Excel Analytics: Linear Regression Analysis in MS Excel
4.3 (426 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
54,422 students enrolled

Excel Analytics: Linear Regression Analysis in MS Excel

Linear Regression analysis in Excel. Analytics in Excel includes regression analysis, Goal seek and What-if analysis
Bestseller
4.3 (426 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
54,422 students enrolled
Last updated 6/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 2.5 hours on-demand video
  • 3 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn how to solve real life problem using the Linear Regression technique
  • Preliminary analysis of data using Univariate analysis before running Linear regression
  • Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
  • Understand how to interpret the result of Linear Regression model and translate them into actionable insight
  • Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
  • Course contains a end-to-end DIY project to implement your learnings from the lectures
Requirements
  • You will need a PC with any version of Excel installed in it
  • Basic understanding of Excel operations like opening, closing and saving a file
Description

You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Excel, right?

You've found the right Linear Regression course!

After completing this course you will be able to:

· Identify the business problem which can be solved using linear regression technique of Machine Learning.

· Create a linear regression model in Excel and analyze its result.

· Confidently practice, discuss and understand Machine Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Linear Regression:

· Section 1 - Basics of Statistics

This section is divided into five different lectures starting from types of data then types of statistics

then graphical representations to describe the data and then a lecture on measures of center like mean

median and mode and lastly measures of dispersion like range and standard deviation

· Section 2 - Data Preprocessing

In this section you will learn what actions you need to take a step by step to get the data and then

prepare it for the analysis these steps are very important.

We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

· Section 3 - Regression Model

This section starts with simple linear regression and then covers multiple linear regression.

We have covered the basic theory behind each concept without getting too mathematical about it so that you

understand where the concept is coming from and how it is important. But even if you don't understand

it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

By the end of this course, your confidence in creating a regression model in R will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.


Go ahead and click the enroll button, and I'll see you in lesson 1!


Cheers

Start-Tech Academy


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Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What is the Linear regression technique of Machine learning?

Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.

Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).

When there is a single input variable (x), the method is referred to as simple linear regression.

When there are multiple input variables, the method is known as multiple linear regression.

Why learn Linear regression technique of Machine learning?

There are four reasons to learn Linear regression technique of Machine learning:

1. Linear Regression is the most popular machine learning technique

2. Linear Regression has fairly good prediction accuracy

3. Linear Regression is simple to implement and easy to interpret

4. It gives you a firm base to start learning other advanced techniques of Machine Learning

How much time does it take to learn Linear regression technique of machine learning?

Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 4 parts:

Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the R environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in R

Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture in R where we actually run each query with you.


Who this course is for:
  • Working Professionals beginning their Data journey
  • Anyone curious to master Linear Regression in short span of time
Course content
Expand all 28 lectures 02:39:53
+ Getting Data Ready for Regression Model
15 lectures 01:18:15
Course resources
00:04
Univariate analysis and EDD
03:34
Discriptive Data Analytics in Excel
10:33
Outlier Treatment
04:15
Identifying and Treating Outliers in Excel
04:14
Missing Value Imputation
03:36
Identifying and Treating missing values in Excel
04:00
Variable Transformation in Excel
03:41
Dummy variable creation: Handling qualitative data
04:50
Dummy Variable Creation in Excel
07:44
Correlation Analysis
09:47
Creating Correlation Matrix in Excel
08:07
+ Creating Regression Model
10 lectures 01:08:18
The Problem Statement
01:25
Basic Equations and Ordinary Least Squares (OLS) method
08:13
Assessing accuracy of predicted coefficients
14:40
Assessing Model Accuracy: RSE and R squared
07:19
Creating Simple Linear Regression model
02:41
Multiple Linear Regression
04:57
The F - statistic
08:22
Interpreting results of Categorical variables
05:04
Creating Multiple Linear Regression model
07:35
Quiz
1 question
Excel: Running Linear Regression using Solver
08:02
Quiz
1 question
+ What-if analysis
1 lecture 12:17

How to Solve Transportation Problem in Excel using Goal Seek. Learn step by step technique to find the optimum solution to a balanced transportation problem. This example illustrates the use of Goal Seek option in the solver to find the Minimum or maximum target value for a given set of constraints.

Transportation Problem in Excel using Goal Seek
12:17
+ Bonus Section
2 lectures 01:02
Congratulations & About your certificate
00:35
Bonus Lecture
00:27