PORTAL USER GUIDE

Linear Model Residual Plot

The Linear Model Residual Plot tool is a scatter plot of the response (dependent) variable against the linear model (“line of best fit”) calculated by a regression, plotting the residuals (that is, the part of the dependent variable that can’t be explained by the model – the “residual” variation) against fitted values (i.e. the linear model). The Linear Model Residual Plot is a plot similar to the Linear Model Response Plot tool except instead of showing how the response variable (your dependent variable) against the independent variable, with a positive or negative slope, it has the linear model as a horizontal line (b = 0) to show how much variation there is around the line.

SET UP

 

To illustrate the Linear Model Residual Plot tool, we will run it on a dataset within Tasmania to visualise the connection between fruit intake and obesity in children. Prepare the context by:

  • Select South Australia as your area.
  • Select PHIDU – Prevalence of Selected Health Risk Factors – Children and Youth (LGA) 2014-2015 as your dataset with the following attributes:
    • LGA Code 2016
    • LGA Name 2016
    • Estimated Number Of People Aged 4-17 Years With Adequate Fruit Intake (Modelled Estimates) 2014-15 ASR per 100
    • Estimated Number Of Children Aged 2-17 Years Who Were Obese (Modelled Estimates)* 2014-15 ASR per 100

Once you have added the dataset and the selected attributes, you are ready to use the Linear Model Residual Plot tool – follow on to learn about the input options.

Inputs

Once you have set up your data, open the Linear Model Residual Plot tool (Tools → Charts → Linear Model Residual Plot). The input fields are as follows:

  • Dataset Input: The dataset containing the variables that you would like to run through the tool. Select PHIDU – Prevalence of Selected Health Risk Factors – Children and Youth (LGA) 2014-2015.
  • Formula: This will need to be typed in the manner that is normally incorporated in the R language. This will require typing in the names rather than the titles of the variables. These can be found in the metadata window of the dataset.
    When we ‘regress’ a dependent variable on an independent variable in R, this is written as dependent variable ~ independent variable with the tilde symbol (~) representing the statistical function of “regressed on”. For this example Type est_chld_2_17_yrs_obese_2014_15_asr_100 ~ est_ppl_4_17_yrs_adq_frt_intk_2014_15_asr_100.
  • Compute Intercept: Checked means we compute the intercept with the y-axis. Tick this box.
  • Chart Title: This is where we enter the name that we want to give the resultant plot. Type Linear Model Residual Plot: Rate of Fruit Intake vs Childhood Obesity.
  • Show Gridlines: Check this box if you want gridlines for your graph. Tick this box.
  • Greyscale: Check this box if you want your graph to be produced in greyscale. Untick this box.

A summary of the inputs to explore our case can be viewed in the image below, once complete click Run Tool.

Outputs

Once your tool has run, click the Display Output button on the pop-up window that appears. You should get a graph which looks like the graph below.

This is a linear model residual plot, with the distance of each observed point marked vertically from the horizontally linear model line.

 

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