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STA305 Department of Statistical Sciences
创建日期：2024-04-29 17:15:50
所属分类：统计学statistics
标签： STA305
Department of Statistical Sciences

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Department of Statistical Sciences
Course Section
STA305 & STA1004 L0101 & L0201

1. DOs
What to submit? When due? Where?
1 Project Report as a single PDF file (1) 8 April 2022 Report, Crowdmark
2 R/Rmd codes 8 April 2022 R/Rmd, Crowdmark
3 Outputs, data sets etc. as a PDF file 8 April 2022 Output, Crowdmark
4 Video Presentation file 8 April 2022 Quercus
2. DON’Ts
2.1 E-mail submissions will NOT be accepted.
2.2 SUBMIT A SINGLE PDF DOCUMENT FOR THE REPORT. DO NOT
SUBMIT descriptive texts in one document and graphs/tables etc. in another.
3. DETAILS in the following pages.
1
Project: General
1 An Overview
Each student (no groups allowed) will plan and perform a homemade factorial experi-
ment using the guidelines given in the following. This includes designing an experiment,
collecting or generating the data, and analyzing the data in R. You are encouraged to
choose a topic of your interest for this project.
STA1004 students: If you are working on a research project where a factorial design
can be implemented then I encourage you to use your research project as your topic.
The report should not be longer than 4 pages including tables and plots and should be
submitted in one document.
An example of report layout is given at the end of this document. Feel free to use this
report as a template.
Each student is required to submit the following four items (Where do you
submit what?):
Crowdmark
1. Your final report as a single PDF file (due: April 8, 23:59 hr on Crowdmark).
See the question box: Report.
2. R/R Markdown codes file (due: April 8, 23:59 hr on Crowdmark). See the ques-
tion box: R/Rmd.
3. Supplementary materials(s) such as output file, along with necessary data files, etc.
save them as PDF file(s) (due: April 8, 23:59 hr on Crowdmark). See the question
box: Output.
Quercus
4. Video presentation of the objectives, methods, results, and conclusions of your
experiment (due: April 8, 23:59 hr on Quercus). See more on the format and size
etc. in PART III.
2
2 Details
2.1 PART I. Guidelines for the project on factorial experi-
ment
1. Number of factors under investigation should be more than two. For example,
you may develop a 24 factorial in blocks of size 8 or less, confound the interactions,
preferably not the same set of interactions in all the replicates. Also use the common
understanding “higher order interactions are either negligible or of lesser importance
compared to the lower order interactions”. Main effects must not be confounded with
blocks. You may also choose a fractional factorial design.
Other experimental designs could be a two factor factorials in Latin Squares, or as a
split plot or strip plot design in RCBD or CRD, or even split-split plot design or other
variants.
2. Write a title for your experimental investigation in the Report.
3. Write [Author: Your name, ID, specialization] in the Report.
2.2 PART II. Project Report
(Submit by April 8, 2022, 23:59 pm)
This document should have:
Introduction/Description (1/2 page maximum)
Introduce the need for this study, i.e., the rational behind this study, or, why and
what did you plan to study/investigate? Write the objectives of the study. Cite any
references used.
Materials and Methods (2 pages maximum)
Write in short the Materials and Methods you have pursued. This may include:
Experimental Design and Data
Describe the experimental design used — details of the factorial designs you generated
or acquired, features of the designs, comment on how this design align with the ob-
jectives you have set. Cite any references used. Details of the data. How did you get
the data — randomly generated? found from a secondary source on the Internet? etc.
Cite clearly the references. If you have arbitrarily inputted the data, write that as well.
3
Statistical Analysis
Details of the statistical methods used in the analysis. Cite any references used.
R/RStudio recommended for computations save the codes as *.Rmd. You may find it
more convenient to run the *.Rmd file on RStudio.cloud, but it not mandatory.
Results and Discussion (1 page maximum)
Describe and display your project findings in text, tables and figures along the lines of
the objectives (including whether the results support or contradict what you expected).
Discuss an overall canvas of the study. Cite any references used.
Conclusion (1/2 page maximum including References)
Present in short the highlights of the study.
References (within 1/2 page of the Conclusion)
List the references in alphabetical order .
Format and size of the document:
Due date for all the submissions: April 8, 23:59 hr
Recommended font size is 12pt, but not less than 10pt.
Crowdmark
1. Report in only PDF, no more than 4 pages, submit this file on Crowdmark. This
file should not contain R codes. Upload in the question box Report.
2. R/R Markdown codes file on Crowdmark. Upload in the question box R/Rmd.
3. Output file, along with necessary data files, etc. save them as PDF file(s) on Crowd-
mark. Upload in the question box Output.
Quercus
4. Video presentation of the objectives, methods, results, and conclusions of your
experiment (due: April 8, 23:59 hr on Quercus). See more on the format and size
etc.
4
2.3 PART III. Video presentation.
(Submit by April 8, 23:59 hr, and on Quercus)
Your video must meet the following criteria:
a In the beginning of the video, you must clearly present your student ID along with
yourself. The grading TA must be able to identify you and your student ID number.
Failure to present your student ID will result in a 0 grade for the video presentation.
We recommend that you update your Quercus profile with a picture where your face
is clearly identifable.
b The presentation should not exceed 5 minutes. Any video beyond 5 minutes will not be
viewed by the grading TA, and will not be considered when marking. In the video you
should describe the objectives, methods, results, and conclusions of your experiment.
c The video may be of any form, so be creative! For example, you may include clips of
yourself conducting the experiment while describing the experiment - beware that the
clips will also count towards your 5 minute limit.
d Notes on video submission. If you are using a Mac, the Quercus media recorder submis-
sion page may not work on your Safari browser. The recorder works fine with Chrome
or Firefox on both Mac and PCs.
e Beware that the Quercus media recorder doesn’t allow pauses but you are able to
retake your videos as many times as you want.
f Quercus accepts media file uploads of size up to 500MB if you are uploading a file.
The supported file types for playbacks are FLV, ASF, QT, MOV, MPG, MPEG, AVI,
M4V, WMV, MP4, and 3GP. If you upload any other file types, the TAs may not be
able to assign you a grade.
5
3 PART IV. Grading Scheme/Grading Rubric.
[Total Marks = 10+10+5=25] Report (submitted on Crowdmark) [Question
box: Report]
[Marks =10] 4.1 Description of the design including objectives of the study.
Grade (points) Descriptions
Excellent (10) Strong evidence of original thinking and a clear explanation of how and why they conducted
the experiment.
Good (8) Grasped the basics of designing a factorial study; a good explanation of how and why they
did the experiment.
Adequate (6) Understood the basics of designing a factorial study, but may not have designed a factorial
experiment. Provided an adequate explanation of the design.
Marginal (4) Some evidence of understanding the basic design of a factorial study. Provided a poor
explanation of their design.
Inadequate (2) Little evidence of even a superficial understanding of a factorial design. Little explanation
about how or why the design was chosen.
[Marks =10] 4.2 Analysis of the data (including methods, 2 pages maximum)
Include appropriate plots and calculations such as: main effects and interactions; estimated variance
of the effect (if replicated); confidence intervals for true values of effects (if replicated); Lenth plot; or
half normal plot.
Grade (points) Analysis of the data
Excellent (10) Strong evidence of data analysis skills. Probably used R to do calculations and plots, but
calculations and plots might also be done neatly by hand.
Good (8) Good evidence of data analysis skills. Appropriate calculations were done, and maybe
appropriate plots were included.
Adequate (6) Understood the basics of required data analysis.
Marginal (4) Some evidence of understanding the basic data analysis required, but might not have carried
out all the appropriate calculations and plots.
Inadequate (2) Little evidence of even a superficial understanding of the data analysis required to analyse
a factorial design.
[Marks =5] 4.3 Conclusions. (1/2 page maximum including references)
What conclusions can you make based on the results of your experiment? Write a paragraph or two
outlining these conclusions.
Grade (points) Conclusion
Excellent (5) Conclusions are highly appropriate given the experiment conducted. Clearly written.
Good (4) Conclusions are appropriate given the experimental context. Writing is good.
Adequate (3) Some conclusions are appropriate; other obvious conclusions might be missing.
Marginal (2) Some evidence that there was an understanding of the basic conclusions, but several obvious
conclusions not stated.
Inadequate (1) Little evidence of even a superficial understanding of the conclusions that can be drawn
from the experiment.
6
[Total Marks = 5 ] 4.4 R/Rmd (submitted on Crowdmark) [Question box: R/Rmd]
Assessment objective: Appropriate use of built-in R functions.
Grade (points) R Markdown notebook.
Excellent (5) Appropriate R functions are used correctly to perform the intended tasks. Entire
notebook runs without an error.
Good (4) Appropriate R functions are used but may con- tain mistakes in their usage. May
contain errors but they do not interrupt the analysis steps. Reproduces similar
results as presented in the report but some numeric results may be different.
Adequate (3) Most R functions are used inappropriately and do not perform the intended tasks.
Contains errors that interrupt some parts of the analysis steps. Produces conflicting
results for minor parts of the conclusions presented.
Marginal (2) Most R functions are used inappropriately and do not perform the intended tasks.
Contains errors that interrupt the analysis steps but requires only minor fixes. Pro-
duces conflicting results for most of the conclusions presented.
Inadequate (1) Contains major errors and does not reproduce the result presented.
[Total Marks = 10 ] 4.5 Video presentation (submitted on Quercus) [Quercus Quizzes:
Video Presentation].
Assessment objective: Effective presentation of study objectives, methods, re-
sults, and conclusions.
Grade (points) Presentation
Excellent (10) Information is presented in a logical and interesting sequence. Experiment objec-
tives, methods, results, and conclusions clearly stated, repeated appropriately, and
strongly supported throughout the presentation. Clearly audible voice throughout
the video.
Good (8) Information is presented in a logical sequence. Experiment objectives, methods,
results, and conclusions clearly stated and supported throughout the presentation.
Audible voice throughout the video.
Adequate (6) Presentation jumps around topics making it difficult to follow. Experiment objec-
tives, methods, results, and conclusions are stated but minimally supported through
out the presentation. Voice is not audible in some parts of the video.
Marginal (4) Presentation has no sequence of information and audience cannot understand the
presentation. Experiment objectives, methods, results, and conclusions are not
explicitly stated and need to be guessed. Majority of the video is not audible.
Inadequate (2) A video presentation was submitted with the student ID presented, but the video
is not audible throughout the presentation.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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