Quantitative Methods - CFA Smart Tips To Pass Your L2 Exam

This training is extremely different from the other programs and has been battle tested by thousands of CFA Candidates.

No matter where you stand in your preparation this program is powerful and will give you the confidence and the elements you are missing.

The program has been designed to make complicated concepts easy. It is especially intended for those who have difficulties going through the material alone.

Whatever the material you like and picked for your preparation, this program will help you take the most out of your investment and time spent actually studying.

Investing in this training is like getting a personal one-on-one helicopter ride to the top of the mountain. You don't have to climb it yourself.

This section is all about the Analysis of Variance (ANOVA) Table. ANOVA is a highly testable topic for the CFA Level 2 Quants section. ANOVA table shows the calculated values for acronyms like SSE, SST, RSS, MSE, MSR, and SEE.

Before we dive into the details and calculations of all these acronyms, let's first discuss least squares regression. This lecture gives you an introduction to regression analysis and the line of best fit.

This lecture discusses the concept of Sum of Squared Errors (SSE) from the ANOVA table. Watch this lecture to understand the concept and calculation of SSE.

The next two concepts from the ANOVA table are Regression Sum of Squares (RSS) and Total Sum of Squares (SST). Watch and learn these fundamental concepts to understand regression analysis.

The concept and calculation of degrees of freedom (df) for SSE, RSS, and SST are discussed in this lecture. Students are advised to open the worksheet attached to this lecture. It contains two tables. The first table shows the formulas to calculate degrees of freedom for any number of variables (k) in regression analysis and the second table shows degrees of freedom for a linear regression model.

Throughout this course we will celebrate your progress at 25%, 50%, 75% and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>

The next three acronyms from the ANOVA table are discussed and explained in this lecture:

1. Mean Squared Error (MSE)

2. Mean Regression Sum of Squares (MSR)

3. Standard Error of Estimate (SEE)

Lastly, the Coefficient of Determination (R square) (another crucial concept from the ANOVA table) is discussed. The concept of Multiple Regression is also discussed.

This lecture is an introduction to the concept of Linear Regression. The ordinary least squares method is used to find the line of best fit for linear regression. Linear regression involves two variables: Dependent and Independent. The independent variable is used to explain the variation in the dependent variable. For example, a house's value can be taken as a dependent variable and a person's income can be taken as an independent variable.

This lecture is a recap of the discussion of all the terms used in an ANOVA Table. The terms include:

1. Sum of Squared Errors (SSE)

2. Regression Sum of Squares (RSS)

3. Sum of Squared errors Total (SST)

4. Mean Regression Sum of Squares (MSR)

5. Mean Squared Error (MSE)

6. Standard Error of Estimate (SEE)

7. Coefficient of Determination (R Square)

A Confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated confidence level; the 95% confidence level is most common, but other levels, such as 90% or 99%, are sometimes used.

Hypothesis Testing for a Regression Coefficient may use the Confidence Interval for the coefficient being tested. For instance, a frequently asked question is whether an estimated slope coefficient is statistically different from zero. In other words, the null hypothesis is H0: b1 = 0 and the alternative hypothesis is Ha: b1 ≠ 0. If the confidence interval at the desired level of significance does not include zero, the null is rejected, and the coefficient is said to be statistically different from zero.

Confidence intervals for the Predicted Value of a dependent variable are calculated in a manner similar to the confidence interval for the regression coefficients.

Throughout this course we will celebrate your progress at 25%, 50%, 75% and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>

The closer the correlation coefficient is to plus or minus one, the stronger the correlation. With the exception of these extremes (i.e., r = ±1.0), we cannot really speak of the strength of the relationship indicated by the correlation coefficient without a statistical test of significance.

For our purposes, we want to test whether the correlation between the population of two variables is equal to zero. Using the lowercase Greek letter rho (ρ) to represent the population parameter, the appropriate null and alternative hypotheses can be structured as a two-tailed test as follows: H0: ρ = 0 versus Ha: ρ ≠ 0

In this lecture, we will use an example to understand the concept of testing the statistical significance of the correlation coefficient. Remember, we will use a t-test. The value of the t-statistic will be compared with the critical values of the t-distribution for a given level of significance and degrees of freedom.

Any conclusions regarding the importance of an independent variable in explaining a dependent variable require determining the statistical significance of the slope coefficient. Simply looking at the magnitude of the slope coefficient does not address the issue of the importance of the variable. A hypothesis test must be conducted, or a confidence interval must be formed, to assess the importance of the variable.

The p-value is the smallest level of significance for which the null hypothesis can be rejected. An alternative method of doing hypothesis testing of the coefficients is to compare the p-value to the significance level:

• If the p-value is less than the significance level, the null hypothesis can be rejected.

• If the p-value is greater than the significance level, the null hypothesis cannot be rejected.

An F-test assesses how well the set of independent variables, as a group, explains the variation in the dependent variable. That is, the F-statistic is used to test whether at least one of the independent variables explains a significant portion of the variation of the dependent variable.

This lecture discusses the first two assumptions of multiple regression analysis.

Out of the total six assumptions of the multiple regression analysis, the last four are discussed in this lecture.

Throughout this course we will celebrate your progress at 25%, 50%, 75% and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>

Jhan Berger uses what he calls "Jhanism" and invents intuitive denotations and acronyms for the three common and notorious violations of regression analysis.  Resultantly, an acronym is created in this lecture i.e. HAM.

1. Heteroskedasticity  (H) ----> ε2

2. Auto Correlation       (A) ----> ε:ε

3. Multicollinearity       (M) -- -> X1:X2

This lecture focuses on the first factor of the acronym HAM i.e. 'H' => Heteroskedasticity. Learn:

• What is Heteroskedasticity?

• Conditional Heteroskedasticity vs Unconditional Heteroskedasticity

This lecture discusses the two methods of detecting Hetroskedasticity:

1. Examining Scatter Plots of the Residuals

2. Breusch-Pagan chi-square Test

You will learn two different methods of correcting heteroskedasticity in this lecture:

1. Robust Standard Errors

2. Generalized Least Squares

This lecture focuses on the second factor of the acronym HAM i.e. 'A' => Auto Correlation. Learn:

• What is Auto/Serial Correlation?

• Positive Serial Correlation vs Negative Serial Correlation

• Effect of Auto Correlation on Regression Analysis

Watch and learn two different ways of detecting autocorrelation in a regression model:

1. Residual Plot

2. Durbin Watson Test

• Watch and learn different Methods of Correcting Autocorrelation in a regression model.

• Learn about the third factor 'M' in HAM: Multicollinearity

You will learn from this lecture two different methods of detecting multicollinearity in a regression model:

1. High Correlation Coefficient between two independent variables

2. F-Test vs Individual t-tests

Students will learn different methods of correcting multicollinearity in this lecture, including:

1. Dropping a variable

2. Step-wise Regression

Throughout this course we will celebrate your progress at 25%, 50%, 75% and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>

In this introduction to the webinar interview, Jhan explains his background as a CFA trainer. He explains what the CFA is, as well as other, similar qualifications that people consider. Jhan calls CFA the "gold standard" in the finance industry, especially with the top firms at which he has been spending many years training employees.

Jhan gives advice for people thinking about getting into CFA and he emphasizes the investment in time that it will require. He compares CFA to the other qualifications which are more expensive but require less time and are less well known. Jhan talks about the number of hours required and the different levels of the CFA.

An audience member asks about how university finance degrees fit with the CFA curriculum. Jhan explains that he does a lot of work with universities and although CFA is a post graduate qualification, often universities will give students time and support towards their CFA exams. Another student asked what advice Jhan would give to be prepared for their Level 1 exam in a few months time.

Peter asks Jhan about his unique teaching style for CFA and why he started recording all his teaching lessons. Jhan talks about how he had a non-financial background and has developed very interesting and memorable ways of teaching, precisely because he had to work it all out for himself. His lessons focus on a way of of learning rather than adding to the high volume of material that CFA students already have to deal with.

An audience member with a BCom in Financial Management asks if CFA would be useful in pursuing a career in financial crime. Jhan explains that CFA has such huge credibility in the industry and it's the world's largest and most recognised financial literacy qualification which would definitely prepare a student for many financial careers.

Jhan answers a question and provides the career options for CFA; wholesale finance, portfolio analysts and roles on the "buy side". He emphasizes that any financial centre such as London, New York or Shanghai will recognise the CFA "passport".

An engineer asks about how complex the accountancy is in the exam and how long it would take to cover that aspect of the exam. Jhan explains that there are many engineers who have done CFA and that engineering is also an analytical career, just like CFA. Jhan doesn't teach in the traditional accountancy format, like a book keeper, he uses real life examples and helps people make sense of the economic realities of investment decisions.

Peter talks about how he found Jhan's accountancy lecture to be very authentic, which uses the "ALE" mnemonic (Assets = Liabilities + Equity). An audience member asks whether Jhan would recommend a CFA over finance honours. Watch to find out Jhan's answer.

Peter quizzes Jhan on the support that he plans to provide students. Jhan explained:

(1) It's all about community, which is available in the CFA Secrets Facebook group and how people will be able get more help and also help each other.

(2) Support and access to experts through the group and online courses which will be rolled out in the coming months to give CFA students the topic based teaching they need.

Jhan explains how his courses are not "cover to cover" but rather focus on the critical areas that will ensure you pass.

An audience member explains that he failed the CFA Level 1 5 years ago and Peter asks Jhan if he can help this person. Jhan gives the data on the very low pass rates for CFA but is very proud of his own student pass rates which is very high in the industry. Peter mentions the endorsements that Jhan has on his LinkedIn page which are an indication of the credibility that Jhan has in the industry. Jhan offers everyone to connect with him on LinkedIn.

Jhan explains the options after passing each of the levels of CFA. Tracey Ashington joined the webinar interview; she is a graduate recruiter for large international banks and explained that CFA is always the most reliable finance qualification that banks look for.

Peter and Tracey announce the winner of the laptop and there was a big surprise: another laptop had become available so Tracey was able to give another one away, based on the rules of the competition. Then Jhan gives a laptop away to the first person who was able to answer the question about how many levels there are in CFA.

In this one hour interview, Peter and Jhan talk extensively about CFA teaching, careers, his teaching techniques and Jhan answers a number of questions from the audience. Learn more about CFA from one of the most respected CFA trainers in the industry and find out more about the teaching content, community and support he is making available to help even more people achieve CFA success. You will also see an audience member win a laptop by answering a question in the webinar.

The CFA exams are 6 weeks away and we are here to help. Jhan Burger is one of the industry's leading CFA instructors with satisfied students all around the world. Make sure you have the right plan to pass, join us in this webinar and get the best advice and support for your success!