STT500 Subject Name Statistics for Decision Making Assessment Number and Title Assessment 3, Individual Statistical Case Scenario Analysis Assessment Type Report and Viva P

Assessment Information and Rubric
Subject Code
STT500
Subject Name
Statistics for Decision Making
Assessment Number and Title
Assessment 3, Individual Statistical Case
Scenario Analysis
Assessment Type
Report and Viva Presentation
Length / Duration
1500 words, and 8-10 min for Viva
Weighting %
Report 15%, Viva Presentation 15%
Total Marks
100
Submission
Turnitin
Due Date
Week 10, Report Submission, Week10, Viva
Presentation
Mode
Individual
Format
Report
Assessment Description and Instructions
Description
Each student must provide a 2000-words to report the findings of their assignment
plus a video presentation will be prepared based on the material discussed. Your
assignment will be prepared based on the material discussed and presented in weeks
7 to 10 lectures and tutorials. Consequently, the topics may include evaluation of
hypotheses testing, Regression analysis, and Time Series analysis. Please use Excel
for statistical analysis in this assignment. Relevant Excel statistical output must be
properly analysed and interpreted
Assignment Data
Demonstrate your ability to perform statistical analysis of a data set. You will locate
your own data set a great source of data is available at Kaggle
(https://www.kaggle.com/)
Your data must contribute to addressing the research objective/questions and cover
the following:
• At least three continuous variables for analysis.
• At least one grouping variables, each with two distinct categories.
Assignment Questions
Make sure the following questions are covered in the data analysis:

1. Develop appropriate summary tables and visualizations (such as bar charts
for categorical variables and histograms or boxplots for numerical variables)
to explore the distribution of property type, presence of a garage, total interior
space, and proporty agc. What patterns or insights do those reveal? For
statistical analysis involving a hypothesis test for comparing the average
interior space of properties with and without a garage (independent two-
sample t-fest): Formulate the null and alternative hypotheses. State your
statistical decision using the significant value (a) of 5%.
2. Evaluate the performance of a simple linear regression analysis using
property age as the independent variable and sale price as the dependent
variable. Describe and explain your process for variable selection. Justify your
choices with regression data analysis.
3. Using diagnostic plots or statistical tests, evaluate whether the assumptions of
the simple linear regression model are satisfied. Discuss the impact of any
violations you identify.
4. Evaluate the performance of a multiple linear regression analysis using three
numerical variables (e.g., property age, interior space, and land size) to
predict sale price. Describe and explain your process for variable selection.
Justify your choices with regression data analysis.

4. Evaluate the performance of a multiple linear regression analysis using three
numerical variables (e.g., property age, interior space, and land size) to
predict sale price. Describe and explain your process for variable selection.
Justify your choices with regression data analysis.
5. Check the model assumptions for the multiple linear regression model and
identify common violations.
6. Examine the residual plots for both simple linear regression (Model 1) and
multiple linear regression (Model 2). What do these plots indicate about the
validity of model assumptions and the quality of fit?
7. Construct a 95% confidence interval for the coefficient of property age in the
simple linear regression model. Based on this interval, what can you conclude
about the significance of property age as a predictor?
8. Using a 10% level of significance (a = 0.10), evaluate whether the variables—
property age, interior space, and land size-collectively make a statistically
significant contribution to the multiple linear regression model predicting sale price.
Clearly state your hypotheses, conduct the appropriate significance test, and
interpret the results to determine if the model provides meaningful explanatory
power based on these predictors.