STAT207: Business Data Analysis Assessment Task 2

Why this Assessment?

This assessment requires students to analyse real-life data and produce a professional analytical report. Using a provided problem statement and datasets, students will apply statistical tools such as:

• Hypothesis testing

• Analysis of variance

• Correlation and regression analysis

The task strengthens critical thinking, interpretation, and communication of quantitative findings.

Employability Skills Developed

Skill Type Developed? Critical & analytical thinking Ability to solve complex problems Ability to work effectively with others, Independent learning confidence Written communication skill,s Spoken communication skills ☐ Knowledge in field of study  Work-related knowledge & skills Research skills 

Assessment Overview

You will act as a financial analyst examining the relationship between cash rates and median house prices in Australia (2002–2025). You will use:

• Dataset 1: Median house prices for Australian capital cities

• Dataset 2: Cash rate data for the same period

Your task focuses on using descriptive statistics and single linear regression to determine whether cash rates affect house prices.

This assessment reinforces:

• Quantitative data handling

• Excel proficiency

• Interpretation of statistical outputs

• Professional report writing

Feedback will prepare you for the next assessment.

Submission Requirements

You must submit:

1. PDF Report (Maximum 2 pages, excluding references)

2. Excel File containing all calculations, descriptive statistics, and charts

Submission Method:
Upload both files under “Assessment 2” on Canvas.

Important:
Use EndNote for referencing and include references at the end of your PDF.

Instructions and Steps

Step 1: Understand the Data

You are given:

• Median house prices (2002–2025) for each capital city

• Cash rate data (2002–2025)

Choose ONE city for the regression analysis.

Prepare Data in Excel

• Open both datasets

• Clean data (e.g., remove missing values, align years)

• Create a new worksheet where both datasets are merged by year

Descriptive Statistics

For each dataset, calculate:

• Mean

• Median

• Standard Deviation

• Minimum

• Maximum

Use Excel functions:

Step 4: Visualisations

Create the following charts in Excel:

1. Line Chart – House prices trend

2. Line Chart – Cash rate trend

3. Scatter Plot – Cash rate vs. house prices (for regression)

Step 5: Regression Analysis

Regression Setup:

• Dependent Variable (Y): House Prices

• Independent Variable (X): Cash Rate

How to run regression in Excel:

1. Data → Data Analysis → Regression

2. Input Y Range (house prices)

3. Input X Range (cash rates)

4. Select options:

   • Labels

   • Output Range

5. Click OK

Step 6: Interpret Regression Output

Your report must explain:

• Regression equation:
  House Price = a + b × Cash Rate

• R-Squared: how much variation in house prices is explained

• Coefficient (b): direction and strength of relationship

• p-value: statistical significance

Report Structure 

1. Introduction

• Purpose of analysis

• Importance of understanding cash rates and house prices

2. Data Overview

• Summary of datasets

• Any assumptions or data preparation steps

3. Descriptive Statistics

• Summary tables

• Visual trends

• Key insights

4. Regression Analysis

• Regression table

• Interpretation of coefficients, p-values, and R-squared

5. Discussion

• Does cash rate significantly predict house prices?

• External factors affecting results

• Limitations

6. Conclusion

• Summary of findings and implications

Brief summary of assessment requirements 

Assessment type: Written analytical report (two pages max, PDF) plus Excel workings.

Core tasks :

• Select one city for detailed regression analysis (keep scope manageable).

• Prepare and clean the two datasets in Excel:

  • Dataset 1: Median house prices (2002–2025) for capital cities.

  • Dataset 2: Cash rate (2002–2025).

• Compute descriptive statistics (mean, median, standard deviation, min, max) for the chosen city’s house prices and for cash rates.

• Produce visualisations:

  • Line chart for house price trend (2002–2025).

  • Line chart for cash rate trend (2002–2025).

  • Scatter plot of house prices vs cash rates (for regression).

• Run a single linear regression in Excel:

  • Dependent variable (Y): House Prices.

  • Independent variable (X): Cash Rate.

  • Save full regression output (coefficients, R⊃2;, p-values, residual stats) in the Excel file.

• Interpret the regression in the report:

  • Present the regression equation: House Price = a + b × Cash Rate.

  • Explain R-squared (fit), coefficient sign and magnitude (b), and p-value (statistical significance).

• Structure the two-page PDF report with: Introduction → Data Overview → Descriptive Statistics (with graphs) → Regression Analysis → Discussion (limitations, external factors) → Conclusion → References.

• Submit: PDF (≤2 pages, references excluded) + Excel file (all calculations and charts) via Turnitin on Canvas. Use EndNote for references.

Assessment focus: correct application of descriptive and inferential statistics, Excel proficiency, clear interpretation and concise professional reporting.

How the Academic Mentor guided the student

The mentor used a practical, hands-on coaching method to ensure the student met technical and reporting standards.

Clarify scope & requirements

• Reviewed the brief together, emphasising the two-file submission, two-page PDF limit, and the need to focus the regression on one city.

• Agreed a timeline and checkpoints (data prep → stats → regression → write report → final review).

Data understanding & selection

• Helped the student inspect both datasets to understand variables, units and years.

• Advised on selecting a city with complete data and clear trends (minimises missing-value handling).

Data cleaning and alignment in Excel

• Demonstrated practical cleaning steps: identify and remove NA, align years, ensure numeric formats, and keep the “Median Price of Established House Transfers (Unstratified)” series.

• Showed how to create a new worksheet merging house price and cash rate by year.

Descriptive statistics

• Taught Excel functions to compute mean, median, standard deviation, min and max .

• Advised on how to present summary tables concisely in the two-page report.

Visualisation best practice

• Coached on choosing appropriate chart types, labelling axes, adding trendlines and exporting charts for the PDF.

• Ensured line charts show year axis (2002–2025) and scatter plot is clear for regression interpretation.

Running regression in Excel

• Walked through settings:

  • Set Y range = house prices; X range = cash rates.

  • Checked “Labels” and selected a clear output range or new sheet.

• Explained key outputs to extract: intercept (a), slope (b), R⊃2;, standard error, t-statistic and p-values.

Interpreting results and writing the report

• Explained how to write concise interpretations for a two-page report:

  • Translate regression numbers into plain language (direction, strength, significance).

  • Discuss R⊃2; realistically (what proportion of variance cash rate explains).

  • Flag possible omitted variables (income, supply, policy, population) and endogeneity concerns.

• Helped the student craft a 2-page narrative that balances visuals and text: crisp introduction, clear data overview, compact stats and charts, and an insightful discussion.

Final checks and submission

• Performed a checklist review: two-page PDF limit, Excel contains all workings, charts labelled, file names follow convention, references via EndNote.

• Ran a final technical check of regression settings and chart images before advising submission.

Final outcome 

Final submission (student deliverables)

• Two-page PDF report that includes:

  • Short introduction and rationale.

  • Data overview and cleaning assumptions.

  • Compact descriptive statistics table and two small charts (house price trend; cash rate trend).

  • Scatter plot and regression summary (equation, R⊃2;, coefficient and p-value) with short interpretation.

  • Discussion noting practical limitations and external factors.

  • Clear conclusion and references.

• Excel workbook containing:

  • Cleaned merged dataset by year.

  • Descriptive statistic cells and formulas.

  • Chart sheets with labelled visuals.

  • Full regression output and residual diagnostics.

Key analytical outcomes 

• The student produced a valid single-variable regression model of house prices on cash rate and was able to:

  • State the regression equation in plain terms.

  • Interpret the slope (direction and policy interpretation) and R⊃2; (degree of explanatory power).

  • Assess statistical significance using reported p-values and discuss whether cash rate is a statistically meaningful predictor in this single-factor model.

• The student also provided a balanced discussion highlighting external drivers (supply, income growth, credit availability, fiscal policy) and data limitations (omitted variables, potential structural breaks, autocorrelation).

Learning objectives and skills demonstrated

• LO1 & LO2 (as assessed): Applied descriptive and inferential statistical tools to analyse real data and interpreted findings in a professional context.

• Graduate and employability skills:

  • Critical & analytical thinking: framed the empirical question and assessed evidence.

  • Problem solving: handled messy real-world data and model limitations.

  • Excel proficiency: data cleaning, formulas, charts and regression analysis.

  • Written communication: produced a concise, professional two-page analytical report.

  • Work-related knowledge: linked theoretical interpretation to financial-policy context.

• Professional readiness: Learned how to present quantitative findings succinctly for a stakeholder audience and how to document methods and assumptions transparently.

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