Correlation, Linear, Multiple, and Logistic Regression

 

The course is sub-divided into the following Lessons:

  1. Correlation
  2. Linear Regression
  3. Multiple Regression
  4. Logistic Regression

Correlation

In this lesson, you will be introduced to the practical use of correlation; expose you to research questions requiring correlational investigation between variables; the measure of correlation between two variables and also explore partial or confounding dependencies between variables.

In this lesson, you will cover the following topics:

  • Graph the relationship using scatter plots - simply, overlay and matrix plots.
  • Measure relationship between two variables using correlation coefficient  (r and r-square).
  • Understand parametric and non-parametric correlation – Pearson, Spearman, Kendall’s tau.
  • Examine partial correlation and semi-partial correlation.

Linear Regression

In this lesson, you will explore the predictive linear relationship between  a dependent variable and a predictor or explanatory variable. You will learn to write the linear regression equation from a statistical output generated for real research data.

In this lesson, you will cover the following topics:

  • Examine which relational variables can be considered for the regression model.
  • Regression methods.
  • Check the goodness of fit of the regression model using residuals.
  • Write the regression equation. 

Multiple Linear Regression

In this lesson, you will explore the predictive linear relationship between  a dependent variable and a number of  predictors or explanatory variables. You will learn to write the linear regression equation from a statistical output generated from real research data.

In this section, you will cover the following topics:

  • Consider what relational variables can be considered for the  regression model.
  • Regression methods to use – Enter, Stepwise etc.
  • Multiple linear regression and model selection.
  • Check for multicollinearity (confounding).
  • Check the goodness of fit of the regression model using residuals.
  • Write the regression equation.

Logistic Regression

This course will provide you with a thorough and practical understanding of Logistic Regression.  You will be guided on how to generate it using SPSS and also interpret the results. You will gain knowledge and competence that will enable you to prepare your data for logistic regression analysis.

In this Lesson, you will cover:

  • The concept of logistic regression.
  • Perform Logistic Regression.
  • Assess the goodness of fit of the logistic regression model.
  • Interpret regression model.

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