-a. Describe a simple linear regression model and the roles of the dependent and independent variables in the model;<\/a><\/em><\/p>\n -b. Describe the least squares criterion, how it is used to estimate regression coefficients, and their interpretation;<\/a><\/em><\/p>\n -c. Explain the assumptions underlying the simple linear regression model and describe how residuals and residual plots indicate if these assumptions may have been violated;<\/a><\/em><\/p>\n \u00a0-d. Calculate and interpret the coefficient of determination and the F-statistic in a simple linear regression;<\/a><\/em><\/p>\n -e. Describe the use of analysis of variance (ANOVA) in regression analysis, interpret ANOVA results, and calculate and interpret the standard error of estimate in a simple linear regression;<\/em><\/a><\/em><\/p>\n -f. Formulate a null and alternative hypothesis about a population value of a regression coefficient, and determine whether the null hypothesis is rejected at a given level of significance;<\/a><\/em><\/p>\n -g. Calculate and interpret the predicted value for the dependent variable, and a prediction interval for it, given an estimated linear regression model and a value for the independent variable;<\/a><\/em>\u00a0<\/p>\n -h. Describe different functional forms of simple linear regressions;<\/a><\/em><\/p>\n -a. Describe the types of investment problems addressed by multiple linear regression and the regression process;<\/a><\/em><\/p>\n -b. Formulate a multiple linear regression model, describe the relation between the dependent variable and several independent variables, and interpret estimated regression coefficients;<\/a><\/em><\/p>\n -c. Explain the assumptions underlying a multiple linear regression model and interpret residual plots indicating potential violations of these assumptions;<\/a><\/em><\/p>\n -a. Evaluate how well a multiple regression model explains the dependent variable by analyzing ANOVA table results and measures of goodness of fit;<\/a><\/em><\/p>\n -b. Formulate hypotheses on the significance of two or more coefficients in a multiple regression model and interpret the results of the joint hypothesis tests;<\/a><\/em><\/p>\n -c. Calculate and interpret a predicted value for the dependent variable, given the estimated regression model and assumed values for the independent variable;<\/a><\/em><\/p>\n -a. Describe how model misspecification affects the results of a regression analysis and how to avoid common forms of misspecification;<\/a><\/em><\/p>\n -b. Explain the types of heteroskedasticity and how it affects statistical inference;<\/a><\/em><\/p>\n -c. Explain serial correlation and how it affects statistical inference;<\/a><\/em><\/p>\n -d. Explain multicollinearity and how it affects regression analysis;<\/a><\/em><\/p>\n -a. Describe Influence analysis and methods of detecting influential data points;<\/a><\/em><\/p>\n -b. Formulate and interpret a multiple regression model that includes qualitative independent variables;<\/a><\/em><\/p>\n -c. Formulate and interpret a logistic regression model;<\/a><\/em><\/p>\n -a. Calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients;<\/em><\/a><\/p>\n -b. Describe factors that determine whether a linear or a log-linear trend should be used with a particular time series and evaluate limitations of trend models;<\/em><\/a><\/p>\n -c. Explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary;<\/em><\/a><\/p>\n -d. Describe the structure of an autoregressive (AR) model of order <\/em>p and calculate one- and two period-ahead forecasts given the estimated coefficients;<\/em><\/a><\/p>\n -e. Explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series;<\/em><\/a><\/p>\n -f. Explain mean reversion and calculate a mean-reverting level;<\/em><\/a><\/p>\n -g. Contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion;<\/em><\/a><\/p>\n -h. Explain the instability of coefficients of time-series models;<\/em><\/a><\/p>\n -i. Describe characteristics of random walk processes and contrast them to covariance stationary processes;<\/em><\/a><\/p>\n -j. Describe implications of unit roots for time-series analysis, explain when unit-roots are likely to occur and how to test for them, and demonstrate how a time series with a unit root can be transformed so it can be analyzed with an AR model;<\/em><\/a><\/p>\n -k. Describe the steps of the unit root test for nonstationary and explain the relation of the test to autoregressive time-series models;<\/em><\/a><\/p>\n -l. Explain how to test and correct for seasonality in a time-series model and calculate and interpret a forecasted value using an AR model with a seasonal lag;<\/em><\/a><\/p>\n -m. Explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series;<\/em><\/a><\/p>\n -n. Explain how time-series variables should be analyzed for nonstationary and\/or cointegration before use in linear regression;<\/em><\/a><\/p>\n -o. Determine an appropriate time-series model to analyze a given investment problem and justify that choice;<\/em><\/a><\/p>\n -a. Describe supervised machine learning, unsupervised machine learning, and deep learning;<\/em><\/a><\/p>\n -b. Describe overfitting and identify methods of addressing it;<\/em><\/a><\/p>\n -c. Describe supervised machine learning algorithms\u2014including penalized regression, support vector machine, k-nearest neighbor, classification and regression tree, ensemble learning, and random forest\u2014and determine the problems for which they are best suited;<\/em><\/a><\/p>\n -d. Describe unsupervised machine learning algorithms\u2014including principal components analysis, K-means clustering, and hierarchical clustering\u2014and determine problems for which they are best suited;<\/em><\/a><\/p>\n -e. Describe neural networks, deep learning nets, and reinforcement learning;<\/em><\/a><\/p>\n -a. Identify\u00a0and explain steps in a data analysis project;<\/em><\/a><\/p>\n -b. Describe objectives, steps, and examples of preparing and wrangling data;<\/em><\/a><\/p>\n -c. Describe objectives, methods, and examples of data exploration;<\/em><\/a><\/p>\n -d. Describe objectives, steps, and techniques in model training;<\/em><\/a><\/p>\n -e.\u00a0Describe preparing, wrangling, and exploring text-based data for financial forecasting;<\/em><\/a><\/p>\nLearning Module 1: Basics of Multiple Regression and Underlying Assumptions<\/h2>\n
Learning Module 2: Evaluating Regression Model Fit and Interpreting Model Results<\/h2>\n
Learning Module 3: Model Misspecification<\/h2>\n
Learning Module 4: Extensions of Multiple Regression<\/h2>\n
Learning Module 5: Time Series Analysis<\/h2>\n
Learning Module 6: Machine Learning<\/h2>\n
Learning Module 7: Big Data Projects<\/h2>\n