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Univariate statistics

November Member Training: Preparing to Use (and Interpret) a Linear Regression Model

by TAF Support

You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?

[Read more…] about November Member Training: Preparing to Use (and Interpret) a Linear Regression Model

Tagged With: Bivariate Statistics, histogram, interpreting regression coefficients, linear regression, Multiple Regression, scatterplot, Univariate statistics

Related Posts

  • Member Training: Using Transformations to Improve Your Linear Regression Model
  • Member Training: Segmented Regression
  • The General Linear Model, Analysis of Covariance, and How ANOVA and Linear Regression Really are the Same Model Wearing Different Clothes
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction

How to Interpret the Width of a Confidence Interval

by Christos Giannoulis 2 Comments

One issue with using tests of significance is that black and white cut-off points such as 5 percent or 1 percent may be difficult to justify.

Significance tests on their own do not provide much light about the nature or magnitude of any effect to which they apply.

One way of shedding more light on those issues is to use confidence intervals. Confidence intervals can be used in univariate, bivariate and multivariate analyses and meta-analytic studies.

[Read more…] about How to Interpret the Width of a Confidence Interval

Tagged With: Bivariate Statistics, confidence interval, multivariate analysis, sample size, standard error, Univariate statistics

Related Posts

  • How Does the Distribution of a Population Impact the Confidence Interval?
  • How Confident Are You About Confidence Intervals?
  • The Difference Between Association and Correlation
  • Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?

Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong

by Karen Grace-Martin Leave a Comment

You’ve probably experienced this before. You’ve done a statistical analysis, you’ve figured out all the steps, you finally get results and are able to interpret them. But they just look…wrong. Backwards, or even impossible—theoretically or logically.

This happened a few times recently to a couple of my consulting clients, and once to me. So I know that feeling of panic well. There are so many possible causes of incorrect results, but there are a few steps you can take that will help you figure out which one you’ve got and how (and whether) to correct it.

Errors in Data Coding and Entry

In both of my clients’ cases, the problem was that they had coded missing data with an impossible and extreme value, like 99. But they failed to define that code as missing in SPSS. So SPSS took 99 as a real data point, which [Read more…] about Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong

Tagged With: Bivariate Statistics, interaction, interpreting regression coefficients, logistic regression, Missing Data, Multicollinearity, Univariate statistics

Related Posts

  • Eight Ways to Detect Multicollinearity
  • A Visual Description of Multicollinearity
  • Member Training: Model Building Approaches
  • How Simple Should a Model Be? The Case of Insignificant Controls, Interactions, and Covariance Structures

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This Month’s Statistically Speaking Live Training

  • February Member Training: Choosing the Best Statistical Analysis

Upcoming Workshops

  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
  • Introduction to Generalized Linear Mixed Models (May 2021)

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Data Analysis with SPSS
(4th Edition)

by Stephen Sweet and
Karen Grace-Martin

Statistical Resources by Topic

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