The document provides step-by-step instructions for conducting various statistical analyses in SPSS, including frequency distributions, histograms, frequency polygons, measures of central tendency (mode, median, mean), measures of variability (range, variance, standard deviation), standard scores (z-scores and t-scores). It explains how to produce and interpret the outputs for each analysis in 3 sentences or less.
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 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.
This document provides an overview and instructions for using the InnerSoft STATS software to analyze data. It describes 15 different analysis procedures that can be accessed from the software's Analyze Menu, including frequency tables, descriptive statistics, crosstabs, hypothesis tests, ANOVA, correlation, regression, and time series analysis. For each analysis procedure, it provides a brief overview and descriptions of the input options and statistical tests that can be selected. The document is intended to help users understand what types of analyses can be performed and how to set up and interpret the results.
Statistics is the application of mathematical principles to the collection, analysis, and presentation of numerical data. Statisticians contribute to scientific inquiry by applying their knowledge to the design of surveys and experiments, collection and analysis of data, and interpretation of results. Key parts of statistics include mean, standard deviation, error bars, significant difference tests using t-tests, and understanding the difference between correlation and causation. The standard deviation is used to summarize the spread of variables around the mean and can be used to compare data sets.
Spreadsheets allow users to organize and calculate data across rows and columns in a grid-like format. Key features include:
- Cells can contain numbers, text, formulas, and more. Formulas allow calculations using data from other cells.
- Common spreadsheet functions include addition, subtraction, multiplication, division, and summing ranges of cells.
- Formulas must follow proper order of operations and use parentheses to group parts of formulas.
- Data and formulas can be copied and pasted to other cells to efficiently reuse values or duplicating calculations.
Focusing on specific data by using filterssum5ashm
1. Excel allows users to focus on important data by limiting the data displayed through powerful filtering tools. Filters can be applied to individual columns to display only certain values.
2. Formulas like SUM and AVERAGE do not dynamically update when rows are hidden, but SUBTOTAL and AGGREGATE functions can summarize just the visible data. Finding unique values in a column can also help analyze data.
3. Validation rules restrict data entry to valid values, helping catch errors. Rules define allowed data types, values, and display custom messages to users.
For sales data analysis, by creating data breakdowns and filters (example by region, product, salesperson, etc). Objective of Easy Pivot is to provide alternative, easier to understand Pivot Table.
The document discusses the major components of the Excel window and how to navigate and work with Excel spreadsheets. It describes key Excel concepts like workbooks, worksheets, cells, formulas, and charts. It also provides instructions for common Excel tasks like entering data, selecting ranges, inserting and deleting rows/columns, moving cells, printing, and using the Chart Wizard to create graphs.
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 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.
This document provides an overview and instructions for using the InnerSoft STATS software to analyze data. It describes 15 different analysis procedures that can be accessed from the software's Analyze Menu, including frequency tables, descriptive statistics, crosstabs, hypothesis tests, ANOVA, correlation, regression, and time series analysis. For each analysis procedure, it provides a brief overview and descriptions of the input options and statistical tests that can be selected. The document is intended to help users understand what types of analyses can be performed and how to set up and interpret the results.
Statistics is the application of mathematical principles to the collection, analysis, and presentation of numerical data. Statisticians contribute to scientific inquiry by applying their knowledge to the design of surveys and experiments, collection and analysis of data, and interpretation of results. Key parts of statistics include mean, standard deviation, error bars, significant difference tests using t-tests, and understanding the difference between correlation and causation. The standard deviation is used to summarize the spread of variables around the mean and can be used to compare data sets.
Spreadsheets allow users to organize and calculate data across rows and columns in a grid-like format. Key features include:
- Cells can contain numbers, text, formulas, and more. Formulas allow calculations using data from other cells.
- Common spreadsheet functions include addition, subtraction, multiplication, division, and summing ranges of cells.
- Formulas must follow proper order of operations and use parentheses to group parts of formulas.
- Data and formulas can be copied and pasted to other cells to efficiently reuse values or duplicating calculations.
Focusing on specific data by using filterssum5ashm
1. Excel allows users to focus on important data by limiting the data displayed through powerful filtering tools. Filters can be applied to individual columns to display only certain values.
2. Formulas like SUM and AVERAGE do not dynamically update when rows are hidden, but SUBTOTAL and AGGREGATE functions can summarize just the visible data. Finding unique values in a column can also help analyze data.
3. Validation rules restrict data entry to valid values, helping catch errors. Rules define allowed data types, values, and display custom messages to users.
For sales data analysis, by creating data breakdowns and filters (example by region, product, salesperson, etc). Objective of Easy Pivot is to provide alternative, easier to understand Pivot Table.
The document discusses the major components of the Excel window and how to navigate and work with Excel spreadsheets. It describes key Excel concepts like workbooks, worksheets, cells, formulas, and charts. It also provides instructions for common Excel tasks like entering data, selecting ranges, inserting and deleting rows/columns, moving cells, printing, and using the Chart Wizard to create graphs.
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
The document provides instructions for using various statistical analyses and tests in IBM's SPSS software. It discusses how to perform descriptive analyses, summarize data, check assumptions, conduct t-tests, ANOVA, correlations, regressions and other inferential analyses. It also provides guidance on interpreting outputs and reporting results from SPSS analyses.
1. This document provides 10 tips, shortcuts, and hacks to help users become more proficient in Microsoft Excel.
2. Some of the tips covered include selecting all cells at once, copying worksheets between workbooks, inserting multiple rows or columns, filtering data, copying formulas across cells, transposing rows and columns, continuing a numbered series, and viewing stats for highlighted numbers.
3. The shortcuts described can help automate common tasks and make Excel more efficient to use.
This document provides shortcuts for navigating, editing, formatting and working with Excel. It lists keyboard shortcuts for entering and editing cell data, working with formulas, copying/pasting/deleting cells, formatting text and cells, navigating within and between sheets, and selecting cells or groups of cells. Shortcuts are provided for common tasks like completing cell entries, navigating the worksheet, applying number/text formats, and inserting functions and formulas.
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 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.
This document provides shortcuts for navigating, editing, formatting and working with Excel. It includes shortcuts for entering and editing cell data, working with formulas, selecting cells and ranges, formatting styles and numbers, cutting/copying/pasting, and moving between sheets and windows. Many shortcuts use common keys like Enter, Esc, Delete, arrow keys, Ctrl, Alt and Shift in combination for quick access to commands.
This document provides shortcuts for Excel. It is organized into sections for entering and editing data, formatting data, selecting cells and objects, moving and scrolling, printing, working with Pivot Tables, and more. Some key shortcuts include Ctrl + ; to enter the time, Ctrl + C to copy a selection, Ctrl + V to paste, and F2 to edit the active cell. Pivot Table shortcuts allow moving fields between the page, row, column, and data areas using Alt + P, R, C, or D respectively.
Microsoft Excel is a spreadsheet program used to analyze and report data in rows and columns. The Excel window includes a title bar, menu bar, toolbars, and worksheet containing cells intersected by rows and columns. Users can select, insert, delete and format cells and their contents. Key functions covered include freezing panes, protecting sheets and workbooks, merging cells, and sorting data.
The document discusses the key components of Microsoft Excel, including worksheets, cells, formulas, functions, charts, and printing. It describes how to enter and format data, use formulas and functions, navigate between sheets, resize rows and columns, and create basic charts using the Chart Wizard. Key components of the Excel window include the worksheet, formula bar, row and column headings, and sheet tabs. Formulas in Excel always begin with an equal sign and can include arithmetic operators. Functions like SUM can be used to calculate values across ranges of cells.
The document provides an overview of a training on using SPSS. It is divided into three parts:
1) Introduction to SPSS, including background, objectives, and definitions.
2) Dealing with SPSS, covering getting started, key terms, creating a code book, and data entry.
3) Data management and analysis using SPSS, including exploratory, descriptive, and inferential analysis.
The training invites participants to properly learn how to use SPSS and makes time for questions.
Shibuthankachan is seeking a challenging role in sales, business development, client relationship management, or a similar position, preferably in the banking or financial sector. He has over 9 years of experience in roles like business development, sales, client relationship management, and collections. Currently he works as a Regional Head of Sales at ICICI Bank, where he is responsible for meeting sales targets, managing dealer partnerships, and other duties. He has a history of consistently achieving sales goals and has received appreciation letters and cash incentives for his performance.
Too often businesses fall into the pitfalls of performance measurement and either have no KPIs, don’t monitor KPIs, or they are measuring the wrong thing.
This presentation is about the benefits of KPIs, the pitfalls to avoid, and some dos and don’ts when it comes to creating KPIs.
This document contains the resume of Shradhanjali Sahu summarizing their experience and skills. They have over 1 year of experience in business oriented web application development using SharePoint 2010 & 2013, C#, ASP.NET, JavaScript, and SQL Server. Their experience includes designing and developing sites for helpdesk management, real estate investment tracking, and e-commerce. They are proficient in SharePoint, CSOM, web parts, workflows, and testing.
Este documento presenta una autoevaluación para docentes que cubre cinco categorías relacionadas con la práctica docente: actitud personal, técnicas de actuación, aplicación didáctica, interacción con los alumnos y perfil dinamizador. Cada categoría incluye declaraciones sobre las cuales los docentes deben calificar su desempeño en una escala del 1 al 4. Al final de cada categoría, los docentes suman sus puntuaciones para determinar el rango en el que se encuentran.
I began working for the avant-garde fashion brand Martin Lamothe in 2012 as an intern and was hired within a month to design patterns and men's looks. Martin Lamothe has shown collections at Madrid Fashion Week since 2007 and is now considered one of the leading avant-garde brands worldwide. My work for Martin Lamothe from 2012-2014 included designing prints and male collections for multiple fashion weeks and seasons.
This document contains a non-disclosure agreement stating that the contents are confidential and proprietary and cannot be disclosed or transmitted without express consent. It then provides a brief corporate profile and overview of Sportify LLP and Sportify Sports Education and Management Services Pvt Ltd, including their vision, mission, and inspirations. It also includes statistics on the Indian sports industry and potential business opportunities in the sector.
Marketing Infographic: Millennial consumer behavior & energy drink categoryKaryna Broadhurst
The document discusses marketing trends related to Millennials based on research from Boston Consulting Group (BCG) and other sources. Key points include:
- Millennials, aged 18-34, are influenced by technology and have different consumer behaviors than older generations like Baby Boomers. They value relevance, reputation, relationships, and social influence.
- Millennials are more likely than older groups to use their phones for coupons/promotions and be influenced by celebrity endorsements. They prioritize personal success, multitasking, adventure, and social causes over glamour.
- Hispanic Millennials, who make up 21% of the generation, are strongly influenced by culture/heritage in their
Presentation during the Bureau of Agricultural Research (BAR) Seminar Series on January 26, 2017 at RDMIC Bldg., cor. Visayas Ave., Elliptical Rd., Diliman, Quezon City
Presentation during the Bureau of Agricultural Research (BAR) Seminar Series on January 26, 2017 at RDMIC Bldg., cor. Visayas Ave., Elliptical Rd., Diliman, Quezon City
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
The document provides instructions for using various statistical analyses and tests in IBM's SPSS software. It discusses how to perform descriptive analyses, summarize data, check assumptions, conduct t-tests, ANOVA, correlations, regressions and other inferential analyses. It also provides guidance on interpreting outputs and reporting results from SPSS analyses.
1. This document provides 10 tips, shortcuts, and hacks to help users become more proficient in Microsoft Excel.
2. Some of the tips covered include selecting all cells at once, copying worksheets between workbooks, inserting multiple rows or columns, filtering data, copying formulas across cells, transposing rows and columns, continuing a numbered series, and viewing stats for highlighted numbers.
3. The shortcuts described can help automate common tasks and make Excel more efficient to use.
This document provides shortcuts for navigating, editing, formatting and working with Excel. It lists keyboard shortcuts for entering and editing cell data, working with formulas, copying/pasting/deleting cells, formatting text and cells, navigating within and between sheets, and selecting cells or groups of cells. Shortcuts are provided for common tasks like completing cell entries, navigating the worksheet, applying number/text formats, and inserting functions and formulas.
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 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.
This document provides shortcuts for navigating, editing, formatting and working with Excel. It includes shortcuts for entering and editing cell data, working with formulas, selecting cells and ranges, formatting styles and numbers, cutting/copying/pasting, and moving between sheets and windows. Many shortcuts use common keys like Enter, Esc, Delete, arrow keys, Ctrl, Alt and Shift in combination for quick access to commands.
This document provides shortcuts for Excel. It is organized into sections for entering and editing data, formatting data, selecting cells and objects, moving and scrolling, printing, working with Pivot Tables, and more. Some key shortcuts include Ctrl + ; to enter the time, Ctrl + C to copy a selection, Ctrl + V to paste, and F2 to edit the active cell. Pivot Table shortcuts allow moving fields between the page, row, column, and data areas using Alt + P, R, C, or D respectively.
Microsoft Excel is a spreadsheet program used to analyze and report data in rows and columns. The Excel window includes a title bar, menu bar, toolbars, and worksheet containing cells intersected by rows and columns. Users can select, insert, delete and format cells and their contents. Key functions covered include freezing panes, protecting sheets and workbooks, merging cells, and sorting data.
The document discusses the key components of Microsoft Excel, including worksheets, cells, formulas, functions, charts, and printing. It describes how to enter and format data, use formulas and functions, navigate between sheets, resize rows and columns, and create basic charts using the Chart Wizard. Key components of the Excel window include the worksheet, formula bar, row and column headings, and sheet tabs. Formulas in Excel always begin with an equal sign and can include arithmetic operators. Functions like SUM can be used to calculate values across ranges of cells.
The document provides an overview of a training on using SPSS. It is divided into three parts:
1) Introduction to SPSS, including background, objectives, and definitions.
2) Dealing with SPSS, covering getting started, key terms, creating a code book, and data entry.
3) Data management and analysis using SPSS, including exploratory, descriptive, and inferential analysis.
The training invites participants to properly learn how to use SPSS and makes time for questions.
Shibuthankachan is seeking a challenging role in sales, business development, client relationship management, or a similar position, preferably in the banking or financial sector. He has over 9 years of experience in roles like business development, sales, client relationship management, and collections. Currently he works as a Regional Head of Sales at ICICI Bank, where he is responsible for meeting sales targets, managing dealer partnerships, and other duties. He has a history of consistently achieving sales goals and has received appreciation letters and cash incentives for his performance.
Too often businesses fall into the pitfalls of performance measurement and either have no KPIs, don’t monitor KPIs, or they are measuring the wrong thing.
This presentation is about the benefits of KPIs, the pitfalls to avoid, and some dos and don’ts when it comes to creating KPIs.
This document contains the resume of Shradhanjali Sahu summarizing their experience and skills. They have over 1 year of experience in business oriented web application development using SharePoint 2010 & 2013, C#, ASP.NET, JavaScript, and SQL Server. Their experience includes designing and developing sites for helpdesk management, real estate investment tracking, and e-commerce. They are proficient in SharePoint, CSOM, web parts, workflows, and testing.
Este documento presenta una autoevaluación para docentes que cubre cinco categorías relacionadas con la práctica docente: actitud personal, técnicas de actuación, aplicación didáctica, interacción con los alumnos y perfil dinamizador. Cada categoría incluye declaraciones sobre las cuales los docentes deben calificar su desempeño en una escala del 1 al 4. Al final de cada categoría, los docentes suman sus puntuaciones para determinar el rango en el que se encuentran.
I began working for the avant-garde fashion brand Martin Lamothe in 2012 as an intern and was hired within a month to design patterns and men's looks. Martin Lamothe has shown collections at Madrid Fashion Week since 2007 and is now considered one of the leading avant-garde brands worldwide. My work for Martin Lamothe from 2012-2014 included designing prints and male collections for multiple fashion weeks and seasons.
This document contains a non-disclosure agreement stating that the contents are confidential and proprietary and cannot be disclosed or transmitted without express consent. It then provides a brief corporate profile and overview of Sportify LLP and Sportify Sports Education and Management Services Pvt Ltd, including their vision, mission, and inspirations. It also includes statistics on the Indian sports industry and potential business opportunities in the sector.
Marketing Infographic: Millennial consumer behavior & energy drink categoryKaryna Broadhurst
The document discusses marketing trends related to Millennials based on research from Boston Consulting Group (BCG) and other sources. Key points include:
- Millennials, aged 18-34, are influenced by technology and have different consumer behaviors than older generations like Baby Boomers. They value relevance, reputation, relationships, and social influence.
- Millennials are more likely than older groups to use their phones for coupons/promotions and be influenced by celebrity endorsements. They prioritize personal success, multitasking, adventure, and social causes over glamour.
- Hispanic Millennials, who make up 21% of the generation, are strongly influenced by culture/heritage in their
Presentation during the Bureau of Agricultural Research (BAR) Seminar Series on January 26, 2017 at RDMIC Bldg., cor. Visayas Ave., Elliptical Rd., Diliman, Quezon City
Presentation during the Bureau of Agricultural Research (BAR) Seminar Series on January 26, 2017 at RDMIC Bldg., cor. Visayas Ave., Elliptical Rd., Diliman, Quezon City
Atvirų švietimo išteklių (AŠI) sąvoka, kas gali būti AŠI, kaip surasti AŠI. DAugiau informacijos (anglų kalba) http://openstudies.eu/trainingmaterial/oer
The document discusses relative permeability, which describes the ability of fluids to flow through porous media in the presence of other fluids. It covers factors that affect relative permeability like fluid saturations, rock properties, wettability, and pressure. Different wettability types can impact relative permeability curves and residual saturations. Mobility ratios also influence waterflood performance. Proper representation and measurement of relative permeability is important for reservoir evaluation and optimization.
Este documento presenta varios problemas métricos y ejercicios resueltos relacionados con el cálculo de áreas, perímetros, volúmenes, triángulos rectángulos y los teoremas del seno y coseno. Se calculan las medidas de figuras geométricas como trapecios, conos, cilindros y triángulos usando estas herramientas métricas. También se resuelven problemas prácticos que involucran distancias y ángulos observados.
This document provides instructions for using SPSS to produce frequency distribution tables, histograms, and bar graphs from data. It also explains how to compute measures of central tendency like the mean. For frequency tables, the SPSS output displays the values, frequency, and percentage. Histograms and bar graphs can be generated by selecting the appropriate chart type. To find the mean and other metrics, the Descriptives or Frequencies options under Descriptive Statistics can be used. For nominal data, values and labels must first be assigned before analyzing frequencies.
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.
MAT 240 Random Sampling in Excel Tutorial This tutorial wiAbramMartino96
MAT 240 Random Sampling in Excel Tutorial
This tutorial will guide you though the steps necessary to collect a random sample of a data set to put on
a new sheet.
1. Open your data set in Excel. Be sure the Analysis toolpak is enabled. Steps for how to do this are
available on the Microsoft support site.
2. To find a random sample, you first need to insert the =rand() function an empty column next to
your data. In the example being shown, it is column G. To do this, select the target cell and type
in =rand() then press enter.
3. Double click the Fill handle (little square icon) at the bottom right side of the highlighted cell to
copy the formula through to the bottom of the data set. This will copy this formula to each row
of data.
4. Sort your new column to rearrange the data into a random order. To do this, select the data
within your column, then click the Sort & Filter button from the Home ribbon and choose Sort
https://support.microsoft.com/en-us/office/load-the-analysis-toolpak-in-excel-6a63e598-cd6d-42e3-9317-6b40ba1a66b4
Smallest to Largest.
5. A dialog box will open asking if you what you want to do. Select to Expand the selection and
click Sort.
6. Capture your sample size by selecting the amount of rows you are sampling. A sample of 50
would mean you should select the first 50 rows of data.
a. By selecting only the first cell of data in the first column and dragging down, Excel will
count the number of rows for you.
b. Once you have the correct number of rows, then drag to the right to highlight all the
data in the appropriate number of rows.
7. Cut and paste this selected data set onto a new sheet and you will have your random sample
separated from the main data set.
8. In the Descriptive statistics window, select input range field, then select all your numerical data
9. Then check the Summary Statistics box and click ok
10. You now should see a new sheet with just your descriptive statistics listed in a chart. Change the
titles of the columns to their respective names from your data: median listing price, median dollars
per square foot, median square feet. And remove any extraneous information that is not needed for
this project.
MAT 240 Random Sampling in Excel Tutorial
MAT 240 Scatterplots in Excel Tutorial
This tutorial will guide you though the steps necessary to create scatterplots using your data. It will also
walk you through inserting a linear trend line and inserting the regression equation and the R-squared
value on the chart.
1. Open your data set in Excel.
2. Select all the data for the two variables you are targeting. (example: median listing price & Median
square feet)
a. Tip: holding down the CTRL button while selecting your data will allow you to select two
columns of data that are not next to each other
3. On the Insert tab select Recommended Charts button
4. This will bring up the insert chart dialog box prompting you to ...
MAT 240 Random Sampling in Excel Tutorial This tutorial wiAbramMartino96
MAT 240 Random Sampling in Excel Tutorial
This tutorial will guide you though the steps necessary to collect a random sample of a data set to put on
a new sheet.
1. Open your data set in Excel. Be sure the Analysis toolpak is enabled. Steps for how to do this are
available on the Microsoft support site.
2. To find a random sample, you first need to insert the =rand() function an empty column next to
your data. In the example being shown, it is column G. To do this, select the target cell and type
in =rand() then press enter.
3. Double click the Fill handle (little square icon) at the bottom right side of the highlighted cell to
copy the formula through to the bottom of the data set. This will copy this formula to each row
of data.
4. Sort your new column to rearrange the data into a random order. To do this, select the data
within your column, then click the Sort & Filter button from the Home ribbon and choose Sort
https://support.microsoft.com/en-us/office/load-the-analysis-toolpak-in-excel-6a63e598-cd6d-42e3-9317-6b40ba1a66b4
Smallest to Largest.
5. A dialog box will open asking if you what you want to do. Select to Expand the selection and
click Sort.
6. Capture your sample size by selecting the amount of rows you are sampling. A sample of 50
would mean you should select the first 50 rows of data.
a. By selecting only the first cell of data in the first column and dragging down, Excel will
count the number of rows for you.
b. Once you have the correct number of rows, then drag to the right to highlight all the
data in the appropriate number of rows.
7. Cut and paste this selected data set onto a new sheet and you will have your random sample
separated from the main data set.
8. In the Descriptive statistics window, select input range field, then select all your numerical data
9. Then check the Summary Statistics box and click ok
10. You now should see a new sheet with just your descriptive statistics listed in a chart. Change the
titles of the columns to their respective names from your data: median listing price, median dollars
per square foot, median square feet. And remove any extraneous information that is not needed for
this project.
MAT 240 Random Sampling in Excel Tutorial
MAT 240 Scatterplots in Excel Tutorial
This tutorial will guide you though the steps necessary to create scatterplots using your data. It will also
walk you through inserting a linear trend line and inserting the regression equation and the R-squared
value on the chart.
1. Open your data set in Excel.
2. Select all the data for the two variables you are targeting. (example: median listing price & Median
square feet)
a. Tip: holding down the CTRL button while selecting your data will allow you to select two
columns of data that are not next to each other
3. On the Insert tab select Recommended Charts button
4. This will bring up the insert chart dialog box prompting you to ...
This document discusses various approaches to demand estimation in marketing research, including consumer surveys, observational research, consumer clinics, and market experiments. It then provides details on regression analysis techniques, including scatter diagrams, the regression line, ordinary least squares estimation, and tests of significance. Multiple regression analysis is also covered.
Instructions(1) Work through the pages below.(2) Use the us_demog.docxdirkrplav
Instructions:(1) Work through the pages below.(2) Use the us_demographics.jmp data table to: (a) select a continuous variable and generate a histogram
(b) select two continuous variables and determine the correlation coefficient(c) generate box plots using College Degrees as the Y, Response variable and Region as the X, Factor variable(3) Copy and paste the results for 2 (a, b, & c) in a Word document.
Histograms, Descriptive Statistics, and Stem and Leaf
Use to display and describe the distribution of continuous (numeric) variables. Histograms and stem and leaf plots allow you to quickly assess the shape, centering and spread of a distribution. For categorical (nominal or ordinal) variables, see the page on Bar Charts and Frequency Distributions.
Histograms and Descriptive Statistics
1. Open the JMP® data table us_demographics.jmp, select Analyze > Distribution.
2. Click on one of the continuous variables from Select Columns, and click Y, Columns (continuous variables have blue triangles).
3. Click OK to generate a histogram, outlier box plot and descriptive statistics.
· The percentiles, including quartiles and the median, are listed under Quantiles.
· The sample mean, standard deviation and other statistics are listed under Summary Statistics.
Example: Car Physical Data.jmp (Help > Sample Data)
Tips:
· To change the display from vertical to horizontal (as shown), click on the top red triangle and select Stack.
· To change the graphical display for a variable, or to select additional options, click on the red triangle for that variable.
· To display different summary statistics, use the red triangle next to Summary Statistics.
· To change all future output to horizontal, go to Preferences > Platforms > Distribution, click Stack and
Horizontal, then click OK.
Stem and Leaf Plot
To generate a stem and leaf plot, click on the red triangle for the variable and select Stem and Leaf.
Tips:
· A key to interpret the values is at the bottom of the plot. The top value in this example is 4300, the bottom value is 1700 (values have been rounded to the nearest 100).
· Click on values in the stem and leaf plot to select observations in both the histogram and the data table. Or, select bars in the histogram to select values in the stem and leaf plot and data table.
jmp.com/learn rev 07/2012
Use to display the distribution of continuous variables. They are also useful for comparing distributions.
Box Plots – One Variable
1. From the open JMP® data table, select Analyze > Distribution.
2. Click on another continuous variable from Select Columns, and Click Y, Columns (continuous variables have blue triangles).
3. Click OK. An outlier box plot is displayed by default next to the histogram (or above if horizontal layout). To display a quantile box plot, select the option from the red triangle for the variable.
jmp.com/learn rev 07/2012
Box Plots
The lines on the Quantile Box Plot correspond to the quantiles in the distribut.
The document discusses crosstabulation, which shows the relationship between two or more categorical variables through frequency tables. It explains how to compute crosstabs in SPSS by selecting variables for the row and column and choosing relevant statistics like chi-square. The chi-square test determines if the frequencies differ from what is expected by chance alone. It assumes categories are independent and expected counts are at least 5 in each cell.
This document provides information on using SPSS for educational research. It discusses descriptive statistics, common statistical issues in research, procedures for creating a SPSS data file and conducting descriptive analyses. It also explains how to perform t-tests, analysis of variance (ANOVA), frequencies analysis and other statistical tests in SPSS. The document is intended as a guide for researchers on applying various statistical analyses in SPSS.
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 discusses how to create and manipulate pivot table reports in Excel. Pivot tables allow users to analyze and manipulate numerical data in spreadsheets to answer questions. The document provides step-by-step instructions for creating a basic pivot table, adding filters, and moving or "pivoting" fields to view the data in different ways. It also describes how to create a pivot chart based on the data in a pivot table report.
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.
This document provides steps to create a bell curve chart in Microsoft Excel by generating random numbers based on a normal distribution, binning the random numbers to create a histogram, and plotting the original data and histogram on the same chart. The chart compares the original data set to a theoretical bell curve.
PAGE 1Using Microsoft Excel 2010 for Selected Tasks(Thr.docxalfred4lewis58146
PAGE
1
Using Microsoft Excel 2010 for Selected Tasks
(Throughout this document, a set of data refers to observations of just one variable.)
(1) To portray as a bar chart a given frequency, relative frequency, or percentage distribution of a set of qualitative data, one may:
With the categories in one column and the counts or proportions or percentages in another:
1. Select (by clicking-and-dragging) the counts or proportions or percentages.
2. Choose (from upper menu) Insert, then Column (for vertical bars) or Bar (for horizontal bars), then the first pictured sub-type.
3. Right-click on a blank spot in the chart area, choose Select Data…, choose (right of center) Edit, enter the location of the categories, click OK, and click OK.
4. Choose (from upper menu) Layout, then Axis Titles to enter appropriate labels for the horizontal and vertical axes, then Chart Title to enter an appropriate title.
5. If you wish the counts or proportions or percentages to be shown on the bars: Choose (from menu) Data Labels, then your preferred position.
(2)To portray as a pie chart a given frequency, relative frequency, or percentage distribution, one may:
With the categories or numeric classes in one column and the counts or proportions or percentages in another:
1. Select (by clicking-and-dragging) the counts or proportions or percentages.
2. Choose (from upper menu) Insert, then Pie, then the first pictured sub-type.
3. Right-click on a blank spot in the chart area, choose Select Data…, choose (right of center) Edit, enter the location of the categories or numeric classes, click OK, and click OK.
4. (a) Choose (from upper menu) Layout, then Data Labels, then More Data Label Options (which will by default cause each “Value”--i.e, each count or proportion or percentage selected in step 1.--to appear on or near a pie slice); (b) if you wish each category or numeric class to appear on or near a pie slice, select Category name, then your preferred position; (c) click on Close; and (d) if the legend box is now superfluous, delete it.
5. Choose (from menu) Chart Title to enter an appropriate title.
(3) Counting the number of cells (within some range of cells) satisfying a particular condition:
Examples:
· To count how many of the cells A1 through A100 contain the word Agree, one may enter in some blank cell =COUNTIF(A1:A100, “Agree”) Note: In lieu of typing in “Agree”, one may click on a cell containing the word Agree.
· To count how many of the cells A1 through A100 contain the number 89, one may enter in some blank cell =COUNTIF(A1:A100, 89) Note: In lieu of typing in 89, one may click on a cell containing the number 89.
· To count how many of the cells A1 through A100 contain a number in the interval 10 to under 20, enter in some blank cell =COUNTIF(A1:A100,”<20”)-COUNTIF(A1:A100,”<10”)
· Note: Each relative address A1:A100 above may be replaced by the absolute address $A$1:$A$100. In lieu of typing in the absolute address $A$1:$A$100, .
The document provides steps to calculate summary statistics and create plots for different datasets in Excel. For the first dataset of nuclear reactor counts from 104 to 111, it describes calculating the mean, mode, median using the AVERAGE, MODE, and MEDIAN functions directly in cells or using the Insert Function tool. For the second dataset of European auto sales from 11.2 to 14.3, it describes calculating the variance, standard deviation, and range using the VAR, STDEV, and MAX-MIN functions. Finally, it provides steps to generate a boxplot and stem-and-leaf plot for a third dataset ranging from 23 to 51 using the MegaStat add-in.
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.
This document provides an introduction and overview of SPSS (Statistical Package for the Social Sciences). It discusses opening SPSS and the main windows, including the Data View and Variable View. It also covers importing data from Excel, entering data directly, sorting data, basic analyses like frequencies and descriptives, and saving data files. The document is intended as an introduction for learning the basics of managing and analyzing data in SPSS.
The document describes analyzing data from a study on human heights using a one sample t-test in SPSS. It provides the height measurements for 10 individuals and the suggestion that the mean height is 66 inches. The SPSS output shows that the p-value is greater than 0.05, so the null hypothesis that the mean height is 66 inches is not rejected.
2. Organizing Data:
A) Simple Frequency Distribution
1) Open the data
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies…
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variable(s).
6) Ensure that the Display Frequency Tables is checked.
7) Click OK.
The output should appear as:
Statistics
Age
N Valid 23
Missing 0
Age
Frequency Percent Valid Percent
Cumulative
Percent
Valid 20 2 8.7 8.7 8.7
21 2 8.7 8.7 17.4
22 2 8.7 8.7 26.1
23 2 8.7 8.7 34.8
24 6 26.1 26.1 60.9
25 2 8.7 8.7 69.6
26 1 4.3 4.3 73.9
27 3 13.0 13.0 87.0
29 1 4.3 4.3 91.3
30 1 4.3 4.3 95.7
35 1 4.3 4.3 100.0
Total 23 100.0 100.0
3. The first column represents all of the possible raw scores from the data set (which in this data set
is age in years). The frequency column displays the amount of times each score (age) appeared.
The total number of subjects (N) is listed at the bottom of the frequency column. The percent
column equals the frequency of each raw score divided by the total number of subjects N
multiplied by 100 ([frequency (ƒ) / N] *100) . The cumulative percent column represents the
percentage of total subjects who had scores at or above the given row.
B) Histogram
1) Open the data
2) On the toolbar, click Graphs.
3) From the dropdown menu, highlight Chart Builder…
4) A dialog box will appear. Press OK.
5) Select Histogram from the Gallery tab on the lower left hand side.
6) Click on the histogram of your choice and drag it to the white Chart preview space.
7) Drag the variable from the Variables box to the x-axis of the histogram.
8) Click OK.
The output should appear as:
4. The value or range of values described by a bar is represented on the x-axis under its base. The
height of each bar is associated with its frequency (# of times occurred) whose numerical value is
found on the y-axis.
C) Frequency Polygon
1) Open the data
2) On the toolbar, click Graphs.
3) From the dropdown menu, highlight Chart Builder…
4) A dialog box will appear. Press OK.
5) Select Area or Line from the Gallery tab on the lower left hand side.
6) Click on the graph of your choice and drag it to the white chart preview space.
7) Drag the variable from the Variables box to the x-axis of the histogram.
8) Click OK.
The output should appear as:
The raw data values are represented on the x-axis. The frequency each X-coordinate value
occurs is represented by the height, or Y-coordinate, that is associated with it. Frequency
polygons can be deceiving, however, as straight lines connect data points and can create the
allusion of a frequency for an x-coordinate for which the score does not actually exist. For
example, in this frequency polygon a frequency of 1 is given for every age from 29-35 years old.
In reality there is one 29 year old, one 30 year old, and one 35 year old in the data set and none
for the other ages. Cross validation of data may be important when using frequency polygons.
5. Central Tendency:
A) Mode
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies...
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variables.
6) Select Statistics.
7) Check Mode under Central Tendency.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
Statistics
X
N Valid 15
Missing 0
Mode 8
The mode is the value that appears most frequently. In this case, there are 15 scores (N) and the
mode is 8. If there are two scores that have the same frequency then the data is bimodal. If all of
the individual scores occur only once or more than two scores occur the same number of times
then the data is multimodal. The data will appear as:
Statistics
X2
N Valid 15
Missing 0
Mode 1a
a. Multiple modes exist.The
smallestvalue is shown
Here multiple modes exist and the lowest mode score is shown.
6. B) Median
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies…
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variables.
6) Select Statistics.
7) Check Median under Central Tendency.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
Statistics
X
N Valid 15
Missing 0
Median 9.00
The median is highlighted in green. In this case there are 15 scores (N) and the median is 173.
C) Mean
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies…
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variables.
6) Select Statistics.
7) Check Meanunder Central Tendency.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
7. Statistics
X
N Valid 15
Missing 0
Mean 9.13
The mean is highlighted in green. In this case, there are 15 subjects (N) and the mean is 173.
The mean is the best measure of central tendency as it gives equal weight to all scores. It is also
the only measure of central tendency that is used in further calculations.
D) Standard Error of the Mean
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies…
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variables.
6) Select Statistics…
7) Check S.E. Meanunder Dispersion.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
Statistics
X
N Valid 15
Missing 0
Std. Error of Mean 1.162
The standard error of the mean numerically estimates sampling error. Sampling error occurs
when a sample of a population is measured instead of the entire population. The standard error
of the mean is highlighted in yellow. If a score is within ±1 SE mean from the sample mean, then
there is a 68% chance that this score is correct. If a score is within ± 2 SE mean from the sample
mean, then there is a 95% chance that this score is correct. If a score is within ±3 SE mean from
the sample mean, then there is a 99% chance that this score is correct.
8. The equation for Standard error of the mean is √SD/n. An increase in Standard Deviation results
in a larger Standard Error of the Mean. Increase the number of subjects decreases the Standard
Error of the Mean.
Variability:
A) Range
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies...
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variable(s).
6) Click the box labeled Statistics…
7) Check Range under Dispersion.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
Statistics
measure
N Valid 9
Missing 0
Range 14
The range is the difference between the highest and lowest raw scores in a data set. It is very
unstable because the range is based on only two values. It is best used to double check work.
B) Variance
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies...
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variable(s).
6) Click the box labeled Statistics…
7) Check Variance under Dispersion.
8) Click Continue.
9) Uncheck Display Frequency Tables.
9. 10) Click OK.
The output should appear as:
Statistics
measure
N Valid 9
Missing 0
Variance 20.750
Variance (s2) is the average of the squared deviation of each score from the mean. It is not
directly useful in the analysis of data though it is used as a precursor in the calculation of
Standard Deviation.
C) Standard Deviation
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies...
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variable(s).
6) Click the box labeled Statistics…
7) Check Standard Deviation under Dispersion.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
Statistics
measure
N Valid 9
Missing 0
Std. Deviation 4.555
Standard Deviation is the square root of variance. It describes variability in the original units of a
data set. The data is typically reported as mean ±SD.
eg. Bulls scoring = 20 ± 28 points. SD = 28
Cavs scoring = 20 ± 10 points. SD = 10
10. Standard Scores:
A) Z-scores
A Z-score is a raw score expressed in standard deviation units. Z-scores eliminate test-specific
units allowing comparisons between tests.
1) Open the Data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics…
4) Move the variables of interest from the list of variables in the left box into the box labeled
Variable(s).
5) Ensure the box labeled Save standardized values as variables is selected
6) Click OK.
The output should appear as:
The Z-scores will be listed in the SPSS data output screen as another column. Here it is shown at
the right of the screen (Zheight).
B) T-scores
A T-score is a derivative of the z-score that produces user-friendly numbers.
T = 50 + 10z
For normally distributed data, ~99% of all T-scores range between 20 and 80.
1) Open the data.
2) On the toolbar, click Transform.
3) From the dropdown menu, highlight Compute Variable.
4) Create a label in the box on the top right labeled Target Variable: (i.e. Tunit).
11. 5) From the box on the top right labeled Numeric Expression type in 50+10*ZScores(make sure that it is
typed identical to the Z score label from the data view)
6) Click OK
The output should appear as:
Here the T-scores are listed in a column adjacent to the corresponding Z-scores.
Assessing Normality:
Data is said to be normal if it passes two conditions:
1) It cannot be skewed:
-2≤ (skewness/SEs)≤2
2) It cannot be kurtosed:
-2≤ (kurtosis/SEk)≤2
A) Skewness
Skewness is the degree to which a curve is not bilaterally symmetrical. Characterized as
positively skewed or negatively skewed.
Negatively Skewed curves look like the curve is being pulled to the right. The data is more
concentrated on positive side of the range.
Positively Skewed curves look like the curve is being pulled to the left. The data is more
concentrated on negative side of the range.
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies...
12. 5) Move the variables of interest from the list of variables in the left box into the box labeled
Variable(s).
6) Click the box labeled Statistics…
7) Check Skewness under Distribution.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
Statistics
measure
N Valid 20
Missing 0
Skewness 1.513
Std. Error of Skewness .512
B) Kurtosis
Kurtosis is the degree of peakedness or flatness to a curve.
Leptokurtic curves have a narrow but tall spike that represents a large frequency over a small
range.
Platykurtic curves are flat and broad in nature. Low frequencies occur over a large range.
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Descriptive Statistics.
4) From the dropdown menu, highlight Frequencies...
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variable(s).
6) Click the box labeled Statistics…
7) Check Kurtosis under Dispersion.
8) Click Continue.
9) Uncheck Display Frequency Tables.
10) Click OK.
The output should appear as:
13. Statistics
measure
N Valid 20
Missing 0
Kurtosis 3.515
Std. Error of Kurtosis .992
Relationships Between/Among Variables:
A) Simple Correlation
Correlation is a numerical value that describes the direction and strength of the linear
relationship between two variables.
- Pearson Product Moment Correlation (r)
Ranges from a perfect relationship (±1) to no relationship (0)
-1 0 1
(complete relationship) (no relationship) (complete relationship)
Correlation can be used to:
- Examine to see if two variables share a relationship
- To evaluate the validity of a new measure
- As the basis for prediction
Correlation should NOT be used to assess reliability.
Steps:
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Correlate.
4) From the dropdown menu, click Bivariate...
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variables.
6) Ensure that Pearson is selected in the Correlation Coefficient box.
7) Ensure that Two-tailed is selected in the Test of Significance box.
8) Ensure that the box labeled Flag significant correlations is checked.
9) Click OK.
14. The output should appear as:
Correlations
Simple Reaction
Time (ms)
Movement Time
for Linear Arm
Movement (ms)
Simple Reaction Time (ms) Pearson Correlation 1 -.034
Sig. (2-tailed) .925
N 10 10
Movement Time for Linear
Arm Movement (ms)
Pearson Correlation -.034 1
Sig. (2-tailed) .925
N 10 10
Conclusion statements:
1) Written in past tense.
2) Use a word that describes strength
3) Use actual r-value.
4) Must have the words “statistically significant”
5) The actual r-value.
If p > 0.05 (regardless of r), the conclusion statement reads:
o “There is no significant relationship (p>0.05) between X and Y
If p≤0.05 and r is between ±0.10 and ±0.30, the conclusion statement reads:
o “A weak (r=0.XXX) statistically significant relationship (p≤0.05) existed between
X and Y.”
If p≤0.05 and r is between ±0.30 and ±0.50, the conclusion statement reads:
o “A moderate (r=0.XXX) statistically significant relationship (p≤0.05) existed
between X and Y.”
If p≤0.05 and r is greater than ±0.50, the conclusion statement reads:
o “A strong (r=0.XXX) statistically significant relationship (p≤0.05) existed
between X and Y.
The conclusion statement for this output reads as:
15. There is no significant relationship (p>0.05) between Simple Reaction Time and Movement
Time for Linear Arm Movement.
B) Coefficient of determination
Area of overlap represents common variance (how much X explains the variability of Y or vice
versa). Can calculate this area by squaring r (r2).
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Correlate.
4) From the dropdown menu, click Bivariate...
5) Move the variables of interest from the list of variables in the left box into the box labeled
Variables.
6) Ensure that Pearson is selected in the Correlation Coefficient box.
7) Ensure that Two-tailed is selected in the Test of Significance box.
8) Ensure that the box labeled Flag significant correlations is checked.
9) Click OK.
The output should appear as:
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .940a
.884 .876 3.6261
a. Predictors:(Constant),mile walk/run time (s)
eg. Body weight (Variable 1) explains 11% of the variability in Body Fat % (Variable 2)
C) Simple Regression
The line of best fit (regression line) best describes the relationship between two variables.
Allows for the prediction of a dependent variable (predicted variable) from an
independent variable (predictor variable).
Equation of the line:
Y = bX + C
Y = score in y variable, b = slope, X = score in x variable, and c = y-intercept.
16. Example:
Using Y = 0.7353X + 7.9852 to describe the relationship between right grip strength and left grip
strength, what is the prediction for the left grip strength of a person with a right grip strength =
40kg?
Y = 0.7353X + 7.9852
Y’ = 0.7353 (40kg) + 7.9852
Y’ = 37.4kg
Y’ = 37kg
Y’ = 37kg is the predicted left grip strength of a person with a right grip strength of 40kg.
The prime (‘) in Y’ denotes that this value is the predicted value and is an estimate. The
original regression equation represents the actual value and becomes the predicted value once
values are inserted and a prediction is made.
Steps:
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Regression.
4) From the dropdown menu, click Linear…
5) Select the variable that you want to predict and move it to the Dependent box.
6) Select the predictor variable and move it to the Independent box.
7) Click OK.
The output should appear as:
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 80.167 3.633 22.068 .000
mile walk/run time (s) -.073 .007 -.940 -11.021 .000
a. DependentVariable:VO2 max (ml/kg/min)
Slope of the regression line = -0.073
Y-intercept = 80.167
D) Multiple Regression
17. Equation of the line:
Y’ = b1X1 + b2X2 + b3X3 + C
Steps:
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Regression.
4) From the dropdown menu, click Linear…
5) Select the variable that you want to predict and move it to the Dependent box.
6) Move the variable of interest (more than one variable) (Predictor Variables(X)) from the list
of variables in the left box into the box labeled Independent.
7) Click OK.
The output should appear as:
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .636a
.405 .364 5.3735
a. Predictors:(Constant),Body Weight (lbs),Body Height(cm)
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 88.346 21.620 4.086 .000
Body Height(cm) -.546 .144 -.711 -3.791 .001
Body Weight(lbs) .146 .035 .792 4.219 .000
a. DependentVariable:Body Fat (%)
Body Height:
Slope of the regression line = -0.546
Y-intercept = 88.346
Body Weight:
Slope of the regression line = 0.146
Y-intercept = 88.346
18. Multiple RegressionEquation:
Y’ = -0.546X1 + 0.146X2 + 88.346
E) Standard Error of the Estimate
The standard error of the estimate (SEE) estimates the error in prediction.
Fundamentally, SEE is the average of the squared residuals of each score.
o Interpreted like a standard deviation (or standard error of the mean), allowing the
determination of ranges and probabilities for each score.
Example: The previously determined left grip strength of a person with a right grip strength of
40kg was 37kg. If the SEE = 5kg, we are ~95% confident the actual score will be between what
two values?
~95% = 2(SEE)
o Y’ - 2(SEE) < actual < Y’ + 2(SEE)
o 37kg – 2(5kg) < actual < 37kg + 2(5kg)
o 37kg – 10kg < actual < 37kg +10kg
o 27kg < actual < 47kg
Steps:
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Regression.
4) From the dropdown menu, click Linear…
5) Select the variable that you want to predict and move it to the Dependent box.
6) Select the predictor variable and move it to the Independent box.
7) Click OK.
The output should appear as:
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .940a
.884 .876 3.6261
a. Predictors:(Constant),mile walk/run time (s)
Differences Between/Among Sample Means:
A) t-tests
19. A t-test is a statistical analysis that determines whether the difference between two means is
“real” or the result of random chance.
t = Sample 1 Mean – Sample 2 Mean
Difference due to Random Chance (SEDiff)
Probability:
If p >0.05, accept H0 and conclude:
o “There was no significant difference (p>0.05) between levels of the independent
variable in the dependent variable.”
o “There was no significant difference (p>0.05) between males and females in
height.”
If p≤0.05, reject H0 and conclude:
o “Independent variable level 1 was significantly (p<0.05) greater than independent
variable 2 in the dependent variable.”
o “Males were significantly greater (p<0.05) than females in height.”
o “Males were significantly taller (p<0.05) than females.”
Conclusion statements must include:
1) p-value.
2) The word “significant”.
3) Written in past tense (was, were…).
4) If there was a difference than it must have directionality (Males were taller than females).
One sample t-test
Compares the mean of one sample against a known standard.
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Compare Means.
4) From the dropdown menu, click One-sample T-Test…
5) Select the variable that you want to compare and move it to the Test Variable(s) box.
6) Select the measurement standard you want to compare against and move it to the Test Value
box.
7) Click OK.
The output should appear as:
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Body Fat (%) 32 18.519 6.7380 1.1911
20. One-Sample Test
Test Value = 20
t df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
Body Fat (%) -1.244 31 .223 -1.4813 -3.911 .948
P>0.05
Conclusion Statement:
There was no significant difference in body fat percentage (p>0.05) between the sample mean
and the standard for body fat %.
Independent t-test (between subjects)
Compares the means of two samples composed of different people.
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Compare Means.
4) From the dropdown menu, click Independent-Samples T Test…
5) Select the dependent variable and move it to the Test Variable(s) box.
6) Select the independent variable and move it to the Grouping Variable box.
7) The Grouping Variable box should say “(Group? ?)”. Click on Define Groups. Designate
the levels of the independent variable using the same labels used in the data. Eg. Men =1 or M;
Women =2 or W. Ensure that the same labels are used for both.
7) Click OK.
The output should appear as:
Group Statistics
Active or
Passive N Mean Std. Deviation Std. Error Mean
Arm positioning Active 10 2.2820 1.24438 .39351
Passive 10 1.9660 1.50606 .47626
Independent Samples Test
21. Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Arm
positioning
Equal variances
assumed
.513 .483 .511 18 .615 .31600 .61780 -.98194 1.61394
Equal variances
not assumed
.511 17.382 .615 .31600 .61780 -.98526 1.61726
What to look for:
Independent Samples Test Box:
Levene’s Test for Equality of Variances:
If Levene’s Test has p<0.05 then there are not equal variances in both samples. Use the p-
value from the bottom row (equal variances not assumed).
If Levene’s Test has a p>0.05 then equal variance is assumed. Use the p-value from the
top row (equal variances assumed).
Conclusion Statement for this output:
Levene’s Test = p>0.05 (equal variance assumed)
p-value >0.05 (0.615). Accept the H0.
There were no significant differences in the errors made by the active group versus the passive
group (p>0.05) in arm positioning.
Dependent (paired samples)t-test (within subjects)
Compares the means of two samples composed of the same people.
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Compare Means.
4) From the dropdown menu, click Paired Samples T Test...
5) Select the first sample mean from the variable you want to compare and move it to the
Variable 1 box.
22. 6) Select the second sample mean from the variable you want to compare and move it to the
Variable 2 box.
7) Click OK.
The output should appear as:
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Not Motivated V02 max test 39.80 10 11.858 3.750
Motivated VO2 max test 47.80 10 9.830 3.108
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)Mean
Std.
Deviation
Std. Error
Mean
95% Confidence Interval of
the Difference
Lower Upper
Pair 1 Not Motivated V02
max test - Motivated
VO2 max test
-8.000 4.738 1.498 -11.389 -4.611 -5.340 9 .000
What to look for:
Paired Samples Test Box:
Sig. (2-tailed) = p-value <0.0005
Paired Samples Statistics Box:
Compare means to determine which Sample Mean is larger. Determines directionality with
p<0.05.
Conclusion Statement for this output:
p-value <0.05. Reject the H0. Accept HA.
VO2 max scores were significantly greater with motivation than without motivation (p<0.05).
B) ANOVA
The analysis of variance (ANOVA) compares any number of sample means to determine if
significant differences exist or if the differences are due to random chance.
H0 = There is no significant difference among sample means.
HA = There is a significant difference among sample means.
23. If p>0.05 accept H0 and reject HA
If p<0.05, reject H0 and accept HA
o There is a significant difference among the means.
o Analyze Post hoc tests.
Post Hoc tests are statistical analyses calculated after ANOVA that
determine which pair(s) of means significantly differ.
Make Pairwise Comparisons between Sample means and evaluate as
normal (conclusion statements are like t-tests).
Simple ANOVA
Simple ANOVA is an extension of the independent t-test. It is a “between” comparison
comparing sample means taken from groups of different people.
o It is a 1-way ANOVA comparing sample means of 1 Independent Variable.
Steps:
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight General Linear Model.
4) From the dropdown menu, click Univariate…
5) Select the dependent variable and move it to the Dependent Variable box.
6) Select the independent variable and move it to the Fixed Factor(s) box.
7) Click Post Hoc button and move Independent Variable into Post Hoc Tests For box.
8) Select Scheffé, Tukey, or LSD Post Hoc Tests depending on preference. Tukey is the most
neutral Post Hoc Test.
9) Click Options and move all variables over to Display Means For box.
10) Check Descriptive Statistics.
11) Click Continue.
The output should appear as:
Descriptive Statistics
DependentVariable:FootSpeed
Groups Mean Std. Deviation N
Good 6.00 1.414 6
Average 7.83 1.472 6
Poor 11.00 1.789 6
Total 8.28 2.585 18
24. Tests of Between-Subjects Effects
DependentVariable:FootSpeed
Source
Type III Sum of
Squares df Mean Square F Sig.
Corrected Model 76.778a
2 38.389 15.633 .000
Intercept 1233.389 1 1233.389 502.285 .000
Groups 76.778 2 38.389 15.633 .000
Error 36.833 15 2.456
Total 1347.000 18
Corrected Total 113.611 17
What to look for:
Tests of Between Subjects Effects Box:
p-value on “Groups” row represents p-value for entire group and determines if a Post Hoc Test is
required.
p-value <0.05 is significant.
There was a significant difference (p<0.05) among the sample means.
Post Hoc tests are required for further evaluations.
Post Hoc:
Multiple Comparisons
DependentVariable:FootSpeed
(I) Groups (J) Groups
Mean Difference
(I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD Good Average -1.83 .905 .140 -4.18 .52
25. Poor -5.00*
.905 .000 -7.35 -2.65
Average Good 1.83 .905 .140 -.52 4.18
Poor -3.17*
.905 .009 -5.52 -.82
Poor Good 5.00*
.905 .000 2.65 7.35
Average 3.17*
.905 .009 .82 5.52
Scheffe Good Average -1.83 .905 .163 -4.29 .62
Poor -5.00*
.905 .000 -7.46 -2.54
Average Good 1.83 .905 .163 -.62 4.29
Poor -3.17*
.905 .011 -5.62 -.71
Poor Good 5.00*
.905 .000 2.54 7.46
Average 3.17*
.905 .011 .71 5.62
What to look for:
Evaluate all pairwise comparisons in Post Hoc Test and make conclusion statements for
each.
If a significant difference is found between a pairwise comparison, look in Descriptive
Statistics Box to determine which sample mean is larger to determine directionality.
Conclusion Statements:
There was a significant difference in the means between Good Sprinters v. Poor Sprinters
(p<0.0005) and Average sprinters v. poor sprinters (p=0.011)
Good sprint group sample mean = 6
Average sprint group sample mean = 8
Poor sprint group sample mean = 11
Good sprinters had a significantly lower horizontal foot speed at touchdown (p<0.05)
than poor sprinters.
26. Average sprinters had a significantly lower horizontal foot speed at touchdown (p<0.05)
than poor sprinters.
Repeatedmeasures ANOVA (with post hocs)
Repeated measures ANOVA is an extension of the dependent t-test. It is a “within”
comparison typically comparing the same subjects on the same test completed on
multiple occasions.
o It is a 1-way ANOVA comparing sample means of 1 Independent Variable.
Steps:
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight General Linear Model.
4) From the dropdown menu, click RepeatedMeasures…
5) In the Within-Subject Factor Name box, rename (set as factor1) the Within-variable (time is
always a within variable).
6) In the Number of Levels box enter the number of levels of the independent variable.
7) Click Add.
8) Move the appropriate variables from the left column into the Within-Subjects Variables box.
9) Click on Options.
10) In the Display Means for box move all variables into Factor(s) and Factor Interactions
box.
11) In the Repeated Measures Options, click on Compare main effects box.
12) Under Confidence interval adjustment ensure LSD is selected.
13) In the Repeated Measures Options, click on descriptive statistics (optional).
14) Click Continue.
15) Click OK.
The output should appear as:
Descriptive Statistics
Mean Std. Deviation N
minute 2 16.60 3.209 5
minute 4 20.80 3.564 5
minute 6 25.00 3.742 5
minute 8 30.60 3.507 5
minute 10 35.40 4.159 5
Minute 2 = 17 ml/kg/min
Minute 4 = 21 ml/kg/min
Minute 6 = 25 ml/kg/min
27. Minute 8 = 31 ml/kg/min
Minute 10 = 35 ml/kg/min
Mauchly's Test of Sphericityb
Measure:MEASURE_1
Within
Subjects
Effect Mauchly's W
Approx. Chi-
Square df Sig.
Epsilona
Greenhouse-
Geisser Huynh-Feldt Lower-bound
time .000 19.748 9 .046 .351 .484 .250
What to look for:
Mauchly’s Test of Sphericity box:
Mauchly’s Test of Sphericity is equivalent to Levene’s test of variance.
If p>0.05 then the assumption of sphericity (variance) is valid.
Use top line of Tests of Within-Subjects Effects box to evaluate p-value for overall H0.
If p<0.05 then the assumption of sphericity (variance) is violated.
Use second line (Greenhouse – Geisser) of Tests of Within-Subjects Effects box to
evaluate p-value for overall H0.
Tests of Within-Subjects Effects
Measure:MEASURE_1
Source
Type III Sum of
Squares df Mean Square F Sig.
time Sphericity Assumed 1127.040 4 281.760 98.517 .000
Greenhouse-Geisser 1127.040 1.405 802.122 98.517 .000
Huynh-Feldt 1127.040 1.937 581.965 98.517 .000
Lower-bound 1127.040 1.000 1127.040 98.517 .001
Error(time) Sphericity Assumed 45.760 16 2.860
Greenhouse-Geisser 45.760 5.620 8.142
Huynh-Feldt 45.760 7.746 5.907
Lower-bound 45.760 4.000 11.440
Post Hoc Required?
p <0.05 = There is a significant difference (p<0.05) among the means.
Yes, Post Hoc Tests are required for pairwise comparisons.
29. What to look for:
Evaluate all pairwise comparisons in Post Hoc Test and make conclusion
statements for each.
If a significant difference is found between a pairwise comparison, look in
Descriptive Statistics Box to determine which sample mean is larger to
determine directionality.
Minute 2 = 17 ml/kg/min
Minute 4 = 21 ml/kg/min
Minute 6 = 25 ml/kg/min
Minute 8 = 31 ml/kg/min
Minute 10 = 35 ml/kg/min
Conclusion Statements:
Minute 2 was significantly lower (p < 0.05) than Minute 4.
Minute 2 was significantly lower (p < 0.05) than Minute 6.
Minute 2 was significantly lower (p < 0.05) than Minute 8.
Minute 2 was significantly lower (p < 0.05) than Minute 10.
Minute 4 was significantly lower (p < 0.05) than Minute 6.
Minute 4 was significantly lower (p < 0.05) than Minute 8.
Minute 4 was significantly lower (p < 0.05) than Minute 10.
Minute 6 was significantly lower (p < 0.05) than Minute 8.
Minute 6 was significantly lower (p < 0.05) than Minute 10.
Minute 8 was significantly lower (p < 0.05) than Minute 10.
Intraclass correlation
Intraclass correlation (ICC) solves the two problems associated with using Pearson’s r for
reliability:
1) Controls for systematic bias.
2) Can evaluate more than two trials.
30. Steps:
1) Open the data.
2) On the toolbar, click Analyze.
3) From the dropdown menu, highlight Scale.
4) From the dropdown menu, click Reliability Analysis…
5) Move variables to be tested into Items: box.
6) Click on Statistics box.
7) Check Intraclass Correlation Coefficient box.
8) Click Continue.
9) Click OK.
The output shout appear as:
Intraclass Correlation Coefficient
Intraclass
Correlationa
95% Confidence Interval F Test with True Value 0
Lower Bound Upper Bound Value df1 df2 Sig
Single Measures .908b
.714 .989 50.272 4 16 .000
Average Measures .980c
.926 .998 50.272 4 16 .000
What to look for:
In the Intraclass Correlation Coefficient box the coefficient is in the Single Measures row.
Conclusion:
R =0.908
The pilot data is reliable with R>0.8
Between-within(mixed model) factorial ANOVA (with post hocs)
A 2-way ANOVA that analyzes 2 Independent variables:
o within IV
o between IV
Three p-values are obtained:
o p-value for Main Effect 1 (between IV)
o p-value for Main Effect 2 (within IV)
o p-value for Interaction effect (Effect of main effect 1 on main effect 2 and 2 on 1)
31. Steps:
1)Open the data..
2) On the toolbar, click Analyze.
3)From the dropdown menu, highlight General Linear Model.
4)From the dropdown menu, click Repeated Measures.
5) In the Within-Subject Factor Name box, define within-subject factor (ex. Pre-anger, post-
anger).
6) In the Number of Levels box enter the number of levels (ex 2)
7) In the Between-Subject Factor(s) box, assign the between-subject variable(s) (mass-loss
group/(group)). (Image 5).
8) Click Options.
9) From the Factor(s) and Factor Interactions box: move variables: overall, group (group
effect on anger), time (time effect on anger), group*time (group-time interaction)) into the
Display Means for box. (Image 6)
10) Click Compare main effects box.
11) Click Display Means under the Display box.
12) Click Continue.
13) Click OK.
The output should appear as:
For Pre and Post Anger:
Tests of Within-Subjects Effects
Measure:MEASURE_1
Source Type III Sum of
Squares df Mean Square F Sig.
time Sphericity Assumed 93.006 1 93.006 8.670 .011
Greenhouse-Geisser 93.006 1.000 93.006 8.670 .011
Huynh-Feldt 93.006 1.000 93.006 8.670 .011
Lower-bound 93.006 1.000 93.006 8.670 .011
time * group Sphericity Assumed 17.042 2 8.521 .794 .473
Greenhouse-Geisser 17.042 2.000 8.521 .794 .473
Huynh-Feldt 17.042 2.000 8.521 .794 .473
Lower-bound 17.042 2.000 8.521 .794 .473
Error(time) Sphericity Assumed 139.458 13 10.728
Greenhouse-Geisser 139.458 13.000 10.728
Huynh-Feldt 139.458 13.000 10.728
Lower-bound 139.458 13.000 10.728
32. Estimates
Measure:MEASURE_1
time
Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
1 1.333 .505 .242 2.424
2 4.806 1.167 2.285 7.326
What to look for:
Tests of Within Subjects Effects:
Use this box for p-value evaluating Main Effect (within).
Conclusion Statement:
Post-anger was significantly greater than pre-anger (p<0.05)
___
For Group Effect on Anger:
Tests of Between-Subjects Effects
Measure:MEASURE_1
Transformed Variable:Average
Source Type III Sum of
Squares df Mean Square F Sig.
Intercept 290.720 1 290.720 20.452 .001
group 13.083 2 6.542 .460 .641
Error 184.792 13 14.215
What to look for:
Tests of Between Subjects Effects:
Use this box for p-value evaluating Main Effect (between).
There was no significant effect on group on anger (p>0.05).
___
For Time and Anger Interaction
Tests of Within-Subjects Effects
Measure:MEASURE_1
33. Source Type III Sum of
Squares df Mean Square F Sig.
time Sphericity Assumed 93.006 1 93.006 8.670 .011
Greenhouse-Geisser 93.006 1.000 93.006 8.670 .011
Huynh-Feldt 93.006 1.000 93.006 8.670 .011
Lower-bound 93.006 1.000 93.006 8.670 .011
time * group Sphericity Assumed 17.042 2 8.521 .794 .473
Greenhouse-Geisser 17.042 2.000 8.521 .794 .473
Huynh-Feldt 17.042 2.000 8.521 .794 .473
Lower-bound 17.042 2.000 8.521 .794 .473
Error(time) Sphericity Assumed 139.458 13 10.728
Greenhouse-Geisser 139.458 13.000 10.728
Huynh-Feldt 139.458 13.000 10.728
Lower-bound 139.458 13.000 10.728
What to look for:
Tests of Within Subjects Effects:
Use this box for p-value evaluating time and anger interaction effect.
Conclusion Statement:
There was no significant time by group interaction p > 0.05.
Steps for evaluating Between-Within ANOVA output:
1. Evaluate p for main effect 1.
If p < 0.05, evaluate pairwise comparisons.
If p > 0.05, stop.
2. Evaluate p for main effect 2.
If p <0.05, evaluate pairwise comparisons.
If p > 0.05, stop.
3. Evaluate p for main effect 3.
If p <0.05, evaluate pairwise comparisons.
If p > 0.05, stop.
34. Decision Tree:
1) Normal? Interval or Ratio?
A) No Non Parametric
B)Yes Question 2
2) Relationship, Difference, or Prediction?
A) Relationship = Correlation
B) Difference = Question 3
C) Predictions = Regression
1 Predictor – Simple Regression
2 Predictor – Multiple Regression
3) How many Sample Means?
A) 2 Sample Means
Are samples composed of Same or Different People?
Same people – Dependent T-Test
Different people – Independent T-Test
B) 3 or more Sample Means = Question 4
4) How many IVs?
A) 1 IV
Are Samples Composed of Same or Different People?
Same – 1-Way Repeated Measures ANOVA
Different – 1-Way Simple ANOVA
B) 2 IVs = Question 5
5) IVs between or within?
A) Between –Within (Mixed Model)
B) Between – Between
C) Within – Within (Repeated Measures)