Multiple Regression Analysis with Excel
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Multiple Regression Analysis with Excel

Learn multiple regression analysis main concepts from basic to expert level through a practical course with Excel.
4.0 (8 ratings)
48 students enrolled
Created by Diego Fernandez
Last updated 4/2017
English
Current price: \$10 Original price: \$50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
• 4 hours on-demand video
• 6 Articles
• 6 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 test 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, symmetric mean absolute percentage error and mean absolute scaled error metrics.
View Curriculum
Requirements
• Spreadsheet software such as Microsoft Excel is required.
• Practical example spreadsheet provided by instructor.
• Prior basic spreadsheet software knowledge is recommended.
Description

Learn multiple regression analysis through a practical course with Excel 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 Excel

• 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 test 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, symmetric 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 37 lectures and 4 hours of content. It’s designed for all multiple regression analysis knowledge levels and a basic understanding of Excel 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 test 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, symmetric 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 Microsoft Excel®.
• 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 Excel Courses
Curriculum For This Course
37 Lectures
03:52:22
+
Course Overview
6 Lectures 24:33

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 04:41

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 03:22

In this lecture you will learn multiple regression analysis definition, bibliography and calculations with Microsoft Excel.

Multiple Regression Analysis
09:57

In this lecture you will learn multiple regression analysis data logarithmic monthly returns, monthly effective rate, logarithmic monthly inflation or deflation, monthly logarithmic change calculations, pre-calculated data sources, training and testing data ranges.

Multiple Regression Analysis Data
06:27

Course File
00:03

Course Overview Slides
00:02
+
Variables Definition
9 Lectures 30:27

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 03:05

In this lecture you will learn dependent variable definition and main calculations.

Dependent Variable
01:59

In this lecture you will learn rates independent variables definitions and main calculations.

Rates Independent Variables
04:20

In this lecture you will learn prices independent variables definitions and main calculations.

Prices Independent Variables
03:20

In this lecture you will macroeconomic independent variables definitions and main calculations.

Macroeconomic Independent Variables
03:05

In this lecture you will learn variables mean and standard deviation definition and main calculations (AVERAGE() and STDEV() functions).

Variables Mean and Standard Deviation
03:49

In this lecture you will learn skewness and kurtosis definition and main calculations (SKEW() and KURT() functions).

Variables Skewness and Kurtosis
05:23

In this lecture you will learn variables standardization definition and main calculations (STANDARDIZE() function).

Variables Standardization
05:24
+
Multiple Regression
5 Lectures 36:33

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
10:24

In this lecture you will learn regression statistics definition and main calculations (REGRESSION Data Analysis ToolPak Add-In, SQRT(), COUNT() and ARRAY{} functions).

Regression Statistics
05:39

In this lecture you will learn analysis of variance definition and main calculations (REGRESSION Data Analysis ToolPak Add-In, SUM(),  COUNT(), FDIST() and ARRAY{} functions).

Analysis of Variance
13:40

In this lecture you will learn regression coefficients analysis definition and main calculations (REGRESSION Data Analysis ToolPak Add-In, ABS(),  T.DIST.2T() and ARRAY{} functions).

Regression Coefficients Analysis
06:48
+
Multiple Regression Assumptions
9 Lectures 01:20:54

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
07:03

In this lecture you will learn correct specification definition and main calculations (REGRESSION Data Analysis ToolPak Add-In).

Correct Specification
13:22

In this lecture you will learn no linear dependence definition and main calculations (CORREL(), MINVERSE() and ARRAY{} functions).

No Linear Dependence
05:35

In this lecture you will learn correct functional form definition and main calculations (REGRESSION Data Analysis ToolPak Add-In).

Correct Functional Form
15:05

In this lecture you will learn residuals normality definition and main calculations (SKEW() and KURT() functions).

Residuals Normality
06:14

In this lecture you will learn residuals no autocorrelation definition and main calculations (REGRESSION Data Analysis ToolPak Add-In).

Residuals No Autocorrelation
07:07

In this lecture you will learn residuals homoscedasticity definition and main calculations (REGRESSION Data Analysis ToolPak Add-In).

Residuals Homoscedasticity
07:49

In this lecture you will learn Box-Cox transformation definition and main calculation (NORM.S.INV(), STANDARDIZE(), AVERAGE(), STDEV(), CORREL(), CUSTOM SORT functions and SOLVER Add-In).

Box-Cox Transformation
18:37
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Multiple Regression Forecasting Accuracy
8 Lectures 59:52

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
12:06

In this lecture you will learn forecasting accuracy correct specification definition and main calculations (COUNT(), FDIST(), T.DIST.2T(), ABS(), INDEX(), TRANSPOSE(), LINEST() and ARRAY{} functions).

Forecasting Accuracy Correct Specification
12:21

In this lecture you will learn forecasting accuracy correct functional form definition and main calculations (COUNT(), FDIST(), T.DIST.2T(), ABS(), INDEX(), TRANSPOSE(), LINEST() and ARRAY{} functions).

Forecasting Accuracy Correct Functional Form
05:36

In this lecture you will learn forecasting accuracy residuals normality definition and main calculations (SKEW() and KURT() functions).

Forecasting Accuracy Residuals Normality
04:16

In this lecture you will learn forecasting accuracy residuals no autocorrelation definition and calculation (COUNT(), FDIST(), T.DIST.2T(), ABS(), INDEX(), TRANSPOSE(), LINEST() and ARRAY{} functions).

Forecasting Accuracy Residuals No Autocorrelation
04:44

In this lecture you will learn forecasting accuracy residuals homoscedasticity definition and main calculations (COUNT(), FDIST(), T.DIST.2T(), ABS(), INDEX(), TRANSPOSE(), LINEST() and ARRAY{} functions).

Forecasting Accuracy Residuals Homoscedasticity
07:38

In this lecture you will learn forecasting accuracy metrics definition and main calculations (AVERAGE(), ABS(), SQUARE() and ARRAY{} functions).

Forecasting Accuracy Metrics
13:09
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Exfinsis

Diego Fernandez is author of high-quality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.

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

He has important online business development experience at fast-growing startups and blue-chip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.

He also achieved outstanding performance in his undergraduate and postgraduate degrees at world-class academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups.

His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.