What is Linear Regression?

Linear regression is a basic and commonly used type of predictive analysis

FYI, Predictive analytics involves extracting data from existing data sets with the goal of identifying trends and patterns. These trends and patterns are then used to predict future outcomes and trends. Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. It uses a number of data mining, predictive modeling and analytical techniques to bring together the management, information technology, and modeling business process to make predictions about future. The patterns found in historical and transactional data can be used to identify risks and opportunities for future. Predictive analytics models capture relationships among many factors to assess risk with a particular set of conditions to assign a score, or weightage.

The overall idea of regression is to examine two things:

(1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable?

(2) Which variables in particular are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable?


These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables.  The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable.


There are several types of linear regression analyses available to researchers.

  • Simple linear regression
    1 dependent variable (interval or ratio), 1 independent variable (interval or ratio or dichotomous)


  • Multiple linear regression
    1 dependent variable (interval or ratio) , 2+ independent variables (interval or ratio or dichotomous)


  • Logistic regression
    1 dependent variable (dichotomous), 2+ independent variable(s) (interval or ratio or dichotomous)


  • Ordinal regression
    1 dependent variable (ordinal), 1+ independent variable(s) (nominal or dichotomous)


  • Multinominal regression
    1 dependent variable (nominal), 1+ independent variable(s) (interval or ratio or dichotomous)


  • Discriminant analysis
    1 dependent variable (nominal), 1+ independent variable(s) (interval or ratio)

When selecting the model for the analysis, an important consideration is model fitting.  Adding independent variables to a linear regression model will always increase the explained variance of the model (typically expressed as R²).  However, overfitting can occur by adding too many variables to the model, which reduces model generalizability.  Occam’s razor describes the problem extremely well – a simple model is usually preferable to a more complex model.  Statistically, if a model includes a large number of variables, some of the variables will be statistically significant due to chance alone.


Article Source: statisticssolutions

You may also like...

20 Responses

  1. Wow, fantastic weblog structure! How lengthy have you ever been running a blog for? you make running a blog look easy. The total glance of your web site is fantastic, as neatly as the content!

  2. Usually I do not read post on blogs, but I wish to say that this write-up very forced me to try and do so! Your writing style has been surprised me. Thanks, quite nice article.

  3. Madeleine says:

    Thank you for the wonderful post

  4. Thanks for the wonderful manual

  5. Trina says:

    This is really useful, thanks.

  6. Thanks to my father who told me concerning this weblog, this weblog is in fact

  7. Wow! Finally I got a blog from where I can actually obtain helpful data regarding my study and knowledge.

  8. here says:

    I enjoy the article

  9. Numbers says:

    Thanks for the great post

  10. If you wish for to grow your familiarity just keep visiting this website and be updated
    with the newest news posted here.

  11. minecraft says:

    I have been surfing on-line greater than three hours lately, but I never found any fascinating article like yours.
    It is beautiful worth enough for me. In my view, if all site owners and bloggers made just right content material
    as you probably did, the web can be a lot more useful than ever before.

  12. tinyurl.com says:

    Very soon this site will be famous amid all blog visitors, due to it’s fastidious articles or reviews

  13. minecraft says:

    I like the helpful information you provide in your articles.
    I’ll bookmark your weblog and check once more right here regularly.
    I am moderately sure I will be informed many new stuff proper here!

    Good luck for the following!

  14. minecraft says:

    Asking questions are really pleasant thing if you are not understanding
    something completely, however this post gives nice understanding yet.

  15. minecraft says:

    Hi there! I know this is kind of off-topic but I needed to ask.

    Does managing a well-established website such as yours take a massive amount work?
    I’m completely new to blogging but I do write in my
    journal every day. I’d like to start a blog so I will be able to share my personal experience
    and views online. Please let me know if you have any ideas or tips for brand
    new aspiring blog owners. Thankyou!

  16. minecraft says:

    Superb site you have here but I was curious if you knew
    of any forums that cover the same topics discussed here?
    I’d really love to be a part of group where I can get suggestions from other experienced individuals that share the same interest.
    If you have any suggestions, please let me know. Many thanks!

  17. Thank you for the great article

  18. Hildegarde says:

    Thanks, it is quite informative

  19. tinyurl.com says:

    Hi my friend! I want to say that this article is amazing, great written and include approximately all vital infos.

    I’d like to see more posts like this .

  20. minecraft says:

    I am genuinely glad to glance at this blog
    posts which includes tons of useful facts, thanks for providing these statistics.

Leave a Reply

Your email address will not be published. Required fields are marked *