UA Problem of Quantitative Methods Statistics & Linear Regression Model Worksheet

Description

I hope you can help to solve the problem of Quantitative Methods. For specific tasks, you can browse the files I uploaded. There are three main tasks. I also uploaded the data and rating criteria you need.

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Quantitative Methods (M) & (H) Semester 2, 2020
Major Project
Project Instructions

The project is separated into 3 interrelated tasks. All 3 tasks are due at the same time in report format.
The final due date is Friday 6th of November at 1pm Adelaide time (UTC +09:30). Please submit
your projects online via MyUni by uploading a single file in Microsoft Word format (either .doc or
.docx) through the Assignments tab and associated link. If your assignment is late, please email it to
George personally.

Tasks 1, 2 and 3 most closely relate to Topics 2, 8 and 9 as detailed in the course outline.

This is an individual project (you can discuss it with other students but everyone needs to make an
individual report submission).

The project comprises 50% of your final Quantitative Methods (M) grade with the following assessment
breakdown:
 Headline Regression Model Derivation
10%
 Task 1: Data Summary
20%
 Task 2: Regression Model – Build and Use
40%
 Task 3: Regression Model – Evaluation
20%
 Report Structure and Written Presentation Quality
10%

Your final report will be processed through Turnitin as a check for plagiarism so please ensure that you
only present your own work. In addition, your final report needs to meet all of the following criteria:
 Font:
Times New Roman
 Font size:
12 point
 Page margins:
2.54cm all around (Normal)
 Page Limit:
5 pages only (A4, single sided)
 Appropriate font, font size, and page margins are all graded against ‘Written Presentation
Quality’. Submissions which exceed the page limit will only have their first 5 pages graded.

Further Advice:
 Do not use a title page.
 Do not use a table of contents.
 Only provide a short introduction.
 Ensure your report is free from spelling and grammatical errors.
 Ensure your report is clear and well-structured.
 Ensure your report is written in context and answers questions in context.
 Make sure it is clear which model is your “Headline Regression Model” (Final answer)
Loxton is a small town with two suburbs. The data file “Major Project – Data Set” contains data on 545 houses
sold in Loxton between 2015 and 2020. This data includes the price at which the house was sold, which of two
agents sold the house (all houses are sold through an agent by law), the year in which the house was sold as well
as data on various characteristics of each house sold (age, size, number of stories etc.). These characteristics
serve as possible explanatory variables of sale price.
Data definitions follow:
OBS =
AGE =
SHOPS =
CRIME =
TOWN =
STORIES =
OCEAN =
POOL =
PRICE =
SELLER =
SIZE =
SUBURB =
TENNIS =
SOLD =
observation
age of house in years
1 if house is close to shopping precinct, 0 otherwise
crime rate of the suburb within which the house is located
distance in kilometres to the town centre
number of dwelling stories
1 if house has an ocean view, 0 otherwise
1 if house has a pool, 0 otherwise
price at which the house was sold (in dollars)
selling agent – “W&M” (0) or “A&B” (1)
size of the house in square metres
Mayfair (0) or Claygate (1)
1 if house has a tennis court, 0 otherwise
year of last sale (2015 to 2020)
Your tasks
Task 1 – 20% of project grade (recommended length of 1 page)
You are required to provide a comprehensive summary of the data set contained in the “Major Project – Data
Set” file. How you choose to do this is entirely at your discretion. However, it is recommended that you
consider using both summary statistic and graphical methods while also noting any peculiarities within the data
set.
Task 2 (including Headline Regression Model) – 50% of project grade (recommended length of 2.5 pages)
You have been hired by Jane, the wealthy owner of a house on Elm Street in Loxton (not included in the data
set) to predict the price at which her house will sell. Her house has two stories, is in Claygate, is 192 square
metres large, is not near a shopping precinct and is 10 km from the town centre. She estimates that the house is
about 10 years old and in a low crime area according to her experiences. Jane inherited the house from her uncle
and is therefore unsure when it was last sold. Some other features of the property can be seen below:
1
Views of and from the house whose sale price you are to predict
You are expected to build a regression model of house prices. In doing so, make sure that you use an
appropriate number of predictors to develop your estimates. Once you have constructed an appropriate model,
use it to obtain and provide for Jane’s house:
1.
2.
3.
4.
A point prediction of the sales price which it can be expected to fetch
A 95% interval prediction for this sale price
An estimate of the marginal effect of house size on this sale price
Financial advice on whether Jane should use “W&M” or “A&B” to sell her house. “W&M” charges a
commission of 5% whereas “A&B” charges a commission of 10% of the final sale price.
Jane, who claims to have some knowledge of regression analysis, has stressed that she thinks you should use a
regression model with an R2 of at least 85%.
Note: Task 1 directed you to take note of any peculiarities in the data set. There are other additional errors in the
data set that you may not have picked up on in Task 1. These will only become clear to you once you start
working on Task 2. Several problems can result if you fail to handle these issues correctly, so be mindful to
address them, both in your regression application as well as your final report. If resolving any of the errors in
the dataset requires you to make assumptions, make sure to clearly state your reasoning and approach in your
report.
Task 3 – 20% of project grade (recommended length of 1.5 pages)
Please provide a reflective discussion on how you executed Task 2 of the project above. Specifically consider
the following:
1.
2.
3.
4.
Verify that your regression model does not suffer from any misspecification errors and provide the
relevant regression diagnostics which support your findings.
If you found that your model is in fact partially misspecified in part (1) of Task 3 above, explain what
you did to ensure that the misspecification only has a minimal impact on your results in Task 2 above.
Were there any other oddities in the data set or your model? Explain.
Is there anything else worth mentioning which is relevant to your work or to your results for Jane?
2
OBS
AGE
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41
42
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44
45
46
SHOPS
11
15
13
16
14
21
15
20
9
14
14
16
14
14
17
14
19
18
18
17
13
11
16
21
11
16
14
15
13
17
15
13
19
15
13
13
14
16
15
14
17
19
15
15
11
14
CRIME
0
1
0
1
1
1
0
0
1
2
0
2
1
1
3
3
1
3
2
0
2
2
2
0
1
2
2
1
1
2
1
1
1
1
3
1
1
1
2
1
2
2
1
1
2
1
TOWN
3
3
3
3
3
3
1
3
3
3
3
1
3
3
3
3
1
1
3
3
3
3
3
3
3
3
3
3
3
3
1
3
3
3
3
3
1
3
3
1
3
3
3
3
1
1
STORIES
60
60
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60
30
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60
30
60
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30
60
60
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60
30
30
OCEAN
2
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
POOL
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PRICE
380875
375348
380769
410756
477252
402323
334363
417701
427162
394807
309693
445537
312503
334913
383261
451008
389315
541813
443936
240305
467798
458823
413689
368867
391602
408485
387082
374184
391832
403907
429234
431566
393845
407309
388534
395802
454597
351463
341814
420321
391722
448386
509114
420972
435763
446006
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13
1
2
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2
0
0
0
2
1
2
1
0
0
1
1
1
2
0
1
2
1
2
1
1
2
2
1
1
2
0
1
1
1
1
1
1
1
2
1
2
2
0
1
1
1
2
1
1
3
1
1
3
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3
1
1
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1
1
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1
1
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1
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3
1
1
1
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1
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1
3
1
3
3
1
30
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1
1
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1
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1
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1
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1
1
1
1
1
1
1
2
1
2
1
1
1
1
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1
1
2
1
1
1
1
2
1
1
1
1
1
2
1
1
1
1
2
1
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
589531
469729
373764
392473
325421
390583
371426
395805
330794
455984
372642
494678
353423
435150
382936
500809
400488
414481
507882
459354
365303
422286
422785
375616
409544
390019
396287
402759
441698
329837
454800
384992
416951
456345
447030
410130
515777
454587
469038
448806
436055
391887
360730
453422
431453
351974
401478
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1
0
1
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1
1
0
0
2
0
1
1
2
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1
0
0
0
0
0
1
0
1
1
0
1
0
1
2
0
1
0
1
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2
1
0
1
1
1
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0
0
0
1
1
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1
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1
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1
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1
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1
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1
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1
1
1
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1
1
1
2
1
1
2
1
1
2
1
1
1
1
1
1
1
2
1
1
2
1
0
0
0
0
0
0
1
1
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
394928
421206
427498
476095
338954
423055
388107
690483
469212
375783
487940
442007
415233
402512
314655
364174
465049
492068
436846
452123
431326
376694
320494
451703
464715
487537
458816
515385
375132
431965
405554
542869
367580
367821
415649
413293
352914
413629
323644
402971
459019
383593
472939
370002
361795
447410
451313
141
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13
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16
12
14
14
17
15
15
13
16
19
1
1
1
0
1
3
0
2
2
2
2
1
2
3
1
2
1
1
1
1
2
1
0
1
1
3
1
0
1
2
0
1
2
1
0
1
1
0
0
1
1
1
2
3
1
1
1
3
3
3
3
1
3
1
3
3
3
3
3
1
3
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3
3
1
3
3
3
1
3
1
3
3
3
3
3
1
1
3
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3
1
3
3
3
1
1
3
1
3
3
3
3
3
60
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1
1
2
1
1
2
1
1
1
1
1
1
1
1
1
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1
1
1
1
1
1
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1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
2
1
0
1
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
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1
0
0
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0
0
0
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1
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1
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1
0
0
0
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1
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0
0
0
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1
0
0
0
0
1
0
0
0
336630
453986
433944
386013
342646
548864
434689
386720
424489
364599
410802
326528
497767
447145
426893
381209
366233
446196
471540
425569
424092
411988
415186
450797
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458593
391227
418218
316349
476664
512064
318230
437112
407164
407980
394049
410885
431645
392069
377165
311939
500124
347319
373037
377380
516046
383870
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14
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13
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15
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16
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2
1
2
2
2
0
2
0
2
0
0
1
0
2
1
2
1
1
0
1
1
2
2
2
1
0
0
0
1
1
2
1
1
0
0
2
1
2
1
1
0
1
0
0
1
0
1
3
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1
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1
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1
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1
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0
0
0
1
0
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0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
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0
0
0
0
389031
275425
454332
340075
358949
398520
374461
407305
436823
341299
422691
376523
466964
390090
417907
336375
362727
432708
470029
449322
486416
410499
286012
386841
357365
420901
372710
467615
476100
398386
402494
399315
482543
383464
468177
383521
475941
309467
308381
463363
360593
348773
409499
386097
505678
330929
440308
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
15
16
16
13
11
13
13
20
14
17
16
15
18
17
17
13
13
19
14
18
15
12
11
17
14
15
15
18
14
2
3
2
1
2
1
1
2
2
0
0
0
0
1
0
0
2
2
0
2
1
2
3
2
1
0
0
0
1
1
3
3
1
3
3
3
1
1
1
3
1
3
3
3
3
3
3
3
3
3
3
3
3
3
1
3
3
3
30
60
60
30
60
60
60
30
30
30
60
30
60
60
60
60
60
60
60
60
60
60
60
60
60
30
60
60
60
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
2
1
1
1
1
2
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
373734
403940
372136
522801
340760
332198
428967
469787
398670
426041
453298
528188
341139
448973
421332
423401
360286
433203
335652
422742
404595
410909
288175
334736
508970
534622
410302
378559
324353
SELLER
SIZE
0
1
0
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
1
SUBURB
156
145
147
157
202
163
126
155
171
171
119
150
116
134
148
163
145
188
166
89
155
191
174
143
161
167
147
148
157
161
157
175
162
151
171
158
162
141
136
180
167
204
192
173
169
146
TENNIS
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
0
0
1
1
SOLD
1
0
0
0
0
1
0
0
0
0
0
0
1
0
0
1
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2016
2020
2019
2018
2016
2019
2015
2018
2015
2017
2019
2016
2019
2016
2017
2018
2016
2019
2019
2016
2016
2019
2016
2017
2018
2017
2017
2019
2016
2019
2018
2017
2018
2018
2019
2019
2016
2018
2019
2015
2015
2015
2017
2019
2018
2018
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
0
1
0
0
1
0
0
182
155
136
146
129
164
172
148
131
187
145
171
151
165
158
155
135
160
154
160
150
147
158
157
160
143
164
172
174
134
175
137
169
184
167
135
186
186
196
198
184
133
140
201
181
142
161
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
1
1
0
0
0
1
1
0
0
1
0
0
0
0
0
1
1
1
0
1
0
0
1
0
1
0
0
1
0
0
0
0
0
0
0
1
1
0
0
0
0
1
0
1
0
0
1
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
2018
2019
2019
2019
2019
2019
2020
2018
2015
2017
2017
2016
2019
2016
2015
2016
2019
2016
2017
2019
2016
2018
2019
2017
2015
2016
2015
2018
2019
2016
2020
2019
2020
2019
2016
2015
2016
2018
2017
2016
2016
2015
2015
2020
2016
2015
2016
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
1
0
1
1
0
0
0
0
0
1
0
1
0
0
0
0
1
1
0
0
0
0
0
0
164
173
183
203
119
160
146
281
181
150
179
170
160
170
131
174
172
209
190
142
180
147
117
197
157
216
185
184
157
168
161
195
159
135
143
143
132
158
139
137
150
156
167
151
134
147
163
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
0
1
0
0
0
0
0
1
1
0
0
1
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
2019
2020
2018
2016
2015
2017
2020
2017
2019
2020
2016
2015
2017
2019
2016
2020
2017
2017
2017
2019
2017
2015
2019
2015
2017
2016
2016
2017
2016
2017
2017
2020
2018
2018
2019
2017
2019
2018
2017
2017
2020
2019
2017
2016
2018
2018
2019
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
1
1
0
0
0
1
0
0
1
0
1
138
155
168
156
126
210
160
147
189
148
160
131
179
153
165
153
160
149
198
180
182
133
176
157
172
174
155
165
125
187
165
116
175
163
123
156
162
179
153
154
126
186
138
150
146
182
160
0
0
0
0
1
0
1
0
0
0
0
0
1
0
0
0
0
1
0
0
0
1
0
1
0
0
0
0
0
1
1
0
0
0
1
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
2020
2016
2017
2018
2020
2018
2017
2016
2017
2018
2020
2015
2015
2017
2019
2019
2016
2016
2016
2016
2018
2018
2018
2017
2019
2018
2019
2016
2019
2017
2017
2019
2018
2015
2019
2018
2016
2016
2018
2015
2020
2018
2017
2015
2019
2016
2019
0
1
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
1
0
1
0
0
1
0
0
0
1
1
1
0
1
0
1
0
0
0
0
0
0
0
184
145
131
183
203
166
130
165
235
223
160
215
163
128
152
175
148
164
150
150
141
189
150
217
129
170
161
157
146
135
162
159
181
134
158
142
154
200
214
121
173
143
146
139
148
160
162
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
1
1
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
2020
2018
2020
2019
2018
2016
2016
2018
2015
2020
2018
2015
2018
2020
2016
2018
2018
2018
2019
2017
2017
2017
2016
2016
2020
2019
2017
2018
2015
2019
2018
2018
2018
2017
2019
2016
2015
2017
2019
2015
2019
2017
2015
2018
2017
2015
2019
1
0
0
1
0
1
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
1
0
1
0
1
0
0
173
158
143
130
142
172
133
178
133
151
224
155
157
150
178
136
170
177
157
144
187
159
165
154
161
147
150
162
122
107
184
155
156
105
147
161
175
166
193
152
155
146
152
136
175
150
138
1
0
0
1
1
0
1
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
1
0
0
1
0
0
0
0
0
0
1
1
1
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
0
0
2017
2019
2016
2017
2016
2017
2016
2015
2015
2019
2018
2019
2017
2019
2017
2016
2015
2020
2018
2017
2017
2016
2018
2016
2018
2016
2019
2019
2017
2018
2017
2017
2019
2016
2016
2015
2017
2017
2017
2018
2020
2018
2018
2018
2018
2016
2018
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
0
1
0
1
0
1
0
0
0
0
0
0
1
0
131
151
148
171
165
142
170
160
154
131
127
146
130
151
177
130
222
161
125
132
172
268
185
146
166
161
198
141
160
214
164
188
166
154
116
148
177
167
141
147
176
148
171
173
157
143
213
0
1
0
0
0
0
0
0
0
1
1
1
0
1
0
0
0
0
1
0
1
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0
1
1
1
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
2016
2020
2018
2017
2019
2017
2019
2015
2020
2015
2020
2017
2016
2016
2019
2016
2017
2019
2017
2017
2020
2018
2017
2015
2019
2018
2017
2018
2017
2018
2018
2016
2020
2018
2016
2018
2017
2017
2016
2019
2020
2017
2016
2015
2018
2019
2015
0
0
0
1
1
0
0
0
1
1
0
0
0
1
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
165
147
153
180
144
159
175
176
113
190
181
168
197
156
146
120
164
125
131
144
171
157
116
161
182
164
115
169
167
158
143
166
199
136
117
157
156
152
134
158
126
153
157
172
169
152
166
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
1
1
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
2017
2017
2015
2019
2017
2016
2016
2019
2020
2016
2016
2019
2019
2017
2019
2018
2018
2017
2020
2019
2019
2016
2019
2020
2019
2020
2016
2017
2017
2019
2016
2015
2017
2016
2016
2018
2018
2020
2018
2020
2016
2018
2016
2019
2019
2017
2017
0
0
0
0
1
0
1
1
0
1
0
0
1
0
0
0
0
1
0
0
1
1
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
1
201
143
170
187
192
249
145
177
157
167
178
132
138
175
141
159
156
184
148
125
166
169
124
156
160
162
182
179
138
177
136
124
161
194
456
150
171
164
166
150
135
162
127
161
208
156
176
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
1
1
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
2016
2016
2016
2016
2020
2018
2016
2018
2019
2018
2017
2015
2017
2017
2016
2017
2018
2017
2017
2018
2015
2015
2018
2020
2016
2018
2019
2018
2020
2018
2017
2017
2016
2016
2019
2017
2019
2020
2019
2016
2015
2015
2020
2016
2018
2018
2015
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
1
1
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
167
114
125
154
142
144
136
167
208
144
141
149
208
164
171
181
170
154
142
163
180
216
161
132
174
144
186
122
135
154
152
195
170
143
154
132
171
165
156
165
152
207
170
182
161
168
144
1
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
1
0
0
0
0
1
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
1
0
1
0
0
0
0
0
1
0
0
0
2017
2020
2018
2016
2019
2016
2018
2019
2018
2015
2015
2020
2017
2019
2018
2018
2017
2016
2018
2017
2016
2020
2017
2019
2017
2015
2017
2016
2018
2017
2016
2015
2017
2018
2016
2016
2017
2020
2018
2018
2020
2020
2017
2018
2016
2020
2015
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
1
0
0
1
1
0
1
0
0
0
0
1
0
1
0
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
161
107
164
145
122
166
134
167
160
136
181
154
194
168
162
137
142
196
155
184
152
166
124
173
147
159
151
181
159
151
154
153
186
141
187
164
191
126
104
143
160
119
164
154
193
137
188
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
1
0
0
0
0
0
0
2019
2017
2019
2017
2019
2017
2019
2019
2020
2017
2018
2019
2019
2017
2017
2016
2017
2019
2018
2018
2020
2019
2017
2018
2016
2016
2018
2019
2019
2017
2017
2018
2019
2018
2018
2020
2018
2020
2017
2016
2020
2018
2018
2016
2016
2016
2017
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
1
0
151
164
161
200
132
144
154
195
157
166
185
171
149
181
172
168
142
155
148
165
172
189
116
130
199
221
181
154
131
1
0
0
1
0
0
0
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
2016
2018
2020
2018
2020
2017
2018
2017
2018
2016
2016
2015
2019
2016
2015
2016
2018
2019
2015
2019
2016
2015
2016
2017
2019
2018
2019
2017
2018
Quantitative Methods (M) and (H) – Major Project Grading Rubric
Grade Band
Headline Regression
Model
(10 marks)
Task 1 – Data
summary
(20 marks)
Task 2 – Regression
Model, Build and Use
(40 marks)
Task 3 – Regression
Model, Evaluation
(20 marks)
Report Structure and
Written Presentation
Quality
(10 marks)
Fail
(

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Central Limit Theorem

Linear Regression Model

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