How To Approach The ANL252 ECA Payment Delay Regression (July 2026)

Quick answer: To score well on the ANL252 July 2026 ECA, treat each question as a small decision log. Clean the payment delay data with reasons, choose three figures that each answer a different question, fit a plain linear regression with an explicit equation, evaluate it on held-out rows, and spend your last 300 words on one honest extension.

You have a CSV of invoice records, five compulsory questions, a word limit on each answer, and a rule that your Python must appear in the Word report beside the question it answers. Many students get stuck less on the code than on how much to explain in 300 words, and on whether three charts and one regression can really earn 100 marks. This guide is for that moment. It follows the ANL252 ECA payment delay question, and the worked extract in our ANL252 payment delay model-answer sample (for reference only) shows the same ideas in code.

If you would like someone to look over your own draft before the deadline, you can send your assignment details for a quote.

How are the 100 marks actually split?

Q1 and Q2 carry 30 marks each, so 60 per cent of the grade is decided before you fit a single model. That is the first thing to absorb. A student who rushes pre-processing and figures to “get to the regression” is working on the wrong end of the paper.

Q1 pays the most per word. Every sentence there should name a method, say what you did in the code, and give the reason. Remember that the limit includes in-text citations and any appendix text but not code or references.

What counts as a good pre-processing method for the payment delay data?

A good method is one you can justify from the data dictionary, not one from a generic checklist. The dataset has an amount in USD, region and city codes, customer age in months, payment terms in days, order counts and ranks, weekday and quarter fields, and the delay in days as the target.

That structure suggests the candidates. Check duplicates by Record_ID. Look for non-positive amounts. Count missing values per column. Look at the skew of Amount. Check whether rank of order and number of orders overlap. Region and City are codes, so a number-reading tool may treat them as quantities when they are labels. Any of these, shown with a before-and-after count, is stronger than “I normalised the data”.

One opinion, because it saves marks: do not drop rows just because they have a gap. Say how many rows would be lost, and then decide. Median filling for a skewed money column and an “Unknown” category for a code column are easy to defend (McKinney, 2022).

Which three figures should you choose?

Choose three that answer different questions. A histogram of the target, a comparison of delay across a category such as payment term, and a view of a time-related field such as the clearing quarter give you distribution, group difference and timing. Three scatter plots of similar variables give you one insight repeated.

Each figure needs a title, labelled axes with units, a caption number and, most importantly, a sentence beginning with what the chart shows and ending with what it implies for the model. Keep that to about 130 words per figure, since the three insights share 450. Use matplotlib or another library, but save the chart as an image for the report (Hunter, 2007).

Figure choices compared

How do you explain a linear regression in 300 words?

State the target, the predictors you kept, the ones you dropped and why, how you encoded categories, and how you split the data. That is five decisions, and it fits comfortably. Then add one sentence on the assumptions you are relying on.

The decision that most students skip is the split. If you fit and score on the same rows, your R-squared flatters you. Use a held-out test set with a fixed random seed and put the encoder and the model in one pipeline so the test rows never influence the training step. The scikit-learn documentation describes ordinary least squares and its sensitivity to correlated features, which is a good citation for mentioning multicollinearity (scikit-learn developers, 2026). The textbook treatment in An Introduction to Statistical Learning with Applications in Python is a sound second source (James et al., 2023).

A short code pattern is enough in the report:

model = Pipeline([("prep", prep), ("ols", LinearRegression())])
model.fit(X_train, y_train)
pred = model.predict(X_test)

What should the evaluation and equation look like?

Report at least R-squared and one error measure in days, such as MAE, on the test set. Then write the regression equation out, with the intercept and the strongest coefficients named in the original variable names. A printed coefficient table with no equation does not answer the question.

Interpretation is where the grade moves. Say what a coefficient means in days for a one-unit change, hold the other variables fixed, and note when a dummy variable is relative to a dropped reference level. If R-squared is low, say so plainly and explain what that means for use: the model may rank risk but not forecast individual invoices. That honesty is not a weakness. It is exactly the critical reading the later question rewards.

What can you write in Q5 that is not generic?

Pick one extension tied to something you saw earlier. If your residuals curve, try a random forest and compare cross-validated scores. If finance teams care about a threshold, convert the target to late or not late and use classification. If customers cluster by order history, try grouping them. Then state your assumption and one limitation, for instance that the file has no information on disputes or customer industry.

With only 10 marks and 300 words, resist listing five techniques. Two, with reasons, beat five names.

Mark-maximising checklist

• Every answer states what you did, why, and what it means.
• Code sits under the question it supports, in a monospaced font, with output visible.
• Each of the three figures has a title, units, a number and an insight.
• The regression equation is written out, not just coefficients printed.
• Evaluation uses unseen data and reports an error in days.
• Word counts include citations and appendix text, and exclude code and references.
• The GenAI attribution table lists tool, prompts, output and your adaptation, if you used any tool.
• You can explain each line of your own code if a viva is called.

Common mistakes markers see

• Describing pre-processing in general terms without counts from the actual file.
• Treating Region and City codes as numbers inside the regression.
• Choosing three figures that all show the same relationship.
• Reporting R-squared on training data only.
• Pasting code outputs that do not match the figures or equation in the text.
• Exceeding a word limit because the code comments were counted as prose.

For other July 2026 ECAs at the university, our ICT340 ECA approach guide follows the same question-by-question logic. You can also browse our wider SUSS assignment help page for other modules.

FAQs

Can I use GenAI tools for the ANL252 ECA?

The brief permits it with attribution. You list the tool, the prompts, the output and how you adapted it. Using it without that table, or submitting output you cannot explain, is the risk. Check the latest instructions on Canvas, since those override any summary here.

Do the code and charts count towards the word limit?

Code and references are excluded from the limits. Your explanations, in-text citations and any related appendix text are included. Keep comments in code brief so there is no confusion.

How many pre-processing methods do I need?

The task asks for at least three. Four well-justified methods are safer than three, provided you stay inside 300 words. Each one needs the code, the reason and the effect on the data.

What R-squared is good enough?

There is no fixed pass mark. Payment behaviour is noisy, so a modest value can be honest. Markers care more that you evaluate on unseen data, explain what the value means and discuss limits.

Should I use statsmodels or scikit-learn?

Either is acceptable if you can state the equation and interpret it. scikit-learn fits neatly with pipelines and train-test splitting, which is why the sample uses it.

Is it all right to remove outliers in the delay data?

Only with a stated reason. A very late payment may be real and informative. Capping or transforming is often easier to defend than deleting, and you should report how many rows were affected.

Our team can review your report structure, code and word counts if you share the brief; programming assignment help gives the details. We provide guidance and model answers; your submission and your institution’s rules remain your responsibility. And if the deadline is close, check the word counts first.

About the expert

This topic sits closest to the work of Eric Koh, whose profile on our site lists an MS in Computer Science from the National University of Singapore, with subject areas including data structures, algorithms, operating systems and computer networks. View Eric Koh’s expert profile.

References

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in Python. Springer.

McKinney, W. (2022). Python for data analysis (3rd ed.). O’Reilly Media.

scikit-learn developers. (2026). Linear models: Ordinary least squares. https://scikit-learn.org/stable/modules/linear_model.html