# Multiple Regression Analysis with R ## Multiple Regression Analysis with R Udemy Course

Multiple Regression Analysis with R Course Created by Diego Fernandez.

Multiple Regression Analysis with R has 4.4 rating out of 5 based on 39 students. Currently this course has 263 students. Course langwage is English.

## Multiple Regression Analysis with R Course Description

Full course content last updated September 2019

Practice multiple regression analysis by hand with R statistical software using historical stocks, rates, prices, and macroeconomic data. Explore key concepts from basic to expert level to help you achieve better grades, advance your academic career, apply your knowledge in the workplace, or conduct business predictive research. All this while exploring the wisdom of the best scholars and practitioners in the field.

Become an expert in multiple regression analysis in this hands-on course with R.

Define stock-dependent or explanatory variables and calculate mean, standard deviation, skewness, and kurtosis descriptive statistics.

Concisely describe rates, prices, and macroeconomic independent or explanatory variables and calculate descriptive statistics.

Analyze multiple regression statistic output with coefficient of determination or R-squared, adjusted R-squared, and regression standard error metrics.

Review multiple regression analysis of variance via regression, residuals and total degrees of freedom, sum of squares, mean squared error, regression F statistic, and regression p-value.

Examine multiple regression coefficients by value, standard error, t-statistic, and regression coefficient p-value.

Evaluate regression correct specifications through statistical significance of individual coefficients and correct through backward elimination stepwise regression.

A multicollinearity test evaluates the regression for the absence of linear dependence and corrects it with a correct specification re-evaluation.

Evaluate a regression-corrected functional form with the Ramsey-RESET test and correct it with a nonlinear quadratic, logarithmic, or inverse transformation.

The Breusch-Godfrey test evaluates residuals that are not autocorrelated and corrects them by adding the lag dependent variable data as independent variables to the original regression.

Evaluate residual equal variances using the White, Breusch-Pagan test, and correct by estimating the heteroscedasticity consistency standard error.

Evaluate residual normality with the Jarque-Bera test.

Evaluate regression prediction accuracy against random walk and arithmetic mean benchmarks with the Mean Absolute Error, Root Mean Squared Error, and Mean Absolute Percent Error metrics.

Becoming an expert in multiple regression and putting your knowledge into practice

Learning multiple regression is essential for business data science applications in areas such as consumer analytics, finance, banking, healthcare, science, e-commerce, and social media. It is also essential for an academic career in data science, applied statistics, economics, econometrics, or quantitative finance. And required for all business forecasting studies.

However, as the learning curve can become steeper as complexity increases, this process is helpful by guiding you step-by-step using historical data of stocks, rates, prices, and macroeconomics for multiple regression analysis to achieve greater efficiencies. It will be.

Content and overview

This lab course consists of 36 lectures and 3.5 hours of content. Designed for all multiple regression knowledge levels, a basic understanding of R statistical software is useful but not required.

Initially, you will learn how to read stock, rate, price, and macroeconomic historical data to perform multiple regression analysis by installing the relevant packages and running script code in RStudio IDE.

Then define a stock-dependent or explanatory variable. Next, we define independent or explanatory variables through ratios, prices, and macroeconomic domains. It then computes the dependent and independent variable mean, standard deviation, skewness, and kurtosis descriptive statistics. Calculate the independent variable transformation later.

Next, we analyze multiple regression statistics with the coefficient of determination or R-squared, adjusted R-squared, and regression standard error metrics. Then analyze multiple regression analysis of variance or ANOVA with regression, residual and total degrees of freedom, sum of squares, mean squared error, regression F statistic, and regression p-value. Later analyze multiple regression coefficients with regression coefficient values, standard error, t-statistic, and regression coefficient p-value.

We will later evaluate the multiple regression prediction accuracy by dividing the data into training and test ranges. Then follow the steps described in the previous section to use the training range to fit the optimal model. It then uses the best-fitting model coefficient values ​​to make predictions through the test range. Finally, the mean absolute error, root mean square error, and mean absolute percent error metrics evaluate the accuracy of the test coverage predicted values ​​compared to random walking and arithmetic mean benchmarks.

Diego Fernandez has authored high-quality online courses and eBooks on Exfinsis for anyone wishing to become a financial data analyst.

His main areas of expertise are financial analysis and data science. Within financial analysis, he has focused on computer finance, quantitative finance and trading strategy analysis. Within data science, he focused on machine learning, applied statistics, and econometrics. For all of this, he became proficient in Microsoft Excel®, R statistical software® and the Python programming language® analysis tools.

He has significant online business development experience in fast-growing startups and blue-chip companies in several European countries. He always exceeded the expected professional goals by effectively executing a formulated strategy, starting with a comprehensive analysis of the business environment.

He has also achieved outstanding achievements in undergraduate and graduate degrees at world-class academic institutions. These outstanding achievements made him an assistant teacher in specialized subjects and a constant student leader within study groups.

His motivation is a lifelong passion for financial data analysis, which he wants to convey in every step of the way.

## Multiple Regression Analysis with R Course for

• Undergraduates or postgraduates at any knowledge level who want to learn about multiple regression analysis using R statistical software.
• Academic researchers who wish to deepen their knowledge in data science, applied statistics, economics, econometrics or quantitative finance.
• Business data scientists who desire to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.