Course Description This ’Linear andamp; Logistic Regression’ online training course will teach you how to build robust linear models and do logistic regressions in Excel, R, and Python that will stand up to scrutiny when you apply them to real world situations. You’ll learn about topics such as: understanding random variables, cause-effect relationships, maximum likelihood estimation, and so much more. Follow along with the experts as they break down these concepts in easy-to-understand lessons. Course Highlights Simple Regression : Method of least squares, Explaining variance, Forecasting an outcome Residuals, assumptions about residuals Implement simple regression in Excel, R and Python Interpret regression results and avoid common pitfalls Multiple Regression : Implement Multiple regression in Excel, R and Python Introduce a categorical variable Logistic Regression : Applications of Logistic Regression, the link to Linear Regression and Machine Learning Solving logistic regression using Maximum Likelihood Estimation and Linear Regression Extending Binomial Logistic Regression to Multinomial Logistic Regression Implement Logistic regression to build a model stock price movements in Excel, R and Python Course Requirements No statistics background required. Everything is built up from basic math The models are implemented in Excel, R and Python. Install these environments to follow along with the demos Target Audience Data analysts who want to move from summarizing data to explaining and prediction Folks aspiring to be data scientists Any business professionals who want to apply Linear regression to solve relevant problems Length: 5 hrs Example Video Course Outline Chapter 01: Introduction Lesson 01: You, This Course, andamp; Us! Chapter 02: Connect the Dots with Linear Regression Lesson 01: Using Linear Regression to Connect the Dots Lesson 02: Two Common Applications of Regression Lesson 03: Extending Linear Regression to Fit Non-linear Relationships Chapter 03: Basic Statistics Used for Regression Lesson 01: Understanding Mean andamp; Variance Lesson 02: Understanding Random Variables Lesson 03: The Normal Distribution Chapter 04: Simple Regression Lesson 01: Setting up a Regression Problem Lesson 02: Using Simple Regression to Explain Cause-Effect Relationships Lesson 03: Using Simple Regression for Explaining Variance Lesson 04: Using Simple Regression for Prediction Lesson 05: Interpreting the results of a Regression Lesson 06: Mitigating Risks in Simple Regression Chapter 05: Applying Simple Regression Lesson 01: Applying Simple Regression in Excel Lesson 02: Applying Simple Regression in R Lesson 03: Applying Simple Regression in Python Chapter 06: Multiple Regression Lesson 01: Introducing Multiple Regression Lesson 02: Some Risks inherent to Multiple Regression Lesson 03: Benefits of Multiple Regression Lesson 04: Introducing Categorical Variables Lesson 05: Interpreting Regression results – Adjusted R-squared Lesson 06: Interpreting Regression results – Standard Errors of Coefficients Lesson 07: Interpreting Regression results – t-statistics andamp; p-values Lesson 08: Interpreting Regression results – F-Statistic Chapter 07: Applying Multiple Regression using Excel Lesson 01: Implementing Multiple Regression in Excel Lesson 02: Implementing Multiple Regression in R Lesson 03: Implementing Multiple Regression in Python Chapter 08: Logistic Regression for Categorical Dependent Variables Lesson 01: Understanding the need for Logistic Regression Lesson 02: Setting up a Logistic Regression problem Lesson 03: Applications of Logistic Regression Lesson 04: The link between Linear andamp; Logistic Regression Lesson 05: The link between Logistic Regression andamp; Machine Learning Chapter 09: Solving Logistic Regression Lesson 01: Understanding the intuition behind Logistic Regression andamp; the S-curve Lesson 02: Solving Logistic Regression using Maximum Likelihood Estimation Lesson 03: Solving Logistic Regression using Linear Regression Lesson 04: Binomial vs Multinomial Logistic Regression Chapter 10: Applying Logistic Regression Lesson 01: Predict Stock Price movements using Logistic Regression in Excel Lesson 02: Predict Stock Price movements using Logistic Regression in R Lesson 03: Predict Stock Price movements using Rule-based andamp; Linear Regression Lesson 04: Predict Stock Price movements using Logistic Regression in Python PACKAGE INCLUDES: Length of Subscription: 12 Months Online On-Demand Access Running Time: 5 hrs Platform: Windows andamp; MAC OS Level: Beginner to Advanced Project Files: Included Learn anytime, anywhere, at home or on the go. Stream your training via the internet, or download to your computer and supported mobile device, including iPad, iPhone, iPod Touch and most Android devices. Need to train your Team? Contact Us for Discounts on Multiple Subscription Purchases.