Review: | Budnick
Chapter 17 (Optimization: Application) |
Module 3: Probability Theory |
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Session 7 July 28 Probability Preliminaries |
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| Basic ideas
of sets and methods of enumeration that form the foundation of probabilities |
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| Concepts: | Events,
outcomes, independence, and mutual exclusiveness Principles of counting, combination, and permutation Joint, conditional, and marginal probabilities |
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| Policy Example: | TBA |
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| Review: | Budnick
13.2-13.3 Kachigan pp. 56-79 |
Session 8 July 30 Probability Distributions |
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| Understanding
and describing theoretical probability distributions. |
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| Concepts: | Bayes'
Theorem and applications Probability distribution and cumulative distribution Normal and binomial distributions as models Describing distributions: central tendency and dispersion |
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| Policy Example: | TBD |
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| Review: | Budnick
13.4; 14.1-14.4 |
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| Handout: | Child
abuse testing problem |
July 31
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Saturday Problem-Solving Session |
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| Recap of Probability Theory | ||
| Discussion of Probability Problem Set | ||
Module 4: Statistical Inferences |
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Session 9 Aug. 2 Applying Statistical Distributions in Sample Study |
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| Distribution of
simple characteristics across population and samples. |
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| Concepts: | Modeling
empirical distribution: Z-score Sampling distribution and central limit theorem |
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| Review: | Kachigan
pp. 79-89 |
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| Handout: | Mock Validation Exams |
Session 10 Aug. 4 Logic of Statistical Inference |
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| Basic logic
and mechanics of statistical estimation and testing. |
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| Concepts: | Precision
vs accuracy Confidence interval Point estimate vs interval estimate Hypothesis testing Mechanics of a simple t-test |
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| Review: | Kachigan
pp. 90 -116 |
Session 11 Aug. 6 Closure on Probability and Inferences |
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| The conceptual
process of inferential research, from thinking about the design to estimating
population information to testing relationships between variables |
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| Concepts: | Intuition:
regression analysis (Not in validation exam) Data management and analytic design Experimental vs statistical vs matching comparisons |
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| Problem-Solving | Discussion of Statistical Inference Problem Set |
August
7
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Saturday Problem-Solving Session will review validation exams: |
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| Discussion of Calculus Mock Exam (10:00 am) | ||
| Discussion of Statistics Mock Exam (2:00 pm) | ||