Multiple Regression Analysis with R
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# Multiple Regression Analysis with R

Learn multiple regression analysis main concepts from basic to expert level through a practical course with R.
4.0 (10 ratings)
54 students enrolled
Created by Diego Fernandez
Last updated 5/2017
English
Current price: \$10 Original price: \$50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
• 3.5 hours on-demand video
• 7 Articles
• 7 Supplemental Resources
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• Define stocks dependent or explained variable and calculate its mean, standard deviation, skewness and kurtosis descriptive statistics.
• Standardize rates, prices and macroeconomic independent or explanatory variables by calculating their mean and standard deviation descriptive statistics.
• Analyze multiple regression statistics output through coefficient of determination or R square, adjusted R square and regression standard error metrics.
• Examine multiple regression analysis of variance through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression F statistical significance.
• Review multiple regression coefficients through their value, standard error, t statistic and t statistical significance or p-value.
• Evaluate regression correct specification through individual coefficients statistical significance and correct it through backward elimination stepwise regression.
• Assess regression no linear dependency through multicollinearity test and correct it through correct specification re-evaluation.
• Appraise regression correct functional form through Ramsey-RESET test and correct it through non-linear quadratic, logarithmic or reciprocal variable transformations.
• Evaluate residuals normality through Jarque-Bera test.
• Assess residuals no autocorrelation through Breusch-Godfrey test and correct it by adding lagged dependent data as independent variables to original regression.
• Appraise residuals homoscedasticity through White, Breusch-Pagan tests and correct it through Box-Cox transformation of dependent variable.
• Test regression forecasting accuracy against random walk benchmark through mean absolute error, root mean square error, mean absolute percentage error and mean absolute scaled error metrics.
View Curriculum
Requirements
• Practical example data and R script code files provided with the course.
• Prior basic R statistical software knowledge is useful but not required.
Description

Learn multiple regression analysis through a practical course with R using real world data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. All of this while exploring the wisdom of best academics and practitioners in the field.

Become a Multiple Regression Analysis Expert in this Practical Course with R

• Define stocks dependent or explained variable and calculate its mean, standard deviation, skewness and kurtosis descriptive statistics.
• Standardize rates, prices and macroeconomic independent or explanatory variables by calculating their mean and standard deviation descriptive statistics.
• Analyze multiple regression statistics output through coefficient of determination or R square, adjusted R square and regression standard error metrics.
• Examine multiple regression analysis of variance through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression F statistical significance.
• Review multiple regression coefficients through their value, standard error, t statistic and t statistical significance or p-value.
• Evaluate regression correct specification through individual coefficients statistical significance and correct it through backward elimination stepwise regression.
• Assess regression no linear dependency through multicollinearity test and correct it through correct specification re-evaluation.
• Appraise regression correct functional form through Ramsey-RESET test and correct it through non-linear quadratic, logarithmic or reciprocal variable transformations.
• Evaluate residuals normality through Jarque-Bera test.
• Assess residuals no autocorrelation through Breusch-Godfrey test and correct it by adding lagged dependent data as independent variables to original regression.
• Appraise residuals homoscedasticity through White, Breusch-Pagan tests and correct it through Box-Cox transformation of dependent variable.
• Test regression forecasting accuracy against random walk benchmark through mean absolute error, root mean square error, mean absolute percentage error and mean absolute scaled error metrics.

Become a Multiple Regression Analysis Expert and Put Your Knowledge in Practice

Learning multiple regression analysis is indispensable for business analysis, financial analysis or data science applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data science, applied statistics, economics, econometrics or quantitative finance. And it is necessary for any business forecasting related decision.

But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.

Content and Overview

This practical course contains 36 lectures and 3.5 hours of content. It’s designed for all multiple regression analysis knowledge levels and a basic understanding of R is recommended.

At first, you’ll define stocks dependent or explained variable. After that, you’ll define independent or explanatory variables through their rates, prices and macroeconomic areas classification. Next, you’ll calculate dependent and independent variables mean, standard deviation, skewness and kurtosis descriptive statistics. Then, you’ll compute independent variables standardization.

Then, you’ll analyze multiple regression statistics analysis through coefficient of determination or R square, adjusted R square and regression standard error metrics. After that, you’ll analyze multiple regression analysis of variance or ANOVA through regression, residuals and total degrees of freedom, sum of squares, mean square error, regression F statistic and regression F statistical significance. Next, you’ll analyze multiple regression coefficient analysis through regression coefficients value, standard error, t statistic and t statistical significance or p-value.

After that, you’ll evaluate multiple regression correct specification through coefficients individual statistical significance and correct it through backward elimination stepwise regression. Then, you’ll evaluate multiple regression independent variables no linear dependence through multicollinearity test and correct it through correct specification re-evaluation. Next, you’ll evaluate multiple regression correct functional form through Ramsey-RESET linearity test and correct it through non-linear quadratic, logarithmic and reciprocal transformations of variables. Later, you’ll evaluate multiple regression residuals normality through Jaque-Bera test. After that, you’ll evaluate multiple regression residuals no autocorrelation through Breusch-Godfrey test and correct it by including lagged dependent variable data as independent variables in original regression. Then, you’ll evaluate multiple regression residuals homoscedasticity through White, Breusch-Pagan tests and correct it through Box-Cox transformation or normalization of dependent variable.

Next, you’ll evaluate multiple regression forecasting accuracy by dividing data into training and testing ranges. After that, you’ll use training range for fitting best model by going through steps described in previous sections. Then, you’ll use best fitting model coefficient values to forecast through testing range. Finally, you’ll evaluate testing range forecasted values accuracy against random walk benchmark through mean absolute error, root mean square error, mean absolute percentage error and mean absolute scaled error metrics.

Who is the target audience?
• Students 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 or financial analysts and data scientists who desire to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.
Compare to Other Regression Analysis Courses
Curriculum For This Course
36 Lectures
03:24:33
+
Course Overview
7 Lectures 29:09

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 04:59

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (course overview, variables definition, multiple regression, multiple regression assumptions and multiple regression forecasting accuracy).

Preview 03:16

In this lecture you will learn multiple regression analysis definition, R statistical software and RStudio Integrated Development Environment (IDE) downloading websites.

Multiple Regression Analysis
04:23

In this lecture you will learn volatility trading analysis data reading into RStudio Integrated Development Environment (IDE), data sources, R script code files originally in .TXT format that need to be converted in .R format with volatility trading analysis computation instructions, R packages installation (xts, e1071,MASS, tseries, lmtest, car, forecast) and related code (read.csv(), xts() functions).

Multiple Regression Analysis Data
16:21

Course Data File
00:03

Course Script File
00:03

Course Overview Slides
00:02
+
Variables Definition
8 Lectures 35:30

Variables Definition Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to variables definition (independent or explained variable, independent or explanatory variables, variables descriptive statistics and independent variables standardization).

Preview 05:15

In this lecture you will learn dependent variable definition and main calculations (plot(), summary() functions).

Dependent Variable
03:23

In this lecture you will learn rates independent variables definitions and main calculations (plot(), summary() functions).

Rates Independent Variables
06:37

In this lecture you will learn prices independent variables definitions and main calculations (plot(), summary() functions).

Prices Independent Variables
05:29

In this lecture you will macroeconomic independent variables definitions and main calculations (plot(), summary() functions).

Macroeconomic Independent Variables
04:06

In this lecture you will learn variables mean and standard deviation definition and main calculations (sapply(), mean(), sd(), skewness(), kurtosis() functions).

Variables Descriptive Statistics
07:58

In this lecture you will learn variables standardization definition and main calculations (mean(), sd() functions).

Variables Standardization
02:40
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Multiple Regression
5 Lectures 29:08

Multiple Regression Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related multiple regression (regression statistics, analysis of variance and regression coefficients analysis).

Multiple Regression Overview
05:21

In this lecture you will learn regression statistics definition and main calculations (lm(), summary(), plot(), lines(), legend() functions).

Regression Statistics
05:14

In this lecture you will learn analysis of variance definition and main calculations (cbind(), anova() functions).

Analysis of Variance
10:19

In this lecture you will learn regression coefficients analysis definition and main calculations (lm(), summary() functions).

Regression Coefficients Analysis
08:12
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Multiple Regression Assumptions
8 Lectures 57:02

Multiple Regression Assumptions Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to multiple regression assumptions (correct specification, no linear dependence, correct functional form, residuals normality, residuals no autocorrelation, residuals homoscedasticity and Box-Cox transformation).

Multiple Regression Assumptions Overview
07:48

In this lecture you will learn correct specification definition and main calculations (lm(), summary() functions).

Correct Specification
08:32

In this lecture you will learn no linear dependence definition and main calculations (cbind(), cor(), ginv() functions).

No Linear Dependence
04:52

In this lecture you will learn correct functional form definition and main calculations (lm(), summary(), \$fitted.values functions).

Correct Functional Form
10:56

In this lecture you will learn residuals normality definition and main calculations (jarque.bera.test(), \$residuals functions).

Residuals Normality
03:44

In this lecture you will learn residuals no autocorrelation definition and main calculations (lag(), is.na(), lm(), summary(), bgtest() functions).

Residuals No Autocorrelation
06:52

In this lecture you will learn residuals homoscedasticity definition and main calculations (lm(), summary(), bptest(), qqnorm(), qqline(), min(), powerTransform(), bcPower(), \$residuals functions).

Residuals Homoscedasticity
14:16
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Multiple Regression Forecasting Accuracy
8 Lectures 53:41

Multiple Regression Forecasting Accuracy Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to multiple regression forecasting accuracy (forecasting accuracy correct specification, forecasting accuracy correct functional form, forecasting accuracy residuals normality, forecasting accuracy residuals no autocorrelation, forecasting accuracy residuals homoscedasticity and forecasting accuracy metrics).

Multiple Regression Forecasting Accuracy Overview
12:24

In this lecture you will learn forecasting accuracy correct specification definition and main calculations (window(), mean(), sd(), lm(), summary() functions).

Forecasting Accuracy Correct Specification
10:42

In this lecture you will learn forecasting accuracy correct functional form definition and main calculations (lm(), summary(), \$fitted.values functions).

Forecasting Accuracy Correct Functional Form
02:52

In this lecture you will learn forecasting accuracy residuals normality definition and main calculations (jarque.bera.test(), \$residuals functions).

Forecasting Accuracy Residuals Normality
02:39

In this lecture you will learn forecasting accuracy residuals no autocorrelation definition and calculation (lag(), is.na(), lm(), summary(), bgtest() functions).

Forecasting Accuracy Residuals No Autocorrelation
04:36

In this lecture you will learn forecasting accuracy residuals homoscedasticity definition and main calculations (lm(), summary(), bptest(), qqnorm(), qqline(), min(), powerTransform(), bcPower(), \$residuals functions).

Forecasting Accuracy Residuals Homoscedasticity
09:59

In this lecture you will learn forecasting accuracy metrics definition and main calculations (lag(), is.na(), mean(), sd(), \$coefficients, as.numeric(), accuracy(), plot(), lines(), legend() functions).

Forecasting Accuracy Metrics
10:27