 # Predictive Analysis 1

### Accordion / FAQ component

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.

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

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Yes, if you complete this module it can be credited as part of the

### Detailed Course Information 