QMS102 SPSS ASSIGNMENT #1 Fall 2015
Due Date: Your project is due in Week 10 and it is due in class. There will be
a penalty for submitting late.
Marks: Each question is worth 5 marks with a total of 35 marks. (Note: There will be a
1 mark penalty for every day late.)
Group: You are allowed to work independently or in a group of two.
You will analyze data that shows some details of a sample of 80 properties that have
recently been sold by a certain real estate company in the province of Alberta.
Reports will be graded based upon the ability to present clear and correct results that demonstrate that
they have learned the rules and techniques that they have been taught in the course. Questions 1 to 7
below must be answered using one SPSS input data file. It may be necessary to add variables to the
original input data in order to answer the questions.
As a minimum, your report must include the following:
1. A title page that stresses the content of the report but also includes your name, student number
and course number.
2. A brief introduction about what you are attempting to do in this report including a mention of
what data is being analyzed, what computer package is being used and what methods will be
3. The answers to the questions.
4. An appendix that includes your input data file as it appears when the project is finished.
5. A table showing the variable properties, i.e. variable view.
6. The original data sheets that were given to the group.
Notes: 1. All statistical results in your report must have been created in an SPSS output file (i.e.
do not import and edit in a word processor).
2. No handwriting will be considered for marking purposes.
The real estate data consists of 6 variables:
X1 = Location
X2 = Type
X3 = List Price ($000’s),
X4 = Size
X5 = Numbers of Bedrooms
X6 = Number of Bathrooms.
1. Input the data for variables x1 (Location), x2 (Type), x3 (List Price), x4 (Size), x5 (Beds)
and x6 (Baths) into an SPSS data sheet. Use labeling techniques for x1 and x2,
e.g. 0 = Calgary area, 1= Edmonton area, etc.
2. For each variable, identify the type of data and measurement scales.
3. Make a pie chart showing the percent of properties with 2, 3, 4 and 5 bedrooms.
4. Make a clustered bar chart showing the number of properties that have 1, 1.5, 2, etc.
bathrooms in each of the 3 locations.
5. Using the type variable as a ‘split file’ variable:
a) Make 3 histograms of the price data. Use 'similar' classes for each histogram.
6. Using the location variable as a 'split file' variable:
a) Make one table (edit by removing items not asked for) that shows only the mean,
median, and 80th percentile of the size data for each of the 3 groups of data. Refer
to all 3 statistics and comment on which location has the largest house sizes.
b) Make one table (edit by removing items not asked for) that shows only the standard
deviation and interquartile range of the size data for the 3 groups of data. Based on
each statistical measure, which region has the least amount of variability in house
7. Make a graph of multiple box-whisker plots of list price data for the different type of
location. Based on observing the graphs only (no other calculations): which location
generally had the highest prices? Comment on the variability of each group of prices. Be
specific and concise with your comments. Are there any locations where a price is
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- Submitted On 18 Nov, 2015 11:33:42