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.
SPSS for beginners, a short course about how novices can use SPSS to analyze their research findings. With this tutorial anyone becomes able to use SPSS for basic statistical analysis. No need to be a professional to use SPSS.
Learn how to navigate Stata’s graphical user interface, create log files, and import data from a variety of software packages. Includes tips for getting started with Stata including the creation and organization of do-files, examining descriptive statistics, and managing data and value labels. This workshop is designed for individuals who have little or no experience using Stata software.
Full workshop materials including example data sets and .do file are available at http://projects.iq.harvard.edu/rtc/event/introduction-stata
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.
SPSS for beginners, a short course about how novices can use SPSS to analyze their research findings. With this tutorial anyone becomes able to use SPSS for basic statistical analysis. No need to be a professional to use SPSS.
Learn how to navigate Stata’s graphical user interface, create log files, and import data from a variety of software packages. Includes tips for getting started with Stata including the creation and organization of do-files, examining descriptive statistics, and managing data and value labels. This workshop is designed for individuals who have little or no experience using Stata software.
Full workshop materials including example data sets and .do file are available at http://projects.iq.harvard.edu/rtc/event/introduction-stata
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
SPSS stands for Statistical package of sports sciences, it is a software package used for statistical analysis of data in field of education, physical education, medical, market etc. researches.
Aside from statistical analysis the software also feature data management which allow the user to create the variable, case selection, create a data drive and save it for further analysis when needed.
SPSS is beneficial for both qualitative and quantitative data equal importance has been given to both data set, SPSS provide graphical representation and also an appropriate result for data entered.
SPSS allow you to analysis the data using different kind of tests like t-test, z-test, further you can use ANOVA, MANOVA etc. for further analysis of result.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
SPSS stands for Statistical package of sports sciences, it is a software package used for statistical analysis of data in field of education, physical education, medical, market etc. researches.
Aside from statistical analysis the software also feature data management which allow the user to create the variable, case selection, create a data drive and save it for further analysis when needed.
SPSS is beneficial for both qualitative and quantitative data equal importance has been given to both data set, SPSS provide graphical representation and also an appropriate result for data entered.
SPSS allow you to analysis the data using different kind of tests like t-test, z-test, further you can use ANOVA, MANOVA etc. for further analysis of result.
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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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).
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.
1. Data Entry in Excel and
SPSS + Basic Data
manipulation
Dhritiman Chakrabarti
Assistant Professor,
Dept of Neuroanaesthesiology
and Neurocritical Care,
NIMHANS, Bangalore
2. SPSS Windows has 3 windows:
Data Editor
Syntax Editor, which displays syntax files
Viewer or Draft Viewer which displays the output files
The Data Editor has two parts:
Data View window, which displays data from the active file in
spreadsheet format
Variable View window, which displays metadata or information
about the data in the active file, such as variable names and labels,
value labels, formats, and missing value indicators.
5. 1.2 Data Entry into SPSS
There are 2 ways to enter data into SPSS:
1. Directly enter in to SPSS by typing in Data View
2. Enter into other database software such as
Excel then import into SPSS
Let’s start with the second option, using data in Excel.
8. 8
1. Give each variable a valid name (8 characters or less with no spaces or
punctuation, beginning with a letter not a numeric number). Short, easy to
remember word names.
Avoid the following variable names: TEST, ALL, BY, EQ, GE, GT, LE, LT,
NE, NOT, OR, TO, WITH. These are used in the SPSS syntax and if they
were permitted, the software would not be able to distinguish between a
command and a variable.
Each variable name must be unique; duplication is not allowed.
Variable names are not case sensitive. The names NEWVAR, NewVar, and
newvar are all considered identical.
First row is always Variable name. Second row onwards always contains
the patient/sample.
General guidelines for data entry into Excel
2. Encode categorical variables - Convert letters and words to numbers.
3. Avoid mixing symbols with data. Convert them to numbers.
4. Give each patient a unique, sequential case number (ID). Place this ID
number in the first column on the left
9. 9
5. Each variable should be in its own column.
Avoid this:
Animal
Control1
Control2
Experiment1
Experiment2
Change to:
Animal Group
1 0
2 0
3 1
4 1
* It is recommended to use 0/1 for 2 groups with 0 as a reference group.
* Do not combine variables in one column
6. All data for a project should be in one spreadsheet.
7. Do not include graphs or summary statistics in the spreadsheet – Only
raw data.
10. 10
8. However when data are repeatedly collected over a patient, it’s
recommended to have patient-day observation on a simple line to ease data
management. SPSS has a nice feature to convert from the longitudinal
format (Long format) to horizontal format (Wide format) – Data
Restructuring. When the number of repeats are few 2 or 3, horizontal
format may be preferred for simplicity.
Date ID SYSBP
1/2/2005 1 130
1/3/2005 1 120
1/4/2005 1 120
3/1/2005 2 110
3/2/2005 2 140
Longitudinal data entry
ID SYSBP1 SYSBP2 SYSBP3
1 130 120 120
2 110 140
Horizontal data entry
7. Each patient should be entered on a single line or row.
9. For yes/no questions, enter “0” for no and “1” for yes. Do not leave
blanks for no. Do not enter “?”, “*”, or “NA” for missing data because this
indicates to the statistical program than the variable is a string variable.
String variables cannot be used for any arithmetic computation.
11. 11
Entering Date in Excel.
In Excel,go to:
Format, Cells, select Date under Category,
Choose Type for a format you like
12. 12
Entering Time in Excel.
In Excel, go to:
Format, Cells, select Time under Category,
Choose Type for a format you like
13. 13
Entering Date / Time in Excel.
In Excel, go to:
Format, Cells, select Time under Category,
Choose Data/Time format
14. 14
Entering Date, Time in SPSS
In SPSS, open Variable View, Click Type for the variable you want to
Assign date format, click on Date, and select a format of your choice.
15. 15
Importing data from Excel spreadsheet into SPSS.
In SPSS, go to:
File, Open, Data
Select Type of file (for example, Excel) you want to open
Select File name you want to open
16. Data Cleaning in SPSS
1. Re-coding existing variables
2. Creating new variables
3. Creating new variable from existing variables
4. Data labeling and formatting
17. 17
Data cleaning in SPSS (1): Recoding existing variables (1)
Old New
ID Group Group
1 A 0
2 A 0
3 B 1
4 B 1
We want to use numeric coding for group instead of A and B.
18. 18
Data cleaning in SPSS (2): Recoding existing variables (2)
From SPSS dialog box, go to:
Transform
Recode
Into Same variables (better to recode into different variable)
19. 19
1. Select Group from the variable box into String Variables box
2. Click on Old and new Values to proceed
Data cleaning in SPSS (1): Recoding existing variables (3)
20. 20
1. Type the old value and the new value you want to convert into
2. Click on Add (To remove, or change, click on Change or Remove)
3. Type all values in the Old New box, then click Continue
4. Click OK to execute the commands.
Data cleaning in SPSS (1): Recoding existing variables (4)
21. 21
Data Cleaning in SPSS (2)
Creating a new variable for Diastolic blood pressure (DiasBP):
In SPSS, go to Variable View,
Then type DiasBP at the last row under
Name
Go back to Data View and directly type diastolic blood pressure to separate
from SysBP.
22. Data Cleaning in SPSS (3)
Computing New variable from Existing variable(s)
Transform Compute Variable this window (Computing MAPB from SBPB
And DBPB