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Curricular information is subject to change
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.
|Student Effort Type||Hours|
|Autonomous Student Learning||
|Computer Aided Lab||
Students are expected to have taken two previous statistics/data analytics modules, such as STAT40720 and STAT30280
|Description||Timing||Component Scale||% of Final Grade|
|Examination: 2 hour end of semester written Examination||2 hour End of Trimester Exam||No||Alternative linear conversion grade scale 40%||Yes||
|Assignment: Exploratory Data Analysis
|Varies over the Trimester||n/a||Alternative linear conversion grade scale 40%||No||
|Resit In||Terminal Exam|
|Spring||Yes - 2 Hour|
• Feedback individually to students, post-assessment
• Online automated feedback
Feedback on assignments provided individually to students, post-assessment
|Ms Sajal Kaur Minhas||Tutor|