Normal Distribution

The normal distribution is one of the most important distributions in statistics. In this part of the website, we will explore the following topics about this distribution, although additional information about this distribution will be provided throughout the website.

Topics

Reference

Howell, D. C. (2010) Statistical methods for psychology (7th ed.). Wadsworth, Cengage Learning.
https://labs.la.utexas.edu/gilden/files/2016/05/Statistics-Text.pdf

7 thoughts on “Normal Distribution”

  1. Dear Charles

    We are conducting an experiment within the immunohaemological field on discrete/ordinal data. We have several paired datasets and some paired multivariate datasets as well.
    We are relativly new to statistics and are attempting to establish whether out data is normally distributed.
    Could you advise us on the following: Which data do we enter into real statisctic for a test of normal distribution – do we enter the differences between the paired datasets or the “raw data” for each variable?
    Hope you can help.

    Reply

  3. Dear Dr. Charles,
    Thank you for your helpful website. it is another school.
    I have posted below message to your attention.

    could you advise me how I can linearize, normalize and homogeneize my data without alterating them?

    regards
    sage
    I am currently developing a model based on Neural Networks.

    Performance analyzes were successfully done but doing graphical residual analysis, I observed the trend to be a bit linear as shown below. while performing residual analysis, I noticed that Percentile Vs residual isnot linear, even Residual Vs predicted is lineary and not randomly distributed.

    I tried a lot of method of data transformation method but I did not succeed. When I transform other data set, it works. It is just my dataset which is kind difficult for me. Can someone show me how to transform my data to achieve linearity? Below are my data:
    Date Observed Predicted
    15-Feb-15 1176.491943 1176.492483
    19-Feb-15 1176.48291 1176.483679
    20-Feb-15 1176.46582 1176.467308
    25-Feb-15 1176.463379 1176.46493
    2-Mar-15 1176.452515 1176.454374
    7-Mar-15 1176.450439 1176.452346
    12-Mar-15 1176.44165 1176.443764
    17-Mar-15 1176.435913 1176.437807
    22-Mar-15 1176.432251 1176.43359
    27-Mar-15 1176.429688 1176.430538
    1-Apr-15 1176.428101 1176.428278
    6-Apr-15 1176.427002 1176.426561
    11-Apr-15 1176.426147 1176.425223
    16-Apr-15 1176.425659 1176.424153
    21-Apr-15 1176.425293 1176.423277
    26-Apr-15 1176.425049 1176.422545
    1-May-15 1176.424805 1176.421921
    6-May-15 1176.424805 1176.421381
    11-May-15 1176.424805 1176.420909
    16-May-15 1176.424805 1176.420491
    21-May-15 1176.424805 1176.420119
    26-May-15 1176.424805 1176.419786
    31-May-15 1176.424805 1176.419486
    1-Jun-15 1176.424805 1176.419216

    Reply

    • Sage,
      When I created a chart of the observed data vs the predicted data it was (probably not surprisingly) almost a perfect straight line (except for the first few data elements which were slightly off). What more linearity do you need?
      Charles

      Reply

Leave a Comment