Quantitative Analysis Study Outline

Lessons in this path

1. 1 Set Theory Foundations for Quantitative Reasoning Define sets and their importance, distinguish common types of sets such as empty, singleton, equal, disjoint, universal, subset, and complement sets, and perform union and intersection operations using simple Venn-diagram-based business examples. 45 min Explanation
2. 2 Time Series Components and Basic Forecasting Methods Identify the components of a time series—trend, seasonal, cyclical, and random variation—and apply forecasting approaches referenced in the handout, including weighted moving averages, linear trend, and exponential smoothing, to support short-term planning decisions. 75 min
3. 3 Regression Analysis: Purpose, Assumptions, and Model Form Explain applications of regression analysis such as forecasting, trend analysis, and estimating the CAPM characteristic line; write simple and multiple regression equations; and state key assumptions and limitations of the regression model. 60 min
4. 4 Simple Regression Diagnostics and Goodness of Fit Compute and interpret coefficient of determination (r²), correlation coefficient (r), standard error of estimate, and prediction intervals from a simple regression example, and relate explained, unexplained, and total variation. 90 min
5. 5 Testing Regression Significance with t and F Statistics Formulate null and alternative hypotheses for regression significance, calculate and interpret the F test for overall model significance, compute the standard error of slope, and use t tests to judge whether a predictor significantly explains the dependent variable. 90 min
6. 6 Multiple Regression Interpretation and ANOVA Output Interpret computer output for multiple regression by reading coefficients, standard errors, t-ratios, regression and residual degrees of freedom, sums of squares, mean squares, adjusted R², and overall F-values, then decide which predictors are significant. 90 min
7. 7 ANOVA Concepts and Conditions Describe analysis of variance in terms of regression and residual variation, degrees of freedom, mean sum of squares, and F-calculated, and state the listed assumptions for two-way ANOVA: approximate normality, independence, equal variances, and equal group sizes. 45 min
8. 8 Applied Quantitative Analysis for Decision and Forecasting Problems Integrate set operations, time-series forecasting, regression interpretation, and ANOVA-style significance testing to analyze business-oriented quantitative problems such as staffing decisions, prediction models, and explanatory power of variables. 60 min