A step by step guide to recoding AGE variables into generational groups in SPSS. Screenshots of every step is provided in an easy to follow tutorial of how to change or transform a list of ages into generational categories in SPSS
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This presentation explains the different options involves in the variable view interface of IBM SPSS Statistics. This slide also describes the different types of data and their meaning in a clear manner.
E-commerce giants design and run frequent campaigns on their touchpoints which also includes websites to attract more and more customers. The purpose of this paper is to investigate the effectiveness of a newly launched web page for consumers and find out if the new page is resulting in different consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’ helps us find out if the different user groups of old and new web page are significantly different from each other based on conversion rate or not!
A step by step guide to recoding AGE variables into generational groups in SPSS. Screenshots of every step is provided in an easy to follow tutorial of how to change or transform a list of ages into generational categories in SPSS
We would like to introduce sampling software which costs just 10 USD. Sampling is statistical software designed to calculate sampling computation easily such as stratified sampling, cluster sampling, sampling with varying probability and etc. You can download free 7 times running trial license here:
http://www.sampling-software.com
This presentation explains the different options involves in the variable view interface of IBM SPSS Statistics. This slide also describes the different types of data and their meaning in a clear manner.
E-commerce giants design and run frequent campaigns on their touchpoints which also includes websites to attract more and more customers. The purpose of this paper is to investigate the effectiveness of a newly launched web page for consumers and find out if the new page is resulting in different consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’ helps us find out if the different user groups of old and new web page are significantly different from each other based on conversion rate or not!
My name is Mathew Olson. I am associated with statisticsassignmenthelp.com for the past 5 years and have been helping statistics students with their T-Test and ANOVA using SPSS Assignments. I have a Master’s in Statistics, from Florida State University.
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DirectionsSet up your IBM SPSS account and run several statisti.docxjakeomoore75037
Directions:
Set up your IBM SPSS account and run several statistical outputs based on the "SPSS Database" Use "Setting Up My SPSS" to set up your SPSS program on your computer or device. You may also use programs such as Laerd Statistics or Intellectus, if you subscribe to them.
The patient outcome or dependent variables and the level of measurement must be displayed in a comparison table which you will provide as an Appendix to the paper. Refer to the "Comparison Table of the Variable's Level of Measurement."
Submit a 1,000-1,250 word data analysis paper outlining the procedures used to analyze the parametric and non-parametric variables in the mock data, the statistics reported, and a conclusion of the results.
Provide a conclusive result of the data analyses based on the guidelines below for statistical significance.
PAIRED SAMPLE T-TEST: Identify the variables BaselineWeight and InterventionWeight. Using the Analysis menu in SPSS, go to Compare Means, Go to the Paired Sample t-test. Add the BaselineWeight and InterventionWeight in the Pair 1 fields. Click OK. Report the mean weights, standard deviations, t-statistic, degrees of freedom, and p level. Report as t(df)=value, p = value. Report the p level out three digits.
INDEPENDENT SAMPLE T-TEST: Identify the variables InterventionGroups and PatientWeight. Go to the Analysis Menu, go to Compare Means, Go to Independent Samples tT-test. Add InterventionGroups to the Grouping Factor. Define the groups according to codings in the variable view (1=Intervention, 2 =Baseline). Add PatientWeight to the test variable field. Click OK. Report the mean weights, standard deviations, t-statistic, degrees of freedom, and p level. Report t(df)=value, p = value. Report the p level out three digits
CHI-SQUARE (Independent): Identify the variables BaselineReadmission and InterventionReadmission. Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add BaselineReadmission to the row and InterventionReadmission to the column. Click the Statistics button and choose Chi-Square. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the total events, the chi-square statistic, degrees of freedom, and p level. Report ꭓ2 (df) =value, p =value. Report the p level out three digits.
MCNEMAR (Paired): Identify the variables BaselineCompliance and InterventionCompliance. Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add BaselineCompliance to the row and InterventionCompliance to the column. Click the Statistics button and choose Chi-Square and McNemars. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the events, the Chi-square, and the McNemar’s p level. Report (p =value). Report the p level out three digits.
MANN WHITNEY U: Identify the variables InterventionGroups and PatientSatisfaction. Using the Analysis Menu, go to Non-parametric Statistics, go to LegacyDialogs, go to 2 I.
Statistical Package for Social Science (SPSS)sspink
This presentation includes the introduction of SPSS is basic features of Spss, how to input data manually, descriptive statistics and how to perform t-test, Anova and Chi-Square.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
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).
5. Step 2
Click on the variable
that has frequencies
and drag it to
Frequency Variable box
then Click Ok to run
the process.
6. Step 3
Click on Analyze tab of
Menu bar, click
Nonparametric Tests,
then Legacy dialogs
and select Chi square
to run the Goodness of
fit test
7. Step 4
In this dialog box you
select variable for
which you want to run
Goodness of Fit Test.
Select the Variable and
drag it in the Test
Variable list box.
8. Step 5
Select “values” in the
expected value box
and add expected
values by manually
typing and clicking on
Add button.
Info: Our expected value is given
in the “Exp_Num_Pref” variable.
We need to manually enter the
values
9. Step 6
Click on Options
button to select
statistics to calculate.
13. Goodness of Fit Test
This table displays Pearson
chi square statistics with
it’s associated degree of
freedom and significance
value. It tests the
hypothesis that the
distribution of cases in a
single categorical variable
follows a known or
hypothesized distribution
14. Developed and designed by:
Address: Mau - Chitrakoot
Contact: unexplordsolutions@gmail.com