*Last Update: September, 2019*

**1. Course Objective**

Learn Multiple Regression Analysis main topics using Microsoft Excel® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.

**2. Skills Learned**

At the end of this course you will know how to:

- Define dependent variable, independent variables and calculate their descriptive statistics (mean, standard deviation, skewness, kurtosis).
- Analyze multiple regression statistics output (coefficient of determination or R squared, adjusted R squared, regression standard error), analysis of variance ANOVA (regression, residuals and total degrees of freedom, sum of squares, mean squared error, regression F statistic, regression p-value) and coefficients (values, standard errors, t statistics and regression coefficients p-values).
- Evaluate regression correct specification (individual coefficients statistical significance), independent variables no linear dependence (multicollinearity test), correct functional form (Ramsey-RESET test), residuals no autocorrelation (Breusch-Godfrey test), residuals homoscedasticity (White test) and residuals normality (Jarque-Bera test).
- Correct regression correct specification (backward elimination stepwise regression), independent variables no linear dependence (correct specification re-evaluation), correct functional form (non-linear quadratic, logarithmic or reciprocal variables transformations), residuals no autocorrelation (adding lagged dependent variable data as independent variables to original regression) and residuals homoscedasticity (heteroscedasticity consistent standard errors estimation).
- Assess regression forecasting accuracy (mean absolute error, root mean squared error, mean absolute percentage error) and compare it with benchmarks (random walk, arithmetic mean).

**3. Typical Student**

This course is ideal for you as:

- Undergraduate or postgraduate who wants to learn about the subject.
- Academic researcher who wishes to deepen your knowledge in data science, applied statistics, economics, econometrics or quantitative finance.
- Business data scientist who desires to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.