How to use SPSS (Statistical Package for Social Science) data. This software program is extensively used for Social Science data analysis. However it is also used by managers, scholars and Engineers also. In this document how to use SPSS for data analysis is explained step by step.
How to use SPSS (Statistical Package for Social Science) data. This software program is extensively used for Social Science data analysis. However it is also used by managers, scholars and Engineers also. In this document how to use SPSS for data analysis is explained step by step.
Data Mining Techniques using WEKA (Ankit Pandey-10BM60012)Ankit Pandey
This term paper contains a brief introduction of a powerful data mining tool WEKA along with a hands-on guide to two data mining techniques namely Clustering (k-means) and Linear Regression using WEKA.
Talk about Apache Nutch on ApacheCon Europe 2014:
http://sched.co/1nyYa7b
http://events.linuxfoundation.org/sites/events/files/slides/aceu2014-snagel-web-crawling-nutch.pdf
Apache Avro and Messaging at Scale in LivePersonLivePerson
This talk covers the challenges we tackled during building our new service oriented system. Summarizing what we realized would bad Ideas to do, what are the better approaches to data consistency, how we used Apache Avro technology and what other supporting infrastructure we created to help us achieving the goal of consistent yet flexible system.
Amihay Zer-Kavod is I'm a Senior Software Architect at LivePerson.
WEKA - A Data Mining Tool - by Shareek AhamedShareek Ahamed
WEKA project was initiated in early 1992 by the New Zealand Government, and more than 21 years have elapsed since the first public release of WEKA. Along that period of time, the software has been rewritten entirely from scratch once. Initially it was written in C programming language, and along the time it became a burden and they have moved to Java. These days, WEKA enjoys its acceptance in both academic and business levels because, WEKA is an open source project and it has an active community.
Comparing Colleges on basis of various attributes and doing regression using Weka Software
Demonstration of Clustering using Weka on various attributes on data set of places.
Data Mining Techniques using WEKA (Ankit Pandey-10BM60012)Ankit Pandey
This term paper contains a brief introduction of a powerful data mining tool WEKA along with a hands-on guide to two data mining techniques namely Clustering (k-means) and Linear Regression using WEKA.
Talk about Apache Nutch on ApacheCon Europe 2014:
http://sched.co/1nyYa7b
http://events.linuxfoundation.org/sites/events/files/slides/aceu2014-snagel-web-crawling-nutch.pdf
Apache Avro and Messaging at Scale in LivePersonLivePerson
This talk covers the challenges we tackled during building our new service oriented system. Summarizing what we realized would bad Ideas to do, what are the better approaches to data consistency, how we used Apache Avro technology and what other supporting infrastructure we created to help us achieving the goal of consistent yet flexible system.
Amihay Zer-Kavod is I'm a Senior Software Architect at LivePerson.
WEKA - A Data Mining Tool - by Shareek AhamedShareek Ahamed
WEKA project was initiated in early 1992 by the New Zealand Government, and more than 21 years have elapsed since the first public release of WEKA. Along that period of time, the software has been rewritten entirely from scratch once. Initially it was written in C programming language, and along the time it became a burden and they have moved to Java. These days, WEKA enjoys its acceptance in both academic and business levels because, WEKA is an open source project and it has an active community.
Comparing Colleges on basis of various attributes and doing regression using Weka Software
Demonstration of Clustering using Weka on various attributes on data set of places.
SPSS is widely used program for statistical analysis in social sciences, particularly in education and research. However, because of its potential, it is also widely used by market researchers, health-care researchers, survey organizations, governments and, most notably, data miners and big data professionals.
Week 2 Project - STAT 3001Student Name Type your name here.docxcockekeshia
Week 2 Project - STAT 3001
Student Name: <Type your name here>
Date: <Enter the date on which you began working on this assignment.>
Instructions: To complete this project, you will need the following materials:
· STATDISK User Manual (found in the classroom in DocSharing)
· Access to the Internet to download the STATDISK program.
This assignment is worth a total of 60 points.
Part I. Histograms and Frequency Tables
Instructions
Answers
1. Open the file Diamonds using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about diamonds. What are the names of the variables in this file?
2. Create a histogram for the depth of the diamonds using the Auto-fit option. Paste the chart here. Once your histogram displays, click Turn on Labels to get the height of the bars.
3. Using the information in the above histogram, complete this table. Be sure to include frequency, relative frequency, and cumulative frequency.
Depth
Frequency
Relative Frequency
Cumulative Frequency
57-58.9
59-60.9
61-62.9
63-64.9
a. Using the frequency table above, how many of the diamonds have a depth of 60.9 or less? How do you know?
b. Using the frequency table above, how many of the diamonds have a depth between 59 and 62.9? Show your work.
c. What percent of the diamonds have a depth of 61 or more?
Part II. Comparing Datasets
Instructions
Answers
1. Create a boxplot that compares the color and clarity of the diamonds. Paste it here.
2. Describe the similarities and differences in the data sets. Please be specific to the graph created.
Part III. Finding Descriptive Numbers
Instructions
Answers
3. Open the file named Stowaway (using Datasets and then Elementary Stats, 9th Edition). This gives information on the number of stowaways going west vs east.List all the variables in the dataset.
4. Find the Mean, median, and midrange for the Data in Column 1.
5. Find the Range, variance, and standard deviation for the first column.
6. List any values for the first column that you think may be outliers. Why do you think that?
[Hint: You may want to sort the data and look at the smallest and largest values.]
7. Find the Mean, median, and midrange for the data in Column 2.
8. Find the Range, variance, and standard deviation for the data in Column 2.
9. List any values for the second column that you think may be outliers. Why do you think that?
10. Find the five-number summary for the stowaways data in Columns 1 and 2. You will need to label each of the columns with an appropriate measure in the top row for clarity.
11. Compare number of stowaways going west and east using a boxplot of Columns 1 and 2. Paste your boxplot here
12. Create a histogram for the
Column 1 data and paste it here.
13. Create a histogram for the
Column 2 data and paste it here.
Part IV. Interpreting Statistical Information
The Stowaway data contains two columns, both of which are mea.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
2. WEKA
Data mining isn't solely the domain of big companies and expensive software. In fact, there's a
piece of software that does almost all the same things as these expensive pieces of software —
the software is called WEKA. WEKA is the product of the University of Waikato (New Zealand)
and was first implemented in its modern form in 1997. It uses the GNU General Public License
(GPL). The software is written in the Java™ language and contains a GUI for interacting with
data files and producing visual results (think tables and curves). It's Java-based, so if we don't
have a JRE installed on your computer, download the WEKA version that contains the JRE, as
well. To load data into WEKA, we have to put it into a format that will be understood. WEKA's
preferred method for loading data is in the Attribute-Relation File Format (ARFF), where we can
define the type of data being loaded, then supply the data itself.
When we start WEKA, the GUI chooser
pops up as shown in figure
It lets us choose four ways to work with
WEKA and our data. The four ways are
Explorer
Experimenter
Knowledge Flow
Simple CLI
REGRESSION
Regression is the easiest technique to use, but is also probably the least powerful. In effect,
regression models all fit the same general pattern. There are a number of independent
variables, which, when taken together, produce a result — a dependent variable. The
regression model is then used to predict the result of an unknown dependent variable, given
the values of the independent variables.
We will perform Regression on the pricing of the house. The price of the house (the dependent
variable) is the result of many independent variables — the square footage of the house, the
size of the lot, whether granite is in the kitchen, bathrooms are upgraded, etc.
To create our regression model, start WEKA, then choose the Explorer. In the Explorer screen,
select the Preprocess tab. Select the Open File button and select the ARFF file. After selecting the
file the explorer window looks as below
3. In the left section of the Explorer window, it outlines all of the columns in the data (Attributes)
and the number of rows of data supplied (Instances). By selecting each column, the right
section of the Explorer window will also give the information about the data in that column of
the data set. For example, by selecting the houseSize column in the left section the right-
section should change to show the additional statistical information about the column. Finally,
there's a visual way of examining the data, which can be viewed by clicking the Visualize
All button.
To create the model, click on the Classify tab. The first step is to select the model we want to
build, so WEKA knows how to work with the data, and how to create the appropriate model:
1. Click the Choose button, then expand the functions branch.
2. Select the LinearRegression leaf.
This tells WEKA that we want to build a regression model. When we have selected the right
model, your WEKA Explorer should look as below
4. Though it may be obvious to us that we want to use the data we supplied in the ARFF file, there
are actually different options than what we'll be using. The other three choices are Supplied
test set, where we can supply a different set of data to build the model; Cross-validation, which
lets WEKA build a model based on subsets of the supplied data and then average them out to
create a final model; and Percentage split, where WEKA takes a percentile subset of the
supplied data to build a final model. With regression, we can simply choose Use training set.
Finally, the last step to creating our model is to choose the dependent variable (the column we
are looking to predict). We know this should be the selling price, since that's what we're trying
to determine for my house. Right below the test options, there's a combo box that lets you
choose the dependent variable. The column SellingPrice should be selected by default. If it's
not, please select it. To create our model, click Start. Figure below shows the output window
INTERPRETATION OF THE RESULT:
SellingPrice = (-26.6882 * houseSize) + (7.0551 * lotSize) + (43166.0767 * bedrooms) +
(42292.0901 * bathroom) - 21661.1208
Interpreting the pattern and conclusion that our model generated we see that besides just a
strict house value:
Granite doesn't matter — WEKA will only use columns that statistically contribute to
the accuracy of the model. It will throw out and ignore columns that don't help in
creating a good model. So this regression model is telling us that granite in your kitchen
doesn't affect the house's value.
5. Bathrooms do matter — Since we use a simple 0 or 1 value for an upgraded bathroom,
we can use the coefficient from the regression model to determine the value of an
upgraded bathroom on the house value.
Bigger houses reduce the value — WEKA is telling us that the bigger our house is, the
lower the selling price. This can be seen by the negative coefficient in front of
the houseSize variable. The model is telling us that every additional square foot of the
house reduces its price by $26.
CLUSTERING
Clustering is the task of assigning a set of objects into groups (called clusters) so that the
objects in the same cluster are more similar (in some sense or another) to each other than to
those in other clusters. WEKA offers clustering capabilities not only as standalone schemes, but
also as filters and classifiers.
To begin with clustering we will use the data set of bank. The data set contains – id, age, sex,
region, income, married, children, car, save_acct, current_acct, mortgage and pep.
To create clustering, start WEKA, then choose the Explorer. In the Explorer screen, select
the Preprocess tab. Select the Open File button and select the ARFF file. After selecting the file
the explorer window looks as below
To perform clustering, select the "Cluster" tab in the Explorer and click on the "Choose" button.
This results in a drop down list of available clustering algorithms. In this case select
6. "SimpleKMeans". Next, click on the text box to the right of the "Choose" button to get the pop-
up window shown in Figure below, for editing the clustering parameter.
In the pop-up window we enter 6 as the number of clusters (instead of the default values of 2)
and we leave the value of "seed" as is. The seed value is used in generating a random number
which is, in turn, used for making the initial assignment of instances to clusters.
Once the options have been specified, we can run the clustering algorithm. Here we make sure
that in the "Cluster Mode" panel, the "Use training set" option is selected, and we click "Start".
We can right click the result set in the "Result list" panel and view the results of clustering in a
separate window.
7. We can choose the cluster number and any of the other attributes for each of the three
different dimensions available (x-axis, y-axis, and color). Different combinations of choices will
result in a visual rendering of different relationships within each cluster. Here we have chosen
the cluster number as the x-axis, the instance number as the y-axis, and the "sex" attribute as
the color dimension. This will result in a visualization of the distribution of males and females in
each cluster. For instance, here clusters 2 and 3 are dominated by males, while clusters 4 and 5
are dominated by females.
8. INTERPRETING THE RESULT:
Each cluster shows us a type of behavior in our customers, from which we can begin to draw
some conclusions:
Cluster 0 – It contains a cluster of Females with an average age of 37 who live in inner city and possess
saving account number and current account number. They are unmarried and donot have any mortgage
or pep. The average monthly income is 23,300.
Cluster 1 - It contains a cluster of Females with an average age of 44 who live in rural area and possess
saving account number and current account number. They are married and donot have any mortgage or
pep. The average monthly income is 27,772.
Cluster 2 - It contains a cluster of Females with an average age of 48 who live in inner city and possess
current account number but no saving account number. They are unmarried and donot have mortgage
but do have pep. The average monthly income is 27,668.
Cluster 3 - It contains a cluster of Females with an average age of 39 who live in town and possess saving
account number and current account number. They are married and donot have any mortgage or pep.
The average monthly income is 24,047.
Cluster 4 - It contains a cluster of Males with an average age of 39 who live in inner city and possess
current account number but no saving account number. They are married and have mortgage and pep.
The average monthly income is 26,359.
Cluster 5 - It contains a cluster of Males with an average age of 47 who live in inner city and possess
saving account number and current account number. They are unmarried and donot have mortgage but
do have pep. The average monthly income is 35,419.