# Regression and Time Series Analysis

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#### Overview Identify the most important predictive variables in a model Quantify qualitative variables and incorporate them in a predictive model Build predictive models to anticipate trends and demand

 Prerequisites Intro to R Instruction 3 hours and 30 minutes Practice 20 to 25 hours

### Syllabus: Regression and Time Series Analysis

This course is designed for students who have taken Data Society’s Introduction to R and Visualization course or have a good knowledge of R programming. This 3 ½ hour course teaches students how to apply advanced regression and time series models to accurately predict business trends and demand.

###### By the end of this course, students will be able to:

1. Identify the most important predictive variables in a model

2. Quantify qualitative variables and incorporate them in a predictive model

3. Build predictive models to anticipate trends and demand

###### Assessment:
1. Concept reviews: these are comprised of short five question quizzes that cover the most important concepts and ideas in each lesson. They encourage holistic understanding and are multi-faceted ques=on types (i.e. drag and drop, fill-in-the-blanks, matching, etc).
2. Exercises: these are additional videos that cover the coding functions in the instructional video in more depth. They are project-based and include coding templates for students to strengthen their skills outside of the course.
###### Materials provided:
1. Accompanying PDFs to use as reference materials
2. R code templates from the instructional videos and exercises
3. Data sets used in the instructional videos and exercises

### Course Outline

1. Setting up linear regression (34 min) – Free trial!

Overview of data science
What is regression?
Building linear regression

2. Measuring model errors (38 min)

Calculating variance and standard deviation
Identifying outliers
Calculating covariance and correlation
Testing a model’s significance

3. Modeling multiple variables (31 min)

Building a multivariate model
Plotting a multivariate regression
Identifying multicollinearity
Datafying categorical variables

Testing for heteroscedasticity
Identifying important variables
Building nonlinear regression models

Transforming variables
Identifying seasonality
Calculating seasonality
Minimizing seasonality errors

Calculating seasonality components
Predicting customer demand
Using the LOESS method