At the end of this Lesson (Part 1) the students should be able to know the following
Descriptive statistics
Saving an SPSS for Windows
Backing up your data
Retrieving your Data Files
At the end of this Lesson (Part 1) the students should be able to know the following
Introduction
Data Entry
Variable and Value Label
Entering Data
File management
Descriptive statistics
Editing and modifying the data
SPSS is a popular statistical analysis software that is known for its ease of use. It has strong graphical capabilities and supports a variety of statistical analyses. However, it lacks some more advanced statistical procedures and has limited data management tools. While suitable for many tasks, some users may outgrow it over time and require more specialized software like SAS or Stata for complex or cutting-edge analyses. Overall, SPSS is best suited for users performing basic to intermediate statistical analysis and reporting.
SPSS is a statistical software package used for entering and analyzing data. It has a menu interface and toolbars for navigating between different windows. Data can be entered manually by defining variables and values or imported from Excel. Various forms of help are available within SPSS. Common tasks involve defining variables, entering data, performing statistical analyses through the Analyze menu, and saving data worksheets and results.
SPSS is a statistical software package used for data management and analysis. It allows users to enter and manage large amounts of data, perform a wide range of statistical analyses, and output results in tables and graphs. The main SPSS windows are the Data Editor, used to enter and view data, and the Viewer, which displays output of statistical analyses. Common analysis techniques demonstrated in the document include independent and paired t-tests to compare group means. The document provides guidance on using SPSS for questionnaire design and statistical analysis to efficiently analyze social science and business data.
This document provides an introduction to the statistical software package SPSS. It describes what SPSS is, its history and capabilities. SPSS is a Windows-based program that can be used for data entry, management and analysis. It allows users to perform statistical tests, create tables and graphs, and handle large datasets. Originally developed in 1968 for social science research, SPSS is now owned by IBM and known as PASW. The document outlines SPSS' interface and main functions.
This document provides an overview of using SPSS (Statistical Package for the Social Sciences) software. It introduces the main interfaces for working with data in SPSS, including the data view, variable view, output view, draft view, and syntax view. It also provides instructions for installing sample data files and demonstrates how to generate a basic cross-tabulation output of employment by gender using the automated features.
A brief introduction for beginners. Topic included: background history of SPSS, some basics but effective data management techniques, frequency distribution, descriptive statistics, hypothesis testing rule, association test/ contingency table test. All these statistical topics are explained with easy hands on example with basic data-set. This slide also provide a short but effective understanding about p-value, which is very important for statistical decision making
This document provides an introduction to using SPSS (Statistical Package for the Social Sciences) for data analysis. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and sorting data. Several basic analysis techniques are introduced, such as frequencies, descriptives, and linear regression. Examples are provided for how to conduct these analyses and interpret the outputs.
At the end of this Lesson (Part 1) the students should be able to know the following
Introduction
Data Entry
Variable and Value Label
Entering Data
File management
Descriptive statistics
Editing and modifying the data
SPSS is a popular statistical analysis software that is known for its ease of use. It has strong graphical capabilities and supports a variety of statistical analyses. However, it lacks some more advanced statistical procedures and has limited data management tools. While suitable for many tasks, some users may outgrow it over time and require more specialized software like SAS or Stata for complex or cutting-edge analyses. Overall, SPSS is best suited for users performing basic to intermediate statistical analysis and reporting.
SPSS is a statistical software package used for entering and analyzing data. It has a menu interface and toolbars for navigating between different windows. Data can be entered manually by defining variables and values or imported from Excel. Various forms of help are available within SPSS. Common tasks involve defining variables, entering data, performing statistical analyses through the Analyze menu, and saving data worksheets and results.
SPSS is a statistical software package used for data management and analysis. It allows users to enter and manage large amounts of data, perform a wide range of statistical analyses, and output results in tables and graphs. The main SPSS windows are the Data Editor, used to enter and view data, and the Viewer, which displays output of statistical analyses. Common analysis techniques demonstrated in the document include independent and paired t-tests to compare group means. The document provides guidance on using SPSS for questionnaire design and statistical analysis to efficiently analyze social science and business data.
This document provides an introduction to the statistical software package SPSS. It describes what SPSS is, its history and capabilities. SPSS is a Windows-based program that can be used for data entry, management and analysis. It allows users to perform statistical tests, create tables and graphs, and handle large datasets. Originally developed in 1968 for social science research, SPSS is now owned by IBM and known as PASW. The document outlines SPSS' interface and main functions.
This document provides an overview of using SPSS (Statistical Package for the Social Sciences) software. It introduces the main interfaces for working with data in SPSS, including the data view, variable view, output view, draft view, and syntax view. It also provides instructions for installing sample data files and demonstrates how to generate a basic cross-tabulation output of employment by gender using the automated features.
A brief introduction for beginners. Topic included: background history of SPSS, some basics but effective data management techniques, frequency distribution, descriptive statistics, hypothesis testing rule, association test/ contingency table test. All these statistical topics are explained with easy hands on example with basic data-set. This slide also provide a short but effective understanding about p-value, which is very important for statistical decision making
This document provides an introduction to using SPSS (Statistical Package for the Social Sciences) for data analysis. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and sorting data. Several basic analysis techniques are introduced, such as frequencies, descriptives, and linear regression. Examples are provided for how to conduct these analyses and interpret the outputs.
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses the history and ownership of SPSS, its use as a statistical analysis program, and an overview of its basic functions. Key features covered include opening and managing data files, descriptive statistics like frequencies and charts, data cleaning techniques for handling missing values, and methods for data manipulation such as recoding variables and creating new computed variables. The goal is to provide readers with foundational knowledge on using SPSS for statistical analysis in the social sciences.
SPSS is statistical software used for statistical analysis, data manipulation, and generating tables and graphs summarizing data. It performs basic descriptive statistics as well as advanced statistical techniques like regression, ANOVA, and factor analysis. SPSS allows importing and exporting data from different sources and connecting to online databases. It is robust, but training is typically required to fully utilize its features. SPSS is widely used in research and development, quality control, compliance and validation, clinical trials, and data mining in the pharmaceutical industry.
SPSS is a statistical software package used for data management and analysis. It can import data from various file formats, perform complex statistical analyses and generate reports, tables, and graphs. Some key features include an easy to use interface, robust statistical procedures, and the ability to work with different operating systems. While powerful and popular, SPSS is also expensive and less flexible than open-source alternatives like R for advanced or custom analyses.
The document discusses statistical analysis packages including SPSS. It provides an overview of SPSS, describing it as a statistical analysis and data management software package that can perform various analyses and generate reports. The document outlines some key features of SPSS, such as its ease of use, data management and editing tools, and statistical and visualization capabilities. It also briefly describes the different windows in SPSS and how to define and manipulate data.
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses opening and closing SPSS, the structure and windows of SPSS including the Data View and Variable View windows for entering data. It defines key concepts in SPSS like variables, different types of variables (nominal, ordinal, interval, ratio), and the process of defining variables in the Variable View window by specifying name, type, width, labels, values etc. before entering data. Examples are given around designing an experiment with independent and dependent variables and dealing with extraneous variables.
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 is a statistical software package used for analyzing data. It was developed in 1968 at Stanford University. SPSS stands for Statistical Package for the Social Sciences. The document discusses the types of variables in SPSS including qualitative (string) and quantitative (numeric) variables. It also covers defining variables such as variable name, type, width and labels to describe the values. Proper coding and labeling helps facilitate analysis and interpretation of results.
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.
The document provides an introduction to the statistical software SPSS. It discusses that SPSS was originally developed in 1965 at Stanford University for social sciences. It is now widely used in health sciences and marketing as well. It describes the core functions of SPSS including statistics, modeling, text analytics, and visualization programs. It also outlines how to set up a data file in SPSS by defining variables, entering and editing data, and saving files.
Software packages for statistical analysis - SPSSANAND BALAJI
This document provides an overview of the Statistical Package for Social Sciences (SPSS). It discusses what SPSS is, how to define and enter variables, and the four main windows in SPSS including the data editor, output viewer, syntax editor, and script window. Basic functions like frequencies analysis, descriptives, and linear regression are also introduced.
Basics of SPSS & how to use the SPSS software.
with Diagram.
Used to Analyze the complex questions.
MAkes questionnaire for your Research.
SPSS like excel solves problems and questions in a few seconds.
SPSS is statistical software used by researchers to perform statistical analysis. It was first released in 1968 as the Statistical Package for the Social Sciences. SPSS is now owned by IBM and allows users to manage and analyze data, perform statistical tests, and produce graphs and reports. Researchers use SPSS to clean, code, and enter data, choose appropriate statistical tests to analyze the data, and interpret the results.
SPSS is a statistical software package used for data analysis in business research that was originally developed for social science applications. It allows users to import, organize, and analyze data using a variety of statistical procedures to generate reports and visualizations. SPSS has evolved over time from mainframe usage to its current version as a product of IBM after being acquired from SPSS Inc. in 2009.
Introduction To Spss - Opening Data File and Descriptive AnalysisDr Ali Yusob Md Zain
This document provides instructions for opening an SPSS data file and performing descriptive analysis. It outlines the steps to open a data file from the File menu, select the file, and press Open. It then describes how to perform frequencies analysis by selecting variables, clicking Continue, and OK to output a frequencies table showing counts and percentages.
This document provides an introduction and overview of SPSS (Statistical Package for the Social Sciences). It discusses what SPSS is, the research process it supports, how questionnaires are translated into SPSS, different question and response formats, and levels of measurement. It also briefly outlines some of SPSS's data editing, analysis, and output features.
Here are the steps to merge the data files:
1. Open the file Student_1 containing information on the first 5 students.
2. Go to Data > Merge Files > Add Cases.
3. Select the file Student_2 containing information on the additional 3 students.
4. The cases from Student_1 will be merged with the cases from Student_2.
5. Save the merged file as Student.
6. Open the file Student.
7. Go to Data > Merge Files > Add Variables.
8. Select the file Student_3 containing the Marks variable.
9. The variables from Student_3 will be added to the cases in Student.
In this presentation various fundamental data analysis using Statistical Tool SPSS was elaborated with special reference to physical education and sports
This document provides instructions for entering data into SPSS from Excel, cleaning data in SPSS, and formatting variables. It discusses:
1. The three main windows in SPSS and how to enter data directly or import from Excel.
2. Guidelines for structuring data in Excel for easy import into SPSS, including naming variables, encoding categories, including IDs, and placing each variable in its own column.
3. Methods for cleaning data in SPSS, such as recoding variables, creating new variables, computing variables from existing ones, and labeling and formatting variables.
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.
This document provides an overview of using the statistical software package SPSS. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and saving data. Finally, it demonstrates some common analyses in SPSS including frequencies, descriptives, and linear regression as well as how to interpret the outputs and plot regression lines. The overall purpose is to introduce the basics of using SPSS to perform statistical analysis and data management.
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses the history and ownership of SPSS, its use as a statistical analysis program, and an overview of its basic functions. Key features covered include opening and managing data files, descriptive statistics like frequencies and charts, data cleaning techniques for handling missing values, and methods for data manipulation such as recoding variables and creating new computed variables. The goal is to provide readers with foundational knowledge on using SPSS for statistical analysis in the social sciences.
SPSS is statistical software used for statistical analysis, data manipulation, and generating tables and graphs summarizing data. It performs basic descriptive statistics as well as advanced statistical techniques like regression, ANOVA, and factor analysis. SPSS allows importing and exporting data from different sources and connecting to online databases. It is robust, but training is typically required to fully utilize its features. SPSS is widely used in research and development, quality control, compliance and validation, clinical trials, and data mining in the pharmaceutical industry.
SPSS is a statistical software package used for data management and analysis. It can import data from various file formats, perform complex statistical analyses and generate reports, tables, and graphs. Some key features include an easy to use interface, robust statistical procedures, and the ability to work with different operating systems. While powerful and popular, SPSS is also expensive and less flexible than open-source alternatives like R for advanced or custom analyses.
The document discusses statistical analysis packages including SPSS. It provides an overview of SPSS, describing it as a statistical analysis and data management software package that can perform various analyses and generate reports. The document outlines some key features of SPSS, such as its ease of use, data management and editing tools, and statistical and visualization capabilities. It also briefly describes the different windows in SPSS and how to define and manipulate data.
This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses opening and closing SPSS, the structure and windows of SPSS including the Data View and Variable View windows for entering data. It defines key concepts in SPSS like variables, different types of variables (nominal, ordinal, interval, ratio), and the process of defining variables in the Variable View window by specifying name, type, width, labels, values etc. before entering data. Examples are given around designing an experiment with independent and dependent variables and dealing with extraneous variables.
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 is a statistical software package used for analyzing data. It was developed in 1968 at Stanford University. SPSS stands for Statistical Package for the Social Sciences. The document discusses the types of variables in SPSS including qualitative (string) and quantitative (numeric) variables. It also covers defining variables such as variable name, type, width and labels to describe the values. Proper coding and labeling helps facilitate analysis and interpretation of results.
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.
The document provides an introduction to the statistical software SPSS. It discusses that SPSS was originally developed in 1965 at Stanford University for social sciences. It is now widely used in health sciences and marketing as well. It describes the core functions of SPSS including statistics, modeling, text analytics, and visualization programs. It also outlines how to set up a data file in SPSS by defining variables, entering and editing data, and saving files.
Software packages for statistical analysis - SPSSANAND BALAJI
This document provides an overview of the Statistical Package for Social Sciences (SPSS). It discusses what SPSS is, how to define and enter variables, and the four main windows in SPSS including the data editor, output viewer, syntax editor, and script window. Basic functions like frequencies analysis, descriptives, and linear regression are also introduced.
Basics of SPSS & how to use the SPSS software.
with Diagram.
Used to Analyze the complex questions.
MAkes questionnaire for your Research.
SPSS like excel solves problems and questions in a few seconds.
SPSS is statistical software used by researchers to perform statistical analysis. It was first released in 1968 as the Statistical Package for the Social Sciences. SPSS is now owned by IBM and allows users to manage and analyze data, perform statistical tests, and produce graphs and reports. Researchers use SPSS to clean, code, and enter data, choose appropriate statistical tests to analyze the data, and interpret the results.
SPSS is a statistical software package used for data analysis in business research that was originally developed for social science applications. It allows users to import, organize, and analyze data using a variety of statistical procedures to generate reports and visualizations. SPSS has evolved over time from mainframe usage to its current version as a product of IBM after being acquired from SPSS Inc. in 2009.
Introduction To Spss - Opening Data File and Descriptive AnalysisDr Ali Yusob Md Zain
This document provides instructions for opening an SPSS data file and performing descriptive analysis. It outlines the steps to open a data file from the File menu, select the file, and press Open. It then describes how to perform frequencies analysis by selecting variables, clicking Continue, and OK to output a frequencies table showing counts and percentages.
This document provides an introduction and overview of SPSS (Statistical Package for the Social Sciences). It discusses what SPSS is, the research process it supports, how questionnaires are translated into SPSS, different question and response formats, and levels of measurement. It also briefly outlines some of SPSS's data editing, analysis, and output features.
Here are the steps to merge the data files:
1. Open the file Student_1 containing information on the first 5 students.
2. Go to Data > Merge Files > Add Cases.
3. Select the file Student_2 containing information on the additional 3 students.
4. The cases from Student_1 will be merged with the cases from Student_2.
5. Save the merged file as Student.
6. Open the file Student.
7. Go to Data > Merge Files > Add Variables.
8. Select the file Student_3 containing the Marks variable.
9. The variables from Student_3 will be added to the cases in Student.
In this presentation various fundamental data analysis using Statistical Tool SPSS was elaborated with special reference to physical education and sports
This document provides instructions for entering data into SPSS from Excel, cleaning data in SPSS, and formatting variables. It discusses:
1. The three main windows in SPSS and how to enter data directly or import from Excel.
2. Guidelines for structuring data in Excel for easy import into SPSS, including naming variables, encoding categories, including IDs, and placing each variable in its own column.
3. Methods for cleaning data in SPSS, such as recoding variables, creating new variables, computing variables from existing ones, and labeling and formatting variables.
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.
This document provides an overview of using the statistical software package SPSS. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and saving data. Finally, it demonstrates some common analyses in SPSS including frequencies, descriptives, and linear regression as well as how to interpret the outputs and plot regression lines. The overall purpose is to introduce the basics of using SPSS to perform statistical analysis and data management.
The document provides instructions for launching and using the statistical software SPSS. It discusses finding the SPSS icon on the computer and launching the program. Once SPSS is open, the user can start a new data file or open an existing one. Basic steps for using SPSS are outlined, including entering data, defining variables, testing for normality, statistical analysis, and interpreting results. Specific functions and menus in SPSS are demonstrated for descriptive statistics, normality testing, and t-tests.
This document introduces the software SPSS by describing its uses, how to open it, its basic layout and interface elements, and how to exit the program. Key points covered include that SPSS can perform statistical analyses and data management, it has menus and toolbars to access commands, and its main windows are the Data Editor and Output Viewer. The document provides instructions for navigating the menus and icons and becoming familiar with SPSS's interface.
Week 4 Project - STAT 3001Student Name Type your name here.docxmelbruce90096
Week 4 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.
Part I. Analyze Data
Instructions
Answers
1. Open the file COTININE using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about a collection of movies. How many observations are there in this file?
In this file, there are three variables, labeled Smokers, ETS, and No ETS. The dataset was collected for a study of second-hand smoke. The sample data consists of the measured serum cotinine levels in three different groups of people.
· The NOETS group lists the cotinine levels for subjects who are nonsmokers and have no exposure to environmental tobacco smoke at home or work.
· The ETS group lists cotinine levels for subjects who are nonsmokers exposed to tobacco smoke at home or work.
· The SMOKERS group lists cotinine levels for subjects who report tobacco use.
Serum cotinine is a metabolite of nicotine, meaning that cotinine is produced when nicotine is absorbed by the body. Higher levels of cotinine correspond to higher levels of exposure to smoke that contains nicotine.
2. What results do you expect to find in this data?
Part II. Descriptive Statistics
3. Generate descriptive statistics for all three groups of people and complete the following table. Round all results to 2 decimal places.
Variable
Sample
Mean
Sample
Standard Deviation
Sample Size
4.
Smokers
5.
ETS
6.
No ETS
7. Did you get the results you expected here? Explain why.
8. In which of the three groups did we experience the MOST variation (highest deviation from the mean)?
Part III. Confidence Intervals
9. Generate a 95% interval for the mean of the SMOKERS group. Paste your results here.
10. Generate a 95% interval for the mean of the ETS group. Paste your results here.
11. Generate a 95% interval for the mean of the No ETS group. Paste your results here
12. Create a graph below by illustrating all three confidence intervals on one graph using the tools in your word processor (example below). Statdisk cannot do this for you. Creat your graph and turn the font red. For this process, I just use the dashes and wrote a scale below the axes.
Here is an example, but it is not based on the data you are analyzing:
Case 1 14---------------------42
Case 2 35-------------------------70
________________________________________
0 20 40 60
Your
Solution
:
11. Based on the confidence intervals shown above, does there appear to be some evidence to indicate that exposure to tobacco smoke corresponds to higher levels of cotinin.
This document provides an introduction and overview of statistical analysis using PASW Statistics software (SPSS). It discusses the need for training in research design and statistics. It then covers the agenda which includes understanding quantitative research, the role of PASW, and a hands-on demonstration of the PASW interface, including how to input raw data, analyze the data, and view results.
This document provides instructions for performing various statistical analyses and data management tasks in SPSS, including sorting data, selecting cases, splitting files, merging files, visual binning, frequencies analysis, descriptive statistics, cross tabulation and chi-square tests, independent samples t-tests, and one-way ANOVA. The document is authored by trainers from the Department of Applied Statistics at the University of Rwanda and dated December 6, 2014.
This document introduces the basic functionality of the PANalytical X'Pert HighScore Plus v3.0 software. It covers selecting user interfaces and program settings, displaying and manipulating data, opening PDF reference patterns, and performing search-match analysis. The last page lists additional features that can be explored using the help section.
This one-day workshop on data analysis using SPSS has two parts. Part 1 covers entering data into SPSS, including preparing datasets, transforming data, and running descriptive statistics. Part 2 provides an overview of statistical analysis techniques and how to choose the appropriate technique for decision making, giving examples. The document introduces SPSS and its four windows: the data editor, output viewer, syntax editor, and script window. It describes how to define variables, enter and manage data files, sort cases, compute new variables, and perform basic analyses like frequencies, descriptives, and linear regression. Proper use of statistical techniques depends on the research question, variable types and definitions, and assumptions.
This document provides an introduction and overview of how to use the statistical software package SPSS. It discusses getting started with SPSS, opening and viewing data files, coding variables, and performing basic descriptive statistics. Specific tasks covered include entering and labeling variables, assigning value labels, handling missing data, generating frequency tables and graphs like histograms and box plots, recoding variables, and using the Compute function to calculate new variables.
SPSS (Statistical Package for the Social Sciences) is a statistical analysis software package that allows users to extract, manage, and analyze data. It provides features like generating reports, charts, descriptive statistics, and complex statistical analyses. While SPSS is easy to use and good for beginners, it has some limitations for advanced users in terms of customizing outputs and performing certain data manipulations. The document then describes the main SPSS interface windows including the data editor, output navigator, and syntax editor. It also covers how to open datasets, define variables, transform data, and create frequency tables and other outputs in SPSS.
This document provides an introduction to SPSS, including descriptions of the four windows in SPSS, basics of managing data files, and basic analysis functions. It discusses the data editor, output viewer, syntax editor, and script windows. It covers opening SPSS, defining and managing variables, saving and sorting data, transforming variables through computations, and conducting basic analyses like frequencies, descriptives, and linear regression. Examples provided include creating new variables, sorting by height, and analyzing relationships between education level and starting salary.
社會網絡分析UCINET Quick Start Guide
This guide provides a quick introduction to UCINET. It assumes that the software has been installedwith the data in the folder C:\Program Files\Analytic Technologies\Ucinet 6\DataFiles and this hasbeen left as the default directory.
Source : https://sites.google.com/site/ucinetsoftware/home
Windows 7 introduces several new and enhanced features such as Jump Lists, Peek/Live Taskbar preview, and Aero desktop experience. Jump Lists allow users to easily access frequently used programs and files without opening the full application. Peek/Live Taskbar preview shows thumbnail previews of open windows when hovering over a program's taskbar button. The Aero desktop experience provides an improved user interface with features like transparent glass windows and animated visual effects.
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.
From this power point you can get the details about Advanced Filter, Use of Macros with Advanced Filter, Data Validation, Creation of data validation Drop-Down List, Handling of External Data, Goal Seek, What-if analysis,
Windows 7 provides several features to help educators get things done faster and easier, including customizable Start menus and taskbars for quick access to frequently used programs and files, desktop gadgets for at-a-glance information, and tools like the Calculator, Snipping Tool, and Sticky Notes.
This document provides an overview of using SPSS (Statistical Package for the Social Sciences) software. It discusses installing sample data files, introduces the main interface windows including the data view, variable view and output view. It also covers how to define variable types, enter and modify data, perform basic analyses like frequencies and cross tabulations, and create charts from the output. The document is intended to help new users learn the basics of navigating the SPSS program and conducting initial analyses.
Minitab is a general-purpose statistical analysis software package developed in 1972 at Pennsylvania State University. It began as a lighter version of the NIST statistical program OMNITAB. Minitab provides tools for data management, statistical analysis, and graphing in a simple interface. Key functions include worksheet editing, data manipulation, statistical tests, graphs, and help/tutorials. Descriptive statistics in Minitab include measures of central tendency like the mean, median, and mode and measures of variability such as standard deviation and range.
Final diary products_as_carriers_of_probiotics_and_prebiotics[1]Michael Taiwo
This document provides an overview of a seminar on dairy products as carriers of probiotics and prebiotics. It defines probiotics and prebiotics and discusses the relationship between the two. The document outlines several probiotic organisms commonly used in dairy products, including Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus gasseri, and Saccharomyces cerevisiae boulardii. It also addresses challenges in the field and future potential advances. In conclusion, the manipulation of the human microbiota through prebiotic and probiotic use provides significant health benefits and dairy products serve as a universal delivery system.
This study aims to investigate the direct and residual effects of dietary maize milling waste (MMW) on broiler performance, nutrient retention, feed intestinal transit time (FITT), and economics. Ninety-six broiler chicks will be fed either a control diet or diets with 5%, 10%, or 15% MMW from 0-3 weeks. At 3 weeks, all birds will receive the control diet to 6 weeks. FITT and nutrient retention will be measured at 3 and 6 weeks. Data will be analyzed to determine the effects of MMW on performance, nutrient retention, FITT, and the correlation between FITT and nutrient retention. The results will provide information on how MMW impacts broiler production
The document discusses the use of pre-starter diets for broiler chickens. It notes that while pre-starter diets are more digestible for neonatal chicks, they are also more expensive than starter diets. The study aims to determine the optimal duration of using pre-starter diets to increase broiler performance in a cost-effective manner. Specifically, it will compare broilers fed pre-starter diets for 5, 10, or 15 days to those fed only a starter diet in terms of growth, nutrient retention, and profitability. A trial will be conducted feeding 108 broiler chicks one of the four diet treatments to draw conclusions about whether using pre-starter diets is economically recommended.
This study aims to investigate the direct and residual effects of dietary rice husk on broiler performance, nutrient retention, feed intestinal transit time, and economics. Specifically, it will:
1) Evaluate the direct impact of feeding broilers 0-5% rice husk from 0-3 weeks on performance, nutrient retention, and intestinal transit time.
2) Assess the residual effects when birds previously fed rice husk are given a standard diet from 4-6 weeks.
3) Determine the correlation between intestinal transit time and nutrient retention/performance when broilers are fed different rice husk levels.
The experiment will involve feeding 96 broilers diets with 0, 5, 10 or 15% rice husk
This study aims to investigate the direct and residual effects of dietary brewer's dried grain (BDG) on broiler performance, nutrient retention, feed intestinal transit time, and economics. Specifically, the study will:
1) Evaluate the direct effects of feeding BDG at 0-3 weeks on performance, nutrient retention, and feed transit time.
2) Assess the residual effects of BDG fed at 0-3 weeks on subsequent performance, nutrient retention, and economics at 4-6 weeks when birds are fed a standard diet.
3) Determine the correlation between feed transit time and nutrient retention, performance, and economics when broilers are fed different levels of BDG.
This document provides guidance on improving sales calls and prospecting. It discusses defining a valid business reason for every sales call to focus on the customer's needs. It also covers properly timing prospecting by respecting business hours, asking for the customer's time, and being willing to reschedule. Additionally, it recommends defining an ideal customer profile by analyzing best and worst past customers to prioritize prospects that align well. While no prospect will be a perfect fit, focusing efforts on those with overlapping characteristics can maximize time and results.
This document provides information about what qualifies students for a class and an overview of a presentation on soft skills. It states that students must have their written materials, phones turned off, be prepared to actively participate in role play groups, and any group member may have to present. It then outlines the contents of the soft skills presentation, including defining soft skills, types of soft skills, sales and marketing skills, and selling solutions. The document provides details on each of these topics through several subsections.
The patient presented with severe chest pain and ECG changes were not conclusive for myocardial infarction (MI). Initial lab results showed elevated total CK and AST, within normal ALT and electrolytes. To confirm MI diagnosis, serial CK-MB estimations every 4-6 hours for 24-36 hours would show if the cardiac isoenzyme is elevated, suggesting MI. Total CK can be elevated in MI, skeletal muscle damage, muscular exercise, intramuscular injections or drugs like digoxin and lidocaine.
This document discusses a study on the effect of variety, pretreatment, and packaging materials on quality attributes of stored orange-fleshed sweet potato flour. It aims to determine the impact on carotenoid retention, functional properties, and microbiology during storage. Two varieties of sweet potato will be pretreated with calcium chloride or potassium metabisulfite before drying, milling, and packaging in polyethylene film or plastic tubs for 3 months. The experiment will analyze properties including carotenoid content, proximate composition, microbial levels, and pasting/functional qualities over time and between treatments. The results intend to evaluate methods for reducing carotenoid losses and maintaining sweet potato flour quality under local storage conditions.
Trait theory focuses on relatively permanent aspects of personality that influence behavior consistency. While other theories consider development and behavior prediction, trait theory compares people based on aspects and degrees of traits without addressing personality change. Gordon Allport proposed functionally autonomous central traits, while Henry Murray focused on psychogenic needs like power, affiliation, and achievement. Raymond Cattell used factor analysis to identify 16 primary personality factors measured by his 16PF assessment.
This document discusses cDNA and genomic libraries. It defines cDNA as complementary DNA synthesized from mRNA, and genomic libraries as collections of DNA fragments representing an organism's entire genome. It describes how cDNA libraries are constructed by synthesizing cDNA from purified mRNA, and genomic libraries by partially digesting genomic DNA and inserting fragments into cloning vectors. It also explains how these libraries are screened using probes to identify clones containing genes of interest or encoding specific proteins.
The document discusses Banisteriopsis caapi, commonly known as ayahuasca vine. It provides details on the plant's taxonomy, features, traditional and medical uses, and active compounds. The main active compounds are harmine, harmaline, and tetrahydroharmine which are reversible inhibitors of MAO-A. It also discusses the plant's mechanism of action as an agonist of 5-HT2A receptors and studies that have been conducted on the plant and its compounds.
This document summarizes research on students' use of social networking sites (SNS). It conducted an online survey of undergraduate students to examine how they use SNS for academic and non-academic purposes. Key findings include that students were less able to articulate the academic value of SNS than their social value, and that self-presentation or image building was not a main concern. The majority of respondents preferred Facebook over MySpace and logged into their preferred SNS multiple times a day.
The document discusses the key differences between measurement and evaluation. Measurement is the assignment of a numerical value or score to assess something and is quantitative, while evaluation makes a qualitative judgment about the value or worth of something based on criteria. Evaluation depends on measurement but involves more than just test scores - it considers progress over time, strengths and weaknesses, and makes decisions. Both measurement and evaluation are important in education, with measurement providing objective data and evaluation making holistic judgments about students, teachers, and curriculum programs.
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
Preparing non-technical founders before engaging a tech agency is crucial for the success of their projects. It starts with clearly defining their vision and goals, conducting thorough market research, and gaining a basic understanding of relevant technologies. Setting realistic expectations and preparing a detailed project brief are essential steps. Founders should select a tech agency with a proven track record and establish clear communication channels. Additionally, addressing legal and contractual considerations and planning for post-launch support are vital to ensure a smooth and successful collaboration. This preparation empowers non-technical founders to effectively communicate their needs and work seamlessly with their chosen tech agency.Visit our site to get more details about this. Contact us today www.ishtechnologies.com.au
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
UI5con 2024 - Bring Your Own Design SystemPeter Muessig
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
1. INTRODUCTION TO
SPSS
PART 1:
INTRODUCTION | FILE MANAGEMENT | DESCRIPTIVE STATISTICS | EDITING AND MODIFYING THE DATA
Session 2: by Michael Taiwo
datalogtutors@gmail.com
+2348166414241
2. LESSON OVERVIEW
• At the end of this Lesson (Part 1) the students should
be able to know the following
• Descriptive statistics
• Saving an SPSS for Windows
• Backing up your data
• Retrieving your Data Files
DATALOG EDUCATIONAL CONSULT
2
3. FILE MANAGEMENT
Saving an SPSS for Windows
Once you have entered some data you should save the file. It
is good practice to save data at regular intervals during data
entry just in case.
• To save the data you have just entered, click the File at the
top left corner of the screen and then the Save As... sub-
option.
• Something similar to the following screen will appear: Save
DATALOG EDUCATIONAL CONSULT
3
4. DATALOG EDUCATIONAL CONSULT
4
Click on the up/down-arrows to move to the relevant pen drive and
enter a suitable name in the File name window.
By default SPSS will add the file extension .sav in order to help identify
the file as a SPSS data file. Finally, click on the Save button.
Save a copy of the current
SPSS for Windows 7 file
on your P: Drive or your
pen drive, under Drives:
click on drop down arrow
in the Look in window to
generate a list of the
drives.
5. Backing Up Your Data
• It is good practice to save data on different disks and also
several names as data entry progresses e.g. mydata1 mydata2
etc). To make a backup copy of your data repeat the Save
As procedure.
Retrieving Data Files
• Retrieving an SPSS for Windows 7 File is essentially the reverse
of the save process. Click on the File option, then the Open
sub-option followed by the Data option. Something similar to
the following screen will appear. Then retrieve the required file
from the saved location.
DATALOG EDUCATIONAL CONSULT
5
7. DESCRIPTIVE STATISTICS
• For the next stage you need to retrieve the data file
foundry.sav which contains the fully labelled dataset you
saved earlier to your desktop (see page 2).
• The open your data in SPSS as you would in any other
package click File, Open, Data and retrieve your data from
your workspace.
• The first step in data analysis is to generate descriptive
statistics. This will give us a feel for the data. It will also help
identify any inconsistencies that may be in the data. This is
sometimes called data cleaning. Techniques that are
commonly used to do this include:
• Frequency Analyses
• Descriptive Statistics
• Cross-tabulations
• Plots
DATALOG EDUCATIONAL CONSULT
7
8. FREQUENCY TABLES
• Carrying out a frequencies analysis on variables is
the first step when checking for data errors, click
on Analyze and choose the Descriptive Statistics
option and then choose Frequencies.
• Move the variables of interest into the Variables
box on the right-hand side, and then click
Statistics to select some summary statistics such
as range, maximum, minimum, mean and
median, which will help you look for errors.
DATALOG EDUCATIONAL CONSULT
8
10. • To select the variable to perform a frequency table for example
the Exposure group variable, click on its name in the left hand
list and then press .
• Finally click on OK and the following output is then generated in
the output window.
DATALOG EDUCATIONAL CONSULT
10
11. • To return to the data editor click on Window and take the
data editor option from the list. With the frequency table you
can have a list of summary statistics as well.
• Click Analyze, Descriptive Statistics, and Frequencies.
Press reset and then bring the variable (say, ht) to the
Variable(s) window, click on Statistics option and select
summary statistics. Click Continue and OK button.
• Once the OK button is pressed the results are automatically
produced in an Output window, if the screen does not
appear then the Output window may already exist but is
located in the background.
• All results including the can be copied into word processing
documents by clicking on the table and performing a
standard copy and paste procedure.
DATALOG EDUCATIONAL CONSULT
11
13. Exercise Using the frequencies options find out
• what proportion of the foundry workers were exposed to
dust?
• what proportions had ever suffered from bronchitis?
• what proportion had ever smoked?
• what proportion smoked more than 40 cigarettes per day?
Video Tutorial – Frequency Tables & Descriptive Statistics
DATALOG EDUCATIONAL CONSULT
13
14. Descriptives
• The descriptives command in SPSS is useful for summarizing
quantitative data. To use this click on the Analyse tile choose the
Descriptive Statistics option and then choose descriptives.
• Move the variables of interest into the Variables box on the right-
hand side. As with the frequencies command we can obtain
descriptive statistics for several variables at once. In the panel
below we have chosen some of the quantitative variables in the
foundry data set.
DATALOG EDUCATIONAL CONSULT
14
15. Exercise:
Use the descriptive procedure to determine
• the current mean exposure to dust per day
• the mean number of cigarettes smoked per day
• For mean number of cigarettes per day you may get a
negative answer. Check the missing value codes and
redo.
DATALOG EDUCATIONAL CONSULT
15
16. Cross-tabulation
• To examine the relationship between two categorical
variables, a two way Frequency Table can be used. This is
called a cross-tabulation. Click on Analyze then Descriptive
Statistics and thenCrosstabs.
• The screen below appears. Suppose we wished to examine
how smoking status related to exposure. We could examine
this by a cross-tabulation of the variables group and
smkever.
• Select the smoking status variable smkever labelled Have
you ever smoked in the source list then click by the Row(s)
box to make this the row variable Select group labelled
Exposure Group in the source list and click by the
Column's box to select the column variable. Finally press OK
DATALOG EDUCATIONAL CONSULT
16
18. • Two way frequency tables are
more informative if they
include percentages. To add
percentages to the table select
Cells from the Crosstabs
screen. On pressing Cells, the
following screen appears.
• Column, row, or total
percentages can be selected
by clicking the appropriate
box. Whilst it is tempting to
click all three this will make the
output confusing.
• For the table above column
percentages are the most
useful as they will allow us to
compare the smoking status of
non-exposed and exposed
subjects. By clicking column we
get the resulting table.
DATALOG EDUCATIONAL CONSULT
18
Cross tabulation with percentages
Video Tutorial – Two-way crosstabulation with
percentages
Simple Cross tabulation –
Adding percentages -
19. DATALOG EDUCATIONAL CONSULT
19
Three-way tables
You may need to do comparisons on
three variables. To do this, choose
Analyze then Descriptive
Statistics and then Crosstabs.
Then the following screen appears.
To create a three dimensional
table instead of a two dimensional
table, click on a variable and move
using to layer 1 of 1 box.
If we add the variable
sex we will now get
separate tables for men
and women giving the
following
output.