2023年CFA二级考试中的Quantitative Methods科目知识变化还是有的，考生如果参加了2022年CFA二级考试再备考2023年CFA二级考试的话，就要注意这个科目的变化，跟着小编看看2023年这个科目的考纲情况！

2023年CFA二级考试

上面是考生备考CFA二级考试这个科目章节要学习的知识对比，可以看到CFA学习章节增加了，具体是怎么回事呢？跟着小编一起看看！

一、原有的Reading2拆分之后，考纲要求也发生了变化！

（1）Basics of Multiple Regression and Underlying Assumptions

describe the types of investment problems addressed by multiple linear regression and the regression process

formulate a multiple linear regression model, describe the relation between the dependent variable and several independent variables, and interpret estimated egression coefficients

explain the assumptions underlying a multiple linear regression model and interpret residual plots indicating potential violations of these assumptions

（2）Evaluating Regression Model Fit and Interpreting Model Results

evaluate how well a multiple regression model explains the dependent variable by analyzing ANOVA table results and measures of goodness of fit

formulate hypotheses on the significance of two or more coefficients in a multiple regression model and interpret the results of the joint hypothesis tests

calculate and interpret a predicted value for the dependent variable, given the estimated regression model and assumed values for the independent variable

（3）Model Misspecification

describe how model misspecification affects the results of a regression analysis and how to avoid common forms of misspecification

explain the types of heteroskedasticity and how it affects statistical inference

explain serial correlation and how it affects statistical inference

explain multicollinearity and how it affects regression analysis

（4）Extensions of Multiple Regression

describe influence analysis and methods of detecting influential data points

formulate and interpret a multiple regression model that includes qualitative


二、Big Data Projects原有的Reading5，考纲要求发生变化！

Big Data Projects

identify and explain steps in a data analysis project

describe objectives, steps, and examples of preparing and wrangling data

evaluate the fit of a machine learning algorithm

describe objectives, methods, and examples of data exploration

describe methods for extracting, selecting and engineering features from textual data

describe objectives, steps, and techniques in model training

describe preparing, wrangling, and exploring text-based data for financial forecasting independent variables