As the companion piece to Quantitative Investment Analysis , this workbook aligns with the text chapter-by-chapter to give you the focused, targeted exercises you need to fully understand each topic. Each chapter explicitly lays out the learning objectives so you understand the 'why' of each problem, and brief chapter summaries help refresh your memory on key points before you begin working.

The practice problems themselves reinforce the practitioner-oriented text, and are designed to mirror the real-world problems encountered every day in the field. Solutions are provided to all of the problems for those who self-study, and an optional online Instructor's manual brings this book into the classroom with ease.

### Copyright:

Quantitative analysis is essential to the investment process, but hypothetical 'pie-in-the-sky' type practice scenarios only waste your time. You need a globally relevant application guide with roots in the real-world industry, so you can spend your time preparing for scenarios that you'll actually encounter. This workbook is your answer, with practice problems covering the complete range of quantitative methods. Refresh your memory with succinct chapter summaries Enhance your understanding with topic-specific practice problems Work toward explicit chapter objectives to internalize key information Practice important techniques with real-world applications Consistent mathematical notation, topic coverage continuity, and evenness of subject matter treatment are critical to the learning process.

This workbook lives up to its reputation of clarity, and provides investment-oriented practice based on actual changes taking place in the global investment community. For those who want a practical route to mastering quantitative methods, the Quantitative Investment Analysis Workbook is your real-world solution. ChaPter 1. ChaPter 9. ChaPter Topics in Probability 4. Principles of Counting 5. Introduction to Common Probability Distributions 2. Discrete Random Variables 2. The Discrete Uniform Distribution 2.

The Binomial Distribution 3. Continuous Random Variables 3. Continuous Uniform Distribution 3. The Normal Distribution 3. Applications of the Normal Distribution 3.

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The Lognormal Distribution 4. Monte Carlo Simulation 5. Sampling 2. Simple Random Sampling 2. Stratified Random Sampling 2.

Time-Series and Cross-Sectional Data 3. Distribution of the Sample Mean 3. The Central Limit Theorem 4.

## Quantitative Investment Analysis Workbook - altopertookur.ml

Point and Interval Estimates of the Population Mean 4. Point Estimators 4. Confidence Intervals for the Population Mean 4. Selection of Sample Size 5. More on Sampling 5. Data-Mining Bias 5.

Sample Selection Bias 5. Look-Ahead Bias 5. Time-Period Bias 6. Hypothesis Testing 3. Hypothesis Tests Concerning the Mean 3. Tests Concerning a Single Mean 3. Tests Concerning Differences between Means 3. Tests Concerning Mean Differences 4. Hypothesis Tests Concerning Variance 4.

## Quantitative Investment Analysis Workbook

Tests Concerning a Single Variance 4. Other Issues: Nonparametric Inference 5. Nonparametric Inference: Summary 6. Correlation Analysis 2. Scatter Plots 2. Calculating and Interpreting the Correlation Coefficient 2. Limitations of Correlation Analysis 2. Uses of Correlation Analysis 2. Testing the Significance of the Correlation Coefficient 3. Linear Regression 3. Linear Regression with One Independent Variable 3. Assumptions of the Linear Regression Model 3. The Standard Error of Estimate 3.

The Coefficient of Determination 3. Prediction Intervals 3. Limitations of Regression Analysis 4. Multiple Linear Regression 2. Assumptions of the Multiple Linear Regression Model 2. Adjusted R2 3. Using Dummy Variables in Regressions 4.

Violations of Regression Assumptions 4. Heteroskedasticity 4. Serial Correlation 4. Multicollinearity 4. Model Specification and Errors in Specification 5. Principles of Model Specification 5. Misspecified Functional Form 5. Other Types of Time-Series Misspecification 6.

Models with Qualitative Dependent Variables 7. Introduction to Time-Series Analysis 2. Challenges of Working with Time Series 3. Trend Models 3. Linear Trend Models 3. Log-Linear Trend Models 3. Trend Models and Testing for Correlated Errors 4. Covariance-Stationary Series 4. Mean Reversion 4. Multiperiod Forecasts and the Chain Rule of Forecasting 4. Comparing Forecast Model Performance 4. Instability of Regression Coefficients 5.

Random Walks and Unit Roots 5. Random Walks 5. The Unit Root Test of Nonstationarity 6.