# Just the LOS - nothing more, nothing less

In many cases, the CFAI text goes beyond the LOS - which is understandable given that these are text books and not just study guides. That said, how do you draw the line? Many people have suggested that candidates get lost when they stray beyond what is expected and try to learn eveything in the CFAI texts. For example, in Quant there is no mention of the testing for or correcting for heteroskedasticity or multicollinearity. Last year, the tests (ie Durbin-Watson) was explicity in the LOS. I’m reading this (with the definitions of command words as given) that we do not need to know how to correct these issues, much less do any calculations. That said, you could interpret 12i to mean you should be able to recognize which correction to use? Reading 12: Multiple Regression and Issues in Regression Analysis The candidate should be able to: a. formulate a multiple regression equation to describe the relation between a dependent variable and several independent variables, determine the statistical significance of each independent variable, and interpret the estimated coefficients and their p-values; b. formulate a null and an alternative hypothesis about the population value of a regression coefficient, calculate the value of the test statistic, determine whether to reject the null hypothesis at a given level of significance, using a one-tailed or two-tailed test, and interpret the result of the test; c. calculate and interpret 1) a confidence interval for the population value of a regression coefficient and 2) a predicted value for the dependent variable, given an estimated regression model and assumed values for the independent variables; d. explain the assumptions of a multiple regression model; e. calculate and interpret the F-statistic, and discuss how it is used in regression analysis, define, distinguish between, and interpret the R2and adjusted R2in multiple regression, and infer how well a regression model explains the dependent variable by analyzing the output of the regression equation and an ANOVA table; f. formulate a multiple regression equation using dummy variables to represent qualitative factors, and interpret the coefficients and regression results; g. discuss the types of heteroskedasticity and the effects of heteroskedasticity and serial correlation on statistical inference; h. describe multicollinearity and discuss its causes and effects in regression analysis; i. discuss the effects of model misspecification on the results of a regression analysis, and explain how to avoid the common forms of misspecification;

hey, i hear ya. I posted the same thing a few days ago and darkstar responded with a good link to another thread on the subject. I think you’re right it’s frustrating. My opinion though is that it’s a hard enough test as it is for me to take chances and leave points on the table. The reason we start studying early is so we can cover everything thoroughly. I don’t want to take a chance on DW being this year’s Treynor Black so i’m going to study that sh*t like no tomorrow.