Due to Covid-19 safety restrictions PhoenixTS will temporarily be unable to provide food to our students who attend class at our Training Center; however, our Break Areas are currently open where students will find a constant supply of Coffee, Tea and Water. Students may bring their own lunch and snacks to eat in our breakrooms or at their seat in the classroom or eat out at one of the many nearby restaurants.
Course Overview
This 2-day data science focused training course explores regression and time-series analysis. At the conclusion of this course, students should be able to do the following:
- Build single and multivariate regression models
- Assess statistical significance and validate models for explanatory power and bias
- Use time-series models to identify seasonality patterns and create forecasts for cyclical data
Schedule
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757.
![[image]](https://stage.phoenixts.com/wp-content/themes/phoenixts-com/assets/images/temp/image-9@2x.jpg)
Not seeing a good fit?
Let us know. Our team of instructional designers, curriculum developers, and subject matter experts can create a custom course for you.
Learn more about custom training
Course Outline
Introduction to Regression and Time-Series Analysis
- Commercial applications of regression and time-series analysis
- Linear relationships: slope, y-intercept, variable interactions
- Variance and standard deviation
- Covariance and correlation
- Normal distribution and bell curves
Evaluating Your Model
- Distribution of errors: Q-Q plot, heteroscedasticity
- Multivariate regression
- R- and adjusted R-
- p-values and the t-test
- F-test and F-distribution
Identifying the Most Important Variables
- Multicollinearity test
- Heteroscedasticity test
- Model selection: Akaike Information Criterion
- Polynomial regression
- Confidence intervals
Time-Series Analysis Seasonality
- Moving averages
- Seasonality detection: auto-correlation
- Seasonality: additive vs. multiplicative
- Decomposing seasonal data: trend, level and seasonality
- Multiplicative Holt-Winters exponential smoothing
- Forecasting seasonal trends
- LOcal regrESSion: LOESS
Regression and Time-Series Analysis Training FAQs
This course is intended for professionals who have a good working knowledge of R, work with time-series data and want to create forecasts for future trends, need to model cyclical or seasonal data such as sales, customer volumes, web traffic, employee behaviors, etc.;
need a good background in basic statistics and statistical modeling
or want to stand out as data scientists with advanced predictive modeling and time-series analysis skills .
Students should have taken the Introduction to Data Science, R, and Visualization course or should have the equivalent knowledge of data manipulation, cleaning and visualization.
Due to Covid-19 safety restrictions PhoenixTS will temporarily be unable to provide food to our students who attend class at our Training Center; however, our Break Areas are currently open where students will find a constant supply of Coffee, Tea and Water. Students may bring their own lunch and snacks to eat in our breakrooms or at their seat in the classroom or eat out at one of the many nearby restaurants.
