Predictive Analysis 1

This module will use a variety of real-world examples to illustrate the use of regression models and cover both theoretical and practical considerations. Key topics covered include: 1. Matrix revision and Exploratory data analysis; 2. Simple linear regression (SLR) 3. Multiple linear regression (MLR) 4. Categorical Predictors and Interactions 5. Analysis of Variance 6. Regression Diagnostics 7. Variable Selection and Model Building

Accordion / FAQ component

Most modules require a 2.2 degree in a related discipline or equivalent professional experience. Should you have any queries regarding your eligibility, please contact us at

N.B. Required to have completed mathematics and statistics modules that contain elements of linear algebra (specifically matrix manipulation) and calculus.

By the end of the module students should be able to:
(i) Interpret scatterplots for bivariate data.
(ii) Define the correlation coefficient for bivariate data.
(iii) Explain the interpretation of the correlation coefficient for bivariate data and perform statistical inference as appropriate.
(iv) Calculate the correlation coefficient for bivariate data.
(v) Explain what is meant by response and explanatory variables.
(vi) Derive the least squares estimates of the slope and intercept parameters in a simple linear regression model.
(vii) Perform statistical inference on the slope parameter.
(viii) Describe the use of measures of goodness of fit of a linear regression model.
(ix) Use a fitted linear relationship to predict a mean response or an individual response with confidence limits
(x) Use residuals to check the suitability and validity of a linear regression model.
(xi) State the multiple linear regression model (with several explanatory variables).
(xii) Use appropriate software to fit a multiple linear regression model to a data set and interpret the output.
(xiii) Use measures of model fit to select an appropriate set of explanatory variables.

Learn from world renowned academic staff in Ireland’s leading, future focused and globally recognised colleges.

Gain an accredited NFQ qualification/micro credential that you may count towards a full award if you so wish in the future.

Previous modules may be used as recognition of prior learning towards Advance Centre degree programmes.

Equip yourself with the latest in demand skillset, tools, know-how and knowledge to succeed in your career.

Gain a competitive edge, influence growth and steer strategic goals in your organisation upon completion of your studies with the Advance Centre.

Yes, if you complete this module it can be credited as part of the MSc Data Analytics or Professional Diploma Data Analytics PT

Detailed Course Information

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