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Machine Learning & AI Foundations: Linear Regression

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Keith McCormick

3:57:20

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  • 01 - Welcome.mp4
    01:18
  • 02 - What you should know.mp4
    01:55
  • 03 - Using the exercise files.mp4
    01:08
  • 01 - Building effective scatter plots in Chart Builder.mp4
    07:11
  • 02 - Adding labels and spikes to a scatter plot.mp4
    03:24
  • 03 - Create a 3D scatter plot.mp4
    02:39
  • 04 - Bubble chart with GPL.mp4
    06:14
  • 05 - Residuals and R2.mp4
    04:27
  • 06 - Calculating and interpreting regression coefficients.mp4
    07:23
  • 01 - Challenges and assumptions of multiple regression.mp4
    08:05
  • 02 - Checking assumptions visually.mp4
    09:00
  • 03 - Checking assumptions with Explore.mp4
    09:55
  • 04 - Checking assumptions Durbin-Watson.mp4
    01:55
  • 05 - Checking assumptions Levines test.mp4
    04:15
  • 06 - Checking assumptions Correlation matrix.mp4
    04:31
  • 07 - Checking assumptions Residuals plot.mp4
    06:23
  • 08 - Checking assumptions Summary.mp4
    03:59
  • 01 - Creating dummy codes.mp4
    08:04
  • 02 - Dummy coding with the R extension.mp4
    01:50
  • 03 - Detecting variable interactions.mp4
    05:01
  • 04 - Creating and testing interaction terms.mp4
    04:33
  • 01 - Three regression strategies and when to use them.mp4
    02:45
  • 02 - Understanding partial correlations.mp4
    03:54
  • 03 - Understanding part correlations.mp4
    03:40
  • 04 - Visualizing part and partial correlations.mp4
    05:11
  • 05 - Simultaneous regression Setting up the analysis.mp4
    02:43
  • 06 - Simultaneous regression Interpreting the output.mp4
    07:55
  • 07 - Hierarchical regression Setting up the analysis.mp4
    05:05
  • 08 - Hierarchical regression Interpreting the output.mp4
    07:20
  • 09 - Creating a train-test partition in SPSS.mp4
    04:30
  • 10 - Stepwise regression Setting up the analysis.mp4
    03:24
  • 11 - Stepwise regression Interpreting the output.mp4
    04:05
  • 01 - Collinearity diagnostics.mp4
    06:30
  • 02 - Dealing with multicollinearity Factor analysisPCA.mp4
    04:17
  • 03 - Dealing with multicollinearity Manually combine IVs.mp4
    03:15
  • 04 - Diagnosing outliers and influential points.mp4
    07:21
  • 05 - Dealing with outliers Studentized deleted residuals.mp4
    05:49
  • 06 - Dealing with outliers Should cases be removed.mp4
    06:48
  • 07 - Detecting curvilinearity.mp4
    05:20
  • 01 - Regression options.mp4
    05:20
  • 02 - Automatic linear modeling.mp4
    06:37
  • 03 - Regression trees.mp4
    06:19
  • 04 - Time series forecasting.mp4
    04:30
  • 05 - Categorical regression with optimal scaling.mp4
    06:09
  • 06 - Comparing regression to Neural Nets.mp4
    04:31
  • 07 - Logistic regression.mp4
    04:54
  • 08 - SEM.mp4
    04:23
  • 01 - Whats next.mp4
    01:35
  • Description


    Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

    Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.

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    Keith McCormick
    Keith McCormick
    Instructor's Courses
    I'm an independent consultant, trainer, speaker, and author of seven books. My consulting specializes in helping analytics leaders build and manage their data science teams. My training, including 20 LinkedIn Learning courses and frequent conference workshops, has reached 1000s of individuals trying to learn statistics, machine learning, and data science. I love that I am able to train and consult. Training allows me to interact with (and learn from) 100s of clients in dozens of industries every year. It prevents me from obtaining too narrow a focus, and it keeps me current. Consulting allows me to work with a smaller number of clients in detail and in-depth, working with them on real problems of immediate concern to them. It keeps me sharp. If you've encountered me through my LinkedIn Learning courses, please consider following me here on LinkedIn. I'm not able to connect with everyone, so I connect only with clients and colleagues that I know directly. But please do follow me here because I'm quite active on LinkedIn and frequently post excerpts from the courses and other content. Follow #freefirstfridays to see when I post a link to watch a course for free. My favorite kind of consulting work involves: - working with analytics management to create effective data science teams - listening carefully to my client explain their business in detail - turning their description into a research question that can be answered with their data - coaching my client on presenting possible solutions to decision-makers - working behind the scenes to get the solution deployed Specialties: For the last several years, my emphasis has been working with analytics management to more efficiently run their teams and to nurture new hires as they expand their teams. I am skilled at explaining complex methods to new users or decision-makers and can do so at any level of technical detail. I specialize in predictive models and segmentation analysis, including classification trees, neural nets, general linear model, cluster analysis, and association rules. Books and Courses The best way to find out more about me is to check out my courses on LinkedIn Learning. They have received over 500,000 views, and each one has some free content. My books can be found on Amazon, and typically that allows you to view some free content as well. I'm very proud of all of this content (listed below in my profile), but I am still primarily an active consultant. If you need consulting help, private training, or a keynote speaker, contact me, and we can discuss.
    LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.
    • language english
    • Training sessions 48
    • duration 3:57:20
    • English subtitles has
    • Release Date 2023/04/11