Conduct exploratory data analysis using appropriate tables

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Reference no: EM133997649 , Length: Word Count:1500

Statistics for Decision Making

Assessment - Case scenarios for data analytics

Assessment Description and instructions

Description

This group assignment requires students to prepare a structured business data analysis report of approximately 1.500 words accompanied by an 8-10 minute in-class presentation delivered under invigilated conditions. Acting as data analysts for a property investment firm, you are required to collect a real-world property dataset, perform appropriate statistical analyses, and present clear, evidence-based insights to support business decision making. Excel (or another statistical tool such as R or Python) may be used to perform the calculations; however, all statistical outputs must be properly analysed, clearly interpreted in context, and incorporated into both the written report and the in-class presentation.

Students must locate and use a free publicly available dataset containing property sales or housing characteristics, such as sale price, year built, number of rooms, total interior area, property type, or garage availability. No AI shortcuts — Only authentic assignment help from real expert tutors.

Different dataset, and students are expected to coordinate with their peers to avoid duplication and ensure originality of work.

Students are required to use the generated data to answer the assessment questions.

Follow these steps to carry out the data analysis:

1. Executive Summary: Start your report with a brief executive summary (150-200 words) highlighting the key findings, major statistical conclusions_ and business implications of your analysis.

2. Dataset Description: Describe the dataset you have collected, including the source, variable list and types (continuous, categorical, etc.), sample size, and any assumptions or limitations. Ensure the data et contains at least 100 observations and both categorical and numerical variables.

3, Exploratory Data Analysis (EDA): Conduct exploratory data analysis using appropriate tables and visualisations. Include frequency distributions and charts (bar, pie) for categorical variables_ and histograms and descriptive statistics for numerical variables. Discuss the shape, centre and spread of distributions. identify outliers. and comment on patterns in the data

4. Statistical Inference:
a. Nomiality Assessment: Evaluate whether the key numerical variable (e.g., Property Age) follows a normal distribution using multiple forms of evidence such as histogram shape, skewnessikurtosis, comparison of mean and median: and a normal probability plot_
b. Confidence Intervals: Construct a 95% confidence interval for the population's average Year Built (or equivalent variable), and a 90% confidence interval for the difference in average Total Interior Space between properties with and without a garage_ Provide clear interpretation of each interval in the business context_
c. Hypothesis Testing: Formulate and test a relevant hypothesis (e.g., whether the population's average Property Age exceeds a benchmark). Clearly state the null and alternative hypotheses, calculate the test statistic and p-value, state the decision rule at a 5% significance level, and interpret the result.

5 Business Insights and Recommendations: Summarise the implications of your statistical analysis far the property investment firm_ Discuss how characterisks such as age. size, or presence of a garage affect property value or suitability. Identify any risks, limitations, or uncertainties in your findings.

Reference no: EM133997649

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