2023年CFA二級(jí)考試中的Quantitative Methods科目知識(shí)變化還是有的，考生如果參加了2022年CFA二級(jí)考試再備考2023年CFA二級(jí)考試的話，就要注意這個(gè)科目的變化，跟著小編看看2023年這個(gè)科目的考綱情況！

2023年CFA二級(jí)考試

上面是考生備考CFA二級(jí)考試這個(gè)科目章節(jié)要學(xué)習(xí)的知識(shí)對(duì)比，可以看到CFA學(xué)習(xí)章節(jié)增加了，具體是怎么回事呢？跟著小編一起看看！

一、原有的Reading2拆分之后，考綱要求也發(fā)生了變化！

（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，考綱要求發(fā)生變化！

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