SAS Macro practice
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Applying SAS Macro to build a model for US Adult Census Income. The purpose of this project is to practice SAS Macro skill and hit all points of the assignment.
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1 PHY 241 Fall 2018 PHY 241 Lab 7- Momentum is Conserved.docx
1
PHY 241 Fall 2018
PHY 241 Lab 7- Momentum is Conserved
Introduction:
Momentum is a vector quantity which is measured by taking the product of an objects mass and
velocity,
𝑝 = 𝑚�⃗�. (1)
Much like energy, the concept of momentum is useful because we have a law which guarantees that the
momentum of an appropriate system is conserved.
“The total amount of momentum in a system is a constant unless momentum is transferred
through the system boundary by an Impulse.”
Where an impulse is an external force which acts on a system over time,
𝐼 = ∫ 𝐹𝑒𝑥𝑡⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ 𝑑𝑡.
Equipment:
Two CBR 2- connected directly to a computer using USB cables
Various collision carts
Mass blocks for carts
2 m track
Bubble level
Computer with Logger Pro or Logger Lite and Excel.
Triple beam balance scale.
Procedure:
1) Design a procedure to collect the information you need to measure the momentum of two
carts simultaneously. WARNING: Occasionally, the clicks from your two different CBRs will
interfere with each other and give incorrect data. Your group should develop criteria to
determine when data is invalid and a response.
2) Generate a plot of the momentum of each cart as well as the total momentum similar to
“Carts’ Momenta.” Notice you must correct for the fact that the two different CBRs are
using different coordinate systems.
2
PHY 241 Fall 2018
3) Similarly, generate a plot of the kinetic energy of each cart as well as the total kinetic
energy.
4) This should allow you to make a single plot containing both the Kinetic Energy and the
Momenta for the same collision. Notice you will need to let Excel know that Energy needs
to be plotted on a “Secondary Axis” because these two quantities have different units.
1 1.2 1.4 1.6 1.8 2
E
n
e
rg
y
(
J)
M
o
m
e
n
tu
m
(
k
g
m
/s
)
Time (s)
Energy and Momentum
Total Momentum Total Kinetic Energy
1 1.2 1.4 1.6 1.8 2
M
o
m
e
n
tu
m
(
k
g
m
/s
)
Time (s)
Carts' Momenta
Cart 1 Cart 2 Total Momentum
1 1.2 1.4 1.6 1.8 2
E
n
e
rg
y
(
J)
Time (s)
Carts' Energies
Cart 1 Cart 2 Total Kinetic Energy
3
PHY 241 Fall 2018
5) At this point there are a few questions that that arise from the Energy and Momentum
graph above. To
A) DA- Is the behavior of the Energy and Momentum graph unique to the specific details of
the collision. Collect energy and momentum data for at least four different collisions
(magnet/spring/Velcro, different mass carts, etc.) and find a way to visualize all this data
so you can qualitatively compare and contrast features you see in the data.
B) Researcher- Choose a single trial to investigate momentum carefully. Is momentum
conserved? Measure the Impulse generated by force(s) on your system and see if you
can account for any changes in momentum you observed. Be as quantitative as
possible.
C) PI- Choose a single trial to investigate energy carefully. Energy appears to
Data Mining for Education. Ryan S.J.d. Baker, Carnegie Mellon University
Data Mining for Education
Ryan S.J.d. Baker, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
rsbaker@cmu.edu
Article to appear as
Baker, R.S.J.d. (in press) Data Mining for Education. To appear in McGaw, B., Peterson, P.,
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Student Alcohol Consumption Prediction: Data Mining Approach
Alcohol consumption in higher education institutes is not a new problem; but excessive drinking by
underage students is a serious health concern. Excessive drinking among students is associated with a number
of life-threatening consequences that include serious injuries; alcohol poisoning; temporary loss of
consciousness; academic failure; violence, unplanned pregnancy; sexually transmitted diseases, troubles with
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is used to apply ANN classifiers, support vector classifiers, random forests, and DT Compute and train a
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1 PHY 241 Fall 2018 PHY 241 Lab 7- Momentum is Conserved.docx
1
PHY 241 Fall 2018
PHY 241 Lab 7- Momentum is Conserved
Introduction:
Momentum is a vector quantity which is measured by taking the product of an objects mass and
velocity,
𝑝 = 𝑚�⃗�. (1)
Much like energy, the concept of momentum is useful because we have a law which guarantees that the
momentum of an appropriate system is conserved.
“The total amount of momentum in a system is a constant unless momentum is transferred
through the system boundary by an Impulse.”
Where an impulse is an external force which acts on a system over time,
𝐼 = ∫ 𝐹𝑒𝑥𝑡⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ 𝑑𝑡.
Equipment:
Two CBR 2- connected directly to a computer using USB cables
Various collision carts
Mass blocks for carts
2 m track
Bubble level
Computer with Logger Pro or Logger Lite and Excel.
Triple beam balance scale.
Procedure:
1) Design a procedure to collect the information you need to measure the momentum of two
carts simultaneously. WARNING: Occasionally, the clicks from your two different CBRs will
interfere with each other and give incorrect data. Your group should develop criteria to
determine when data is invalid and a response.
2) Generate a plot of the momentum of each cart as well as the total momentum similar to
“Carts’ Momenta.” Notice you must correct for the fact that the two different CBRs are
using different coordinate systems.
2
PHY 241 Fall 2018
3) Similarly, generate a plot of the kinetic energy of each cart as well as the total kinetic
energy.
4) This should allow you to make a single plot containing both the Kinetic Energy and the
Momenta for the same collision. Notice you will need to let Excel know that Energy needs
to be plotted on a “Secondary Axis” because these two quantities have different units.
1 1.2 1.4 1.6 1.8 2
E
n
e
rg
y
(
J)
M
o
m
e
n
tu
m
(
k
g
m
/s
)
Time (s)
Energy and Momentum
Total Momentum Total Kinetic Energy
1 1.2 1.4 1.6 1.8 2
M
o
m
e
n
tu
m
(
k
g
m
/s
)
Time (s)
Carts' Momenta
Cart 1 Cart 2 Total Momentum
1 1.2 1.4 1.6 1.8 2
E
n
e
rg
y
(
J)
Time (s)
Carts' Energies
Cart 1 Cart 2 Total Kinetic Energy
3
PHY 241 Fall 2018
5) At this point there are a few questions that that arise from the Energy and Momentum
graph above. To
A) DA- Is the behavior of the Energy and Momentum graph unique to the specific details of
the collision. Collect energy and momentum data for at least four different collisions
(magnet/spring/Velcro, different mass carts, etc.) and find a way to visualize all this data
so you can qualitatively compare and contrast features you see in the data.
B) Researcher- Choose a single trial to investigate momentum carefully. Is momentum
conserved? Measure the Impulse generated by force(s) on your system and see if you
can account for any changes in momentum you observed. Be as quantitative as
possible.
C) PI- Choose a single trial to investigate energy carefully. Energy appears to
Data Mining for Education. Ryan S.J.d. Baker, Carnegie Mellon University
Data Mining for Education
Ryan S.J.d. Baker, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
rsbaker@cmu.edu
Article to appear as
Baker, R.S.J.d. (in press) Data Mining for Education. To appear in McGaw, B., Peterson, P.,
Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.
This is a pre-print draft. Final article may involve minor changes and different formatting.
Student Performance Prediction via Data Mining & Machine Learning
https://www.irjet.net/archives/V9/i1/IRJET-V9I1192.pdf
Clustering Students of Computer in Terms of Level of Programming
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
[IJCT-V3I2P30] Authors: Sunny Sharma
There are numerous ways to analyse the web information, generally web substance are housed in
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approaches like decision trees, association rules, Meta heuristic and basic learning methods are embraced
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This project examines the effectiveness of applying machine learning techniques to the realm of college
student success, specifically with the intent of discovering and identifying those student characteristics and
factors that show the strongest predictive capability with regards to successful graduation. The student
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to determine the relative importance of the student characteristics with genetic algorithms to perform
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This project examines the effectiveness of applying machine learning techniques to the realm of college
student success, specifically with the intent of discovering and identifying those student characteristics and
factors that show the strongest predictive capability with regards to successful graduation. The student
data examined consists of first time freshmen and transfer students who matriculated at California State
University San Marcos in the period of Fall 2000 through Fall 2010 and who either graduated successfully
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feature selection and pruning. To improve the machine learning algorithm cross validated hyperparameter
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learning model are explored.
PREDICTING SUCCESS: AN APPLICATION OF DATA MINING TECHNIQUES TO STUDENT OUTCOMES
This project examines the effectiveness of applying machine learning techniques to the realm of college
student success, specifically with the intent of discovering and identifying those student characteristics and
factors that show the strongest predictive capability with regards to successful graduation. The student
data examined consists of first time freshmen and transfer students who matriculated at California State
University San Marcos in the period of Fall 2000 through Fall 2010 and who either graduated successfully
or discontinued their education. Operating on over 30,000 student observations, random forests are used
to determine the relative importance of the student characteristics with genetic algorithms to perform
feature selection and pruning. To improve the machine learning algorithm cross validated hyperparameter tuning was also implemented. Overall predictive strength is relatively high as measured by the
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Similar to SAS Macro practice (20)
Student Alcohol Consumption Prediction: Data Mining Approach
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Data mining approach to predict academic performance of students
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1 PHY 241 Fall 2018 PHY 241 Lab 7- Momentum is Conserved.docx
1 PHY 241 Fall 2018 PHY 241 Lab 7- Momentum is Conserved.docx
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Innovative Methods in Media and Communication Research by Sebastian Kubitschk...
Innovative Methods in Media and Communication Research
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The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈
1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】
2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】
3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。
如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。
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【本科硕士】密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单（GPA修改）；学历认证（教育部认证）；大学Offer录取通知书留信认证使馆认证；雅思语言证书等高仿类证书。
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真实网上可查的证明材料
1教育部学历学位认证留服官网真实存档可查永久存档。
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我们对海外大学及学院的毕业证成绩单所使用的材料尺寸大小防伪结构（包括：密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单隐形水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪）都有原版本文凭对照。质量得到了广大海外客户群体的认可同时和海外学校留学中介做到与时俱进及时掌握各大院校的（毕业证成绩单资格证结业证录取通知书在读证明等相关材料）的版本更新信息能够在第一时间掌握最新的海外学历文凭的样版尺寸大小纸张材质防伪技术等等并在第一时间收集到原版实物以求达到客户的需求。
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国外毕业证学位证成绩单办理流程：
1客户提供办理格罗宁根大学格罗宁根大学毕业证学历书信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作格罗宁根大学毕业证成绩单电子图；
3格罗宁根大学毕业证成绩单电子版做好以后发送给您确认；
4格罗宁根大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5格罗宁根大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快递邮寄格罗宁根大学格罗宁根大学毕业证学历书）。也得开灯开风扇山娃不想浪费电总将小方桌搁在门口看书写作业有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死受
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SAS Macro practice
- 1. Adult census income analysis Ping Yin 10/12/2016
- 3. Overview Purpose Exploring data Using Macro and Macro functions Build a full model Content Exploring data with Macro & Macro functions Using Proc Freq to create cross tabulation tables Using Proc Corr to investigate correlations Using Proc Logistic to build a full model Conclusion
- 8. 2. Using Proc Freq
- 9. 2. Using Proc Freq
- 10. 3. Using Proc Corr
- 14. 5.Conclusion P-value and Fit Statistics indicate this full model is significant Most of variables can be used in the model People’s income level is related with gender, age, education, etc. To fit the model better, some issues need be addressed, including missing value, categorical variables, etc.
- 15. Thanks!