2020 FRM SchweserNotes Part I Book 2 Quantitative Analysis 2020th Edition by Kaplan Schweser ISBN 1078801935 9781078801935

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Product details:

ISBN 10:  1078801935 
ISBN 13: 978-1078801935 
Author:      Kaplan Schweser

2020 FRM SchweserNotes Part I Book 2 Quantitative Analysis 2020th Table of contents:

Reading 12: Fundamentals of Probability

Exam Focus:

• Basics of probability concepts, including events, outcomes, and sample spaces.
• Key techniques for computing probabilities and understanding their properties.

Modules:

• 12.1: Introduction to probability.
• 12.2: Conditional, unconditional, and joint probabilities.
  • Key Concepts: Understanding how probabilities relate under different conditions (e.g., conditional probability, joint probability, independent events).
  • Answer Key for Module Quizzes: Check for practice problems on these concepts.

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Reading 13: Random Variables

Exam Focus:

• Understanding the different types of random variables and how they are used to model uncertainty in outcomes.

Modules:

• 13.1: Probability mass functions (PMF), cumulative distribution functions (CDF), and expected values (mean).
• 13.2: Statistical measures: mean, variance, skewness, and kurtosis.
• 13.3: Probability density functions (PDF), quantiles, and linear transformations.
  • Key Concepts: Be able to calculate expected values, understand distributions, and transformations.
  • Answer Key for Module Quizzes: Focus on PMF, CDF, variance, skewness, and kurtosis for practice.

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Reading 14: Common Univariate Random Variables

Exam Focus:

• Familiarize yourself with the key distributions used in statistics and their properties.

Modules:

• 14.1: Uniform, Bernoulli, Binomial, and Poisson distributions.
• 14.2: Normal and lognormal distributions.
• 14.3: Student’s t, Chi-squared, and F-distributions.
  • Key Concepts: Know how to calculate probabilities and use these distributions in real-world scenarios.
  • Answer Key for Module Quizzes: Focus on the properties and applications of each distribution.

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Reading 15: Multivariate Random Variables

Exam Focus:

• Multivariate distributions, especially the relationships between multiple random variables.

Modules:

• 15.1: Marginal and conditional distributions for bivariate distributions.
• 15.2: Moments of bivariate random distributions.
• 15.3: Behavior of moments for bivariate random variables.
• 15.4: Independent and identically distributed random variables (i.i.d.).
  • Key Concepts: Learn how to calculate and interpret joint, marginal, and conditional distributions, and understand the concept of independence.
  • Answer Key for Module Quizzes: Pay attention to joint and conditional probabilities.

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Reading 16: Sample Moments

Exam Focus:

• Estimating sample moments, particularly mean and variance.

Modules:

• 16.1: Estimating mean, variance, and standard deviation from sample data.
• 16.2: Estimating higher-order moments (e.g., skewness, kurtosis).
  • Key Concepts: Understand how to compute sample moments and what they tell you about the data distribution.
  • Answer Key for Module Quizzes: Review exercises on moment estimation.

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Reading 17: Hypothesis Testing

Exam Focus:

• Hypothesis testing procedures, significance levels, and interpreting test results.

Modules:

• 17.1: Hypothesis testing basics: null and alternative hypotheses, significance levels.
• 17.2: Hypothesis testing results: p-values, test statistics, and conclusions.
  • Key Concepts: Be able to conduct basic hypothesis tests (e.g., t-tests, chi-squared tests), interpret p-values, and make decisions based on test results.
  • Answer Key for Module Quizzes: Focus on formulating hypotheses and interpreting test statistics.

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Reading 18: Linear Regression

Exam Focus:

• Understanding simple linear regression and ordinary least squares (OLS) estimation.

Modules:

• 18.1: Regression analysis basics: simple linear regression models.
• 18.2: Ordinary Least Squares (OLS) estimation and interpretation.
• 18.3: Hypothesis testing in regression analysis.
  • Key Concepts: Know how to estimate and interpret regression coefficients, test hypotheses in regression, and assess model fit.
  • Answer Key for Module Quizzes: Practice interpreting OLS results and hypothesis tests for regression coefficients.

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Reading 19: Regression with Multiple Explanatory Variables

Exam Focus:

• Expanding on simple regression to include multiple predictors.

Modules:

• 19.1: Multiple regression models: multiple independent variables.
• 19.2: Measures of fit in multiple regression: R-squared, adjusted R-squared.
  • Key Concepts: Be comfortable with interpreting multiple regression output and understanding the role of each predictor variable.
  • Answer Key for Module Quizzes: Focus on interpreting coefficients and model fit metrics in multiple regression.

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Reading 20: Regression Diagnostics

Exam Focus:

• Diagnosing issues in regression models, such as heteroskedasticity and multicollinearity.

Modules:

• 20.1: Heteroskedasticity and multicollinearity: what they are and how to detect them.
• 20.2: Model specification errors.
  • Key Concepts: Learn to identify and address issues that can affect the validity of your regression models.
  • Answer Key for Module Quizzes: Review diagnostic tools like residual plots and variance inflation factors (VIF).

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Reading 21: Stationary Time Series

Exam Focus:

• Analyzing time series data, focusing on stationary processes.

Modules:

• 21.1: Covariance stationary processes: definition and examples.
• 21.2: Autoregressive (AR) and moving average (MA) models.
• 21.3: Autoregressive moving average (ARMA) models.
  • Key Concepts: Understand how to model and forecast stationary time series data.
  • Answer Key for Module Quizzes: Practice with AR, MA, and ARMA models.

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Reading 22: Non-Stationary Time Series

Exam Focus:

• Handling time series that exhibit trends or seasonal patterns.

Modules:

• 22.1: Time trends and their impact on time series analysis.
• 22.2: Seasonality in time series data.
• 22.3: Unit roots and their importance.
  • Key Concepts: Learn how to handle non-stationary data and apply techniques like differencing to make data stationary.
  • Answer Key for Module Quizzes: Focus on testing for unit roots and removing trends.

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Reading 23: Measuring Return, Volatility, and Correlation

Exam Focus:

• Key financial measures for analyzing time series data in finance, such as returns and volatility.

Modules:

• 23.1: Defining returns and volatility.
• 23.2: Normal and non-normal distributions in finance.
• 23.3: Measuring correlations and dependence.
  • Key Concepts: Be able to calculate returns, understand volatility models, and measure relationships between variables using correlation.
  • Answer Key for Module Quizzes: Focus on calculating returns and volatility.

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Reading 24: Simulation and Bootstrapping

Exam Focus:

• Using simulation techniques to model uncertainty and apply bootstrapping for statistical inference.

Modules:

• 24.1: Monte Carlo simulation and reducing sampling errors.
• 24.2: Bootstrapping and random number generation for inference.
  • Key Concepts: Understand how simulation and bootstrapping are used to estimate distributions and conduct hypothesis tests.
  • Answer Key for Module Quizzes: Practice using Monte Carlo methods and bootstrapping techniques.

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