1) JMP is statistical software that allows for easy import, organization, and analysis of data. It features spreadsheet-like data tables, powerful statistical modeling capabilities, and customizable graphics.
2) The document reviews various features of JMP including importing data, organizing data tables, performing statistical analyses through platforms like distribution and fit model, and creating graphs and reports.
3) Assistance is available for using JMP through free training, support contacts, and detailed help menus within the software. JMP allows for both simple and advanced statistical analysis of data.
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.
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.
This document discusses different types of sampling methods used in qualitative research. It defines key terms like sample, random sampling, and non-probability sampling. It then explains different sampling techniques in more detail, including simple random sampling, systematic random sampling, stratified random sampling, multi-stage cluster sampling, convenience sampling, snowball sampling, quota sampling, accidental sampling, panel sampling, and improving response rates. The document emphasizes that qualitative researchers are more concerned with understanding phenomena in depth than statistical validity or generalizability.
STATA is data analysis software that can be used via menu options or typed commands. It has a wide range of econometric techniques and can open, examine, and run regressions on datasets. The tutorials on www.STATA.org.uk provide step-by-step guides for using STATA to perform tasks like data management, statistical analysis, importing data, summary statistics, graphs, regressions, and other analyses.
Topics for the class include multiple regression, dummy variables, interaction effects, hypothesis tests, and model diagnostics. Prerequisites include a general familiarity with Stata, including importing and managing datasets and data exploration, the linear regression model, and the ordinary least squares estimation.
Workshop materials including do files and example data sets are available from http://projects.iq.harvard.edu/rtc/event/regression-stata
This document provides an introduction to statistics. It defines statistics as the science of data that involves collecting, classifying, summarizing, organizing, and interpreting numerical information. It outlines key terms such as data, population, sample, parameter, and statistic. It describes different types of variables like independent and dependent variables. It discusses descriptive statistics, inferential statistics, and predictive modeling. Finally, it explains important concepts like measures of central tendency, measures of variation, and statistical distributions like the normal distribution.
This document discusses data management and presentation. It begins by defining data as a collection of facts such as values or measurements. The document then discusses different types of data including numerical, categorical, qualitative, and quantitative data. Various methods of collecting and presenting data are also outlined, including tables, graphs, charts, and different types of each. The document provides examples and discusses how to interpret data findings by relating them to objectives and real-life contexts.
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.
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.
This document discusses different types of sampling methods used in qualitative research. It defines key terms like sample, random sampling, and non-probability sampling. It then explains different sampling techniques in more detail, including simple random sampling, systematic random sampling, stratified random sampling, multi-stage cluster sampling, convenience sampling, snowball sampling, quota sampling, accidental sampling, panel sampling, and improving response rates. The document emphasizes that qualitative researchers are more concerned with understanding phenomena in depth than statistical validity or generalizability.
STATA is data analysis software that can be used via menu options or typed commands. It has a wide range of econometric techniques and can open, examine, and run regressions on datasets. The tutorials on www.STATA.org.uk provide step-by-step guides for using STATA to perform tasks like data management, statistical analysis, importing data, summary statistics, graphs, regressions, and other analyses.
Topics for the class include multiple regression, dummy variables, interaction effects, hypothesis tests, and model diagnostics. Prerequisites include a general familiarity with Stata, including importing and managing datasets and data exploration, the linear regression model, and the ordinary least squares estimation.
Workshop materials including do files and example data sets are available from http://projects.iq.harvard.edu/rtc/event/regression-stata
This document provides an introduction to statistics. It defines statistics as the science of data that involves collecting, classifying, summarizing, organizing, and interpreting numerical information. It outlines key terms such as data, population, sample, parameter, and statistic. It describes different types of variables like independent and dependent variables. It discusses descriptive statistics, inferential statistics, and predictive modeling. Finally, it explains important concepts like measures of central tendency, measures of variation, and statistical distributions like the normal distribution.
This document discusses data management and presentation. It begins by defining data as a collection of facts such as values or measurements. The document then discusses different types of data including numerical, categorical, qualitative, and quantitative data. Various methods of collecting and presenting data are also outlined, including tables, graphs, charts, and different types of each. The document provides examples and discusses how to interpret data findings by relating them to objectives and real-life contexts.
This document discusses concepts related to data, including collection, organization, presentation, and analysis of data. It defines key terms like qualitative vs quantitative data and primary vs secondary data. It explains methods of collecting primary data through surveys, sampling techniques, and secondary data from published and unpublished sources. The document also covers organizing data through frequency distributions, statistical series, and presenting data in tabular, diagrammatic and graphical forms like pie charts, histograms, bar diagrams and ogives. It concludes with analyzing organized data through measures of central tendency, dispersion, correlation and regression.
This document provides an introduction to biostatistics. It defines key concepts such as statistics, data, variables, populations, and samples. It discusses different types of variables including quantitative and qualitative variables. It also describes different measurement scales including nominal, ordinal, interval and ratio scales. Sources of data and descriptive statistics are introduced. Descriptive statistics help summarize and organize data using tables, graphs, and numerical measures.
This document discusses ordinal regression and linear models used for ordinal regression. It describes how ordinal regression can be used to predict an ordinal variable where the relative ordering of values is significant. Linear models like generalized linear models (GLMs) are commonly used to fit coefficients to model the cumulative probability between threshold values for each class. The document outlines how the model parameters are updated by maximizing the log-likelihood function, and how predictions are made by comparing fitted values to the threshold values. It also provides an example of implementing ordinal regression using the H2O machine learning library in R and Python. Results on several datasets show that directly optimizing an error function to increase prediction accuracy can perform better than maximizing the likelihood.
Statistics is the science of dealing with numbers.
It is used for collection, summarization, presentation and analysis of data.
Statistics provides a way of organizing data to get information on a wider and more formal (objective) basis than relying on personal experience (subjective).
This document provides instructions for creating and deploying a survey form using the KoboToolbox platform. It outlines 17 steps, including creating an account, building the form with different question types like multiple choice, matrix, and grouping questions, adding metadata and skip logic, deploying the form, and collecting and analyzing survey response data.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
This document provides an overview of getting started with data analysis using Stata. It discusses what Stata is, describes the Stata screen and interface, and covers first steps like setting the working directory, creating log files, allocating memory, using do-files, opening and saving Stata data files, finding variables quickly, subsetting data using conditional statements, understanding Stata's color-coding system, importing data from other programs like SPSS and SAS, and provides an example of a dataset in Excel. The document serves as an introduction to basic functions and workflows in Stata.
Descriptive Statistics and Data VisualizationDouglas Joubert
This document provides an overview of descriptive statistics and data visualization techniques. It discusses levels of measurement, descriptive versus inferential statistics, and univariate analysis. Various graphical methods for displaying data are also described, including frequency distributions, histograms, Pareto charts, boxplots, and scatterplots. The document aims to help readers choose appropriate analysis and visualization methods based on their research questions and data types.
This document provides an overview of key concepts in biostatistics including data display and summary. It defines different types of data, variables, and statistical measures. Descriptive statistics like mean, median and mode are used to summarize central tendencies, while measures like range, variance and standard deviation describe data dispersion. Various graphs including histograms, boxplots and stem-and-leaf plots are discussed as tools for data visualization.
Introduction to survival analysis Providing intuition of hazard function, survival function, cumulative failure function. Life table, KM and log-rank test
This document discusses various aspects of data analysis. It outlines the basic steps in research and data analysis, including identifying the problem, collecting data, analyzing and interpreting results. Both qualitative and quantitative data analysis methods are covered. Descriptive statistics are used to summarize data through measures like frequencies and central tendency. Inferential statistics allow generalization to populations through hypothesis testing using techniques like t-tests and chi-square tests. The document provides an overview of common statistical analysis methods and selecting the appropriate tests.
This document provides an overview of descriptive statistics used in cardiovascular research. Descriptive statistics summarize and describe data through calculations of central tendency, dispersion, and shape. They are used to analyze variables that are discrete (categorical nominal and ordinal) or continuous. Common descriptive statistics include mean, median, mode, range, variance, standard deviation, quartiles, interquartile range, skewness, and kurtosis. Graphs such as dot plots, box plots, and histograms can complement tabular descriptive statistics to display patterns in the data. Univariate analysis examines one variable at a time to understand its distribution, central tendency, and dispersion.
This document provides an introduction to statistics and biostatistics in healthcare. It defines statistics and biostatistics, outlines the basic steps of statistical work, and describes different types of variables and methods for collecting data. The document also discusses different types of descriptive and inferential statistics, including measures of central tendency, dispersion, frequency, t-tests, ANOVA, regression, and different types of plots/graphs. It explains how statistics is used in healthcare for areas like disease burden assessment, intervention effectiveness, cost considerations, evaluation frameworks, health care utilization, resource allocation, needs assessment, quality improvement, and product development.
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.
Role of statistics in biomedical researcheman youssif
This document provides an overview of the role of statistics in biomedical research. It discusses how statistics is used for data collection, presentation, and analysis in medical studies. Data collection methods include gathering constant or variable data that can be continuous, discrete, nominal, ordinal, quantitative, or qualitative. Common ways to present data include calculating the mean, median, mode, range, and proportion or percentage. Data is also presented visually using charts, graphs, boxes and error bars. Statistical analysis allows for hypothesis testing and determining statistical significance. The document then examines different types of epidemiological studies including descriptive (case reports, case series, correlational, and cross-sectional) and analytical (case-control, cohort including prospective and retrospective, and
Multivariate and Conditional Distributionssusered887b
The document discusses key concepts in multivariate analysis including:
1) The multivariate normal distribution plays a fundamental role as both a population model and approximate sampling distribution for many statistics.
2) Multivariate distributions are determined by their mean vectors and covariance matrices.
3) Multivariate analysis involves measuring and analyzing dependence between variables and sets of variables.
4) Many real-world problems fall within the framework of multivariate normal theory.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Learn how to navigate Stata’s graphical user interface, create log files, and import data from a variety of software packages. Includes tips for getting started with Stata including the creation and organization of do-files, examining descriptive statistics, and managing data and value labels. This workshop is designed for individuals who have little or no experience using Stata software.
Full workshop materials including example data sets and .do file are available at http://projects.iq.harvard.edu/rtc/event/introduction-stata
The document discusses key concepts in public health methodologies and biostatistics. It defines data as facts that can be processed by computers. Statistics is described as the study of collecting, summarizing, analyzing and interpreting data. Biostatistics applies statistical techniques to health-related fields like medicine. Descriptive statistics refers to methods used to describe data, while inferential statistics are used to draw conclusions from numeric data. Variables, grouped vs. ungrouped data, and types of variables are also outlined.
L9 using datawarrior for scientific data visualizationSeppo Karrila
A tutorial for beginning graduate students on data visualization, by hands-on training in using DataWarrior. These are only handout notes so the students can try things out on their own laptops, with the free software, instead of scribbling notes themselves. The instructor needs to demonstrate the options or functions listed in the handout notes.
Crystal tr///SAP Design Studio online training by design studio Export-24/7//...venkat training
///SAP Design Studio online training by design studio Export-24/7//
venkat
Contact numbers : +91 9972971235,+91-9663233300(India)
Email Id : Madhukar.dwbi@gmail.com, https://www.youtube.com/watch?v=KK6DxwhYxAI&t=23s
Website:
http://www.sap-bo-online-training.com/
This document discusses concepts related to data, including collection, organization, presentation, and analysis of data. It defines key terms like qualitative vs quantitative data and primary vs secondary data. It explains methods of collecting primary data through surveys, sampling techniques, and secondary data from published and unpublished sources. The document also covers organizing data through frequency distributions, statistical series, and presenting data in tabular, diagrammatic and graphical forms like pie charts, histograms, bar diagrams and ogives. It concludes with analyzing organized data through measures of central tendency, dispersion, correlation and regression.
This document provides an introduction to biostatistics. It defines key concepts such as statistics, data, variables, populations, and samples. It discusses different types of variables including quantitative and qualitative variables. It also describes different measurement scales including nominal, ordinal, interval and ratio scales. Sources of data and descriptive statistics are introduced. Descriptive statistics help summarize and organize data using tables, graphs, and numerical measures.
This document discusses ordinal regression and linear models used for ordinal regression. It describes how ordinal regression can be used to predict an ordinal variable where the relative ordering of values is significant. Linear models like generalized linear models (GLMs) are commonly used to fit coefficients to model the cumulative probability between threshold values for each class. The document outlines how the model parameters are updated by maximizing the log-likelihood function, and how predictions are made by comparing fitted values to the threshold values. It also provides an example of implementing ordinal regression using the H2O machine learning library in R and Python. Results on several datasets show that directly optimizing an error function to increase prediction accuracy can perform better than maximizing the likelihood.
Statistics is the science of dealing with numbers.
It is used for collection, summarization, presentation and analysis of data.
Statistics provides a way of organizing data to get information on a wider and more formal (objective) basis than relying on personal experience (subjective).
This document provides instructions for creating and deploying a survey form using the KoboToolbox platform. It outlines 17 steps, including creating an account, building the form with different question types like multiple choice, matrix, and grouping questions, adding metadata and skip logic, deploying the form, and collecting and analyzing survey response data.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
This document provides an overview of getting started with data analysis using Stata. It discusses what Stata is, describes the Stata screen and interface, and covers first steps like setting the working directory, creating log files, allocating memory, using do-files, opening and saving Stata data files, finding variables quickly, subsetting data using conditional statements, understanding Stata's color-coding system, importing data from other programs like SPSS and SAS, and provides an example of a dataset in Excel. The document serves as an introduction to basic functions and workflows in Stata.
Descriptive Statistics and Data VisualizationDouglas Joubert
This document provides an overview of descriptive statistics and data visualization techniques. It discusses levels of measurement, descriptive versus inferential statistics, and univariate analysis. Various graphical methods for displaying data are also described, including frequency distributions, histograms, Pareto charts, boxplots, and scatterplots. The document aims to help readers choose appropriate analysis and visualization methods based on their research questions and data types.
This document provides an overview of key concepts in biostatistics including data display and summary. It defines different types of data, variables, and statistical measures. Descriptive statistics like mean, median and mode are used to summarize central tendencies, while measures like range, variance and standard deviation describe data dispersion. Various graphs including histograms, boxplots and stem-and-leaf plots are discussed as tools for data visualization.
Introduction to survival analysis Providing intuition of hazard function, survival function, cumulative failure function. Life table, KM and log-rank test
This document discusses various aspects of data analysis. It outlines the basic steps in research and data analysis, including identifying the problem, collecting data, analyzing and interpreting results. Both qualitative and quantitative data analysis methods are covered. Descriptive statistics are used to summarize data through measures like frequencies and central tendency. Inferential statistics allow generalization to populations through hypothesis testing using techniques like t-tests and chi-square tests. The document provides an overview of common statistical analysis methods and selecting the appropriate tests.
This document provides an overview of descriptive statistics used in cardiovascular research. Descriptive statistics summarize and describe data through calculations of central tendency, dispersion, and shape. They are used to analyze variables that are discrete (categorical nominal and ordinal) or continuous. Common descriptive statistics include mean, median, mode, range, variance, standard deviation, quartiles, interquartile range, skewness, and kurtosis. Graphs such as dot plots, box plots, and histograms can complement tabular descriptive statistics to display patterns in the data. Univariate analysis examines one variable at a time to understand its distribution, central tendency, and dispersion.
This document provides an introduction to statistics and biostatistics in healthcare. It defines statistics and biostatistics, outlines the basic steps of statistical work, and describes different types of variables and methods for collecting data. The document also discusses different types of descriptive and inferential statistics, including measures of central tendency, dispersion, frequency, t-tests, ANOVA, regression, and different types of plots/graphs. It explains how statistics is used in healthcare for areas like disease burden assessment, intervention effectiveness, cost considerations, evaluation frameworks, health care utilization, resource allocation, needs assessment, quality improvement, and product development.
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.
Role of statistics in biomedical researcheman youssif
This document provides an overview of the role of statistics in biomedical research. It discusses how statistics is used for data collection, presentation, and analysis in medical studies. Data collection methods include gathering constant or variable data that can be continuous, discrete, nominal, ordinal, quantitative, or qualitative. Common ways to present data include calculating the mean, median, mode, range, and proportion or percentage. Data is also presented visually using charts, graphs, boxes and error bars. Statistical analysis allows for hypothesis testing and determining statistical significance. The document then examines different types of epidemiological studies including descriptive (case reports, case series, correlational, and cross-sectional) and analytical (case-control, cohort including prospective and retrospective, and
Multivariate and Conditional Distributionssusered887b
The document discusses key concepts in multivariate analysis including:
1) The multivariate normal distribution plays a fundamental role as both a population model and approximate sampling distribution for many statistics.
2) Multivariate distributions are determined by their mean vectors and covariance matrices.
3) Multivariate analysis involves measuring and analyzing dependence between variables and sets of variables.
4) Many real-world problems fall within the framework of multivariate normal theory.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Learn how to navigate Stata’s graphical user interface, create log files, and import data from a variety of software packages. Includes tips for getting started with Stata including the creation and organization of do-files, examining descriptive statistics, and managing data and value labels. This workshop is designed for individuals who have little or no experience using Stata software.
Full workshop materials including example data sets and .do file are available at http://projects.iq.harvard.edu/rtc/event/introduction-stata
The document discusses key concepts in public health methodologies and biostatistics. It defines data as facts that can be processed by computers. Statistics is described as the study of collecting, summarizing, analyzing and interpreting data. Biostatistics applies statistical techniques to health-related fields like medicine. Descriptive statistics refers to methods used to describe data, while inferential statistics are used to draw conclusions from numeric data. Variables, grouped vs. ungrouped data, and types of variables are also outlined.
L9 using datawarrior for scientific data visualizationSeppo Karrila
A tutorial for beginning graduate students on data visualization, by hands-on training in using DataWarrior. These are only handout notes so the students can try things out on their own laptops, with the free software, instead of scribbling notes themselves. The instructor needs to demonstrate the options or functions listed in the handout notes.
Crystal tr///SAP Design Studio online training by design studio Export-24/7//...venkat training
///SAP Design Studio online training by design studio Export-24/7//
venkat
Contact numbers : +91 9972971235,+91-9663233300(India)
Email Id : Madhukar.dwbi@gmail.com, https://www.youtube.com/watch?v=KK6DxwhYxAI&t=23s
Website:
http://www.sap-bo-online-training.com/
Excel is a computer program used to create electronic spreadsheets. It allows users to organize data, create charts and perform calculations. Key features include conditional formatting to highlight certain cells based on values, pivot tables to analyze and summarize large datasets, and functions like SUM, AVERAGE, and IF to perform calculations on cell values. Formulas can contain relative or absolute cell references, and functions follow an order of operations to evaluate complex formulas correctly.
Dr. D. Sugumar discusses using Microsoft Excel to analyze measurement data from an experiment. Key points covered include using Excel to calculate statistics like mean, median, mode, and standard deviation. Students will take measurements, input the data into Excel, and use functions and charts to analyze the results. Formatting, sorting, filtering and other Excel skills are reviewed to facilitate the data analysis task.
This courseware will introduce you to basics in working with Excel Spreadsheets. It'll serve as a compliment to the in-lab sessions that will be held during the data journalism training session - Voter's Count - in Kumasi
This presentation shows how to use Microsoft Excel to analyze data. It covers basics, formulas, ranges, formatting, functions, charts, and pivots.
Examples are provided for more than 200 concepts introduced to users of MS Excel to enable them in analyzing and visualizing their data using this powerful and widely available tool.
Examples are also available in an MS Excel spreadsheet.
Please reach out to the author for a copy.
This document provides an overview of using computers for data analysis. It discusses Excel, a spreadsheet program, and SPSS, a statistical analysis program. For Excel, it describes basic functions like entering data, formulas, formatting, and common analysis tools. It explains how to perform operations like sums, averages, statistical functions. It also covers manipulating sheets and importing/exporting data. For SPSS, it lists some statistical tests it can run like confidence intervals, distributions, frequencies, correlations, t-tests, ANOVA, and forecasting growth trends.
Uses & applications of microsoft excel in vph researchDr Alok Bharti
Microsoft Excel is a spreadsheet application that allows users to enter and organize data into columns and rows, perform calculations with formulas, and visualize data through graphs and charts. It consists of worksheets where data is entered into cells that are organized by columns and rows. Common functions include formatting cells, filtering and sorting data, and using formulas to analyze data through calculations and pivot tables to examine relationships between variables.
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.
This document provides an overview of spreadsheets and Microsoft Excel. It discusses how Excel allows users to perform calculations, organize and analyze data. Common uses of spreadsheets include sales, accounting, scheduling and inventory. The document then reviews Excel basics like worksheets, cells, formatting, sorting, inserting/deleting rows and columns. It provides instructions for entering labels and values, cutting/copying/pasting, and formatting cells.
This document provides instructions for using descriptive statistics, filtering, advanced filtering, pivot tables, and other data analysis tools in Excel. It explains how to compute descriptive statistics using functions or the Data Analysis ToolPak. It also demonstrates how to filter datasets, use advanced filtering to extract subsets of records, create and format pivot tables to summarize data, add and modify fields, and link pivot table cells to external formulas.
This document outlines a training overview for a Microsoft Excel extended introduction course. The course consists of 6 classes covering topics like terminology, navigation, formatting, functions, macros, importing data, and charts. Each class is scheduled for a different date and includes the topics that will be covered, such as formatting, sorting, filtering, and different types of functions like date, logical, and statistical functions.
MS Excel and Visual Basic Applications.pptxsurekha1287
Microsoft Excel can be used to solve engineering problems by integrating Excel and Visual Basic for Applications (VBA). The course aims to teach students how to perform calculations in Excel, solve civil engineering problems using VBA, and design structural elements by combining Excel and VBA. Students will learn functions, charts, and how to write macros in VBA. Conditional formatting and sorting data in Excel are also covered.
The document provides instructions for a training session on Microsoft Excel concepts including conditional formatting, data validation, pivot tables, and slicers. The training will cover how to use conditional formatting to automatically format cells based on conditions, how to set up data validation lists to restrict data entry, how pivot tables can automatically summarize and organize data, and how slicers make filtering pivot table data easier. The agenda includes demonstrations of these features and examples of how they can be applied.
The 7 basic quality tools through minitab 18RAMAR BOSE
The document provides an overview of creating and customizing control charts in Minitab. It explains how to create an I-MR chart and Xbar-R chart from sample data files, including how to select test criteria, format scales and axes, and add reference lines. The document also provides general information about when to use control charts and considerations for the type of data needed to create these charts.
Prepared as part of the IT for Business Intelligence course of MBA @VGSOM, IIT Kharagpur. The tutorial describes how to create an interactive map using the open source software QGIS.
Here are the steps to visualize a potential indel region after realignment:
1. Run GATK IndelRealigner on the target list:
java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T IndelRealigner -R ../human_g1k_v37.fasta -I sample.dedup.bam -targetIntervals sample.intervals -o sample.realigned.bam
2. Index the realigned BAM:
samtools index sample.realigned.bam
3. Load the realigned BAM into IGV and navigate to a region of interest from the target list (sample.intervals).
4. In I
This document discusses phylogenetic analysis and tree building. It introduces the Bioinformatics and Computational Biology Branch (BCBB) group and their work analyzing biological sequences and constructing phylogenetic trees. The document explains why biological sequences are important to analyze and compares sequences to understand relatedness and evolution. It also covers multiple sequence alignment, substitution models, and algorithms for building trees, including neighbor-joining.
The webinar covered new features and updates to the Nephele 2.0 bioinformatics analysis platform. Key updates included a new website interface, improved performance through a new infrastructure framework, the ability to resubmit jobs by ID, and interactive mapping file submission. New pipelines for 16S analysis using DADA2 and quality control preprocessing were introduced, and the existing 16S mothur pipeline was updated. The quality control pipeline provides tools to assess data quality before running microbiome analyses through FastQC, primer/adapter trimming with cutadapt, and additional quality filtering options. The webinar emphasized the importance of data quality checks and highlighted troubleshooting tips such as examining the log file for error messages when jobs fail.
1) METAGENOTE is a new web-based tool for annotating genomic samples and submitting metadata and sequencing files to the Sequence Read Archive (SRA) at the National Center for Biotechnology Information (NCBI).
2) It provides templates and controlled vocabularies to streamline sample metadata annotation using existing ontologies and standards. This allows for easier cross-study comparisons.
3) The demonstration showed how to use METAGENOTE's interface to annotate a mouse ear skin sample with terms from relevant ontologies, import additional annotations in batch, and submit the metadata and files to NCBI SRA through a 5-step wizard.
This document provides an introduction to homology modeling using computational tools like I-TASSER and Phyre2. It discusses how homology modeling can be used to generate 3D structural models of proteins when an experimental structure is not available. The document addresses common questions from users and outlines the I-TASSER modeling pipeline. Hands-on exercises are provided to allow users to run homology modeling tools and examine the resulting models.
This document summarizes different computational methods for protein structure prediction, including homology modeling, fold recognition, threading, and ab initio modeling. Homology modeling relies on identifying proteins with similar sequences and known structures. Fold recognition and threading can be used when there are no homologs, to identify proteins with the same overall fold but different sequences. Ab initio modeling uses physics-based modeling and protein fragments to predict structure from sequence alone, and has challenges due to the vast number of possible conformations.
Homology modeling is a computational technique for predicting the structure of a protein target based on its sequence similarity to proteins with known structures, and it involves finding a suitable template, aligning the target and template sequences, building a 3D model of the target, and evaluating the model quality. While experimental methods like X-ray crystallography and NMR can determine protein structures, they have limitations in terms of which proteins can be studied, so computational methods like homology modeling are needed to predict structures for the many proteins whose structures remain unknown.
The document discusses function prediction for unknown proteins. It begins with an overview of common methods for function prediction, including sequence and structure similarity, domains and motifs, gene expression, and interactions. It then uses a protein called Msa as a case study, analyzing it with various tools and finding evidence it may function as a signal transducer in bacterial response to environment. Finally, it briefly discusses another protein M46 and challenges in evaluating prediction accuracy.
This presentation discusses protein structure prediction using Rosetta. It begins with an overview of the Critical Assessment of Protein Structure Prediction (CASP) experiments and notes that Rosetta is one of the top performing free-modeling servers. The presentation then describes the basic ab initio protocol used by Rosetta, which involves fragment insertion, scoring, and refinement. It also discusses limitations and success rates. Key aspects of the Rosetta energy functions and sampling algorithms are presented. Examples of specific Rosetta applications including low-resolution modeling and refinement are provided.
This document provides an outline for a presentation on biological networks, including introducing biological networks, describing their basic components and types, methods for predicting and building networks, sources of interaction data, tools for network visualization and analysis, and a demonstration of building, visualizing and analyzing biological networks using Cytoscape. The presentation covers topics like nodes and edges in networks, features used to analyze networks, methods for predicting networks from sequences and omics data, integrated databases for interaction data, and popular tools for searching, visualizing and performing network analysis.
This document provides an overview and introduction to using the command line interface and submitting jobs to the NIAID High Performance Computing (HPC) Cluster. The objectives are to learn basic Unix commands, practice file manipulation from the command line, and submit a job to the HPC cluster. The document covers topics such as the anatomy of the terminal, navigating directories, common commands, tips for using the command line more efficiently, accessing and mounting drives on the HPC cluster, and an overview of the cluster queue system.
This document provides an overview of statistical analyses that can be performed in PRISM. It discusses how to perform common statistical tests like t-tests, ANOVA, linear regression, and summarizes the appropriate tests to address different research questions. Examples are given of how to analyze pre-post treatment data using paired t-tests and compare groups using independent t-tests or ANOVA. Guidance is also provided on interpreting results and checking assumptions.
This document discusses methods for analyzing categorical data and response variables, including contingency tables, chi-square tests, Fisher's exact test, odds ratios, logistic regression, and generalized linear models. Contingency tables are used to display relationships between categorical variables and tests of independence. Fisher's exact test and chi-square tests determine if a relationship is statistically significant. Odds ratios and relative risk indicate the magnitude of relationships. Logistic regression models relationships between continuous predictors and categorical responses. Generalized linear models extend these methods.
This document provides a training manual on better graphics in R. It begins with an overview of R and BioConductor and reviews basic R functions. It then covers creating simple and customized graphics, multi-step graphics with legends, and multi-panel layouts. The manual aims to help researchers learn visualization techniques to improve the communication of their data and results.
This document describes two web tools that were created using R to automate biostatistics workflows: HDX NAME and DRAP. HDX NAME analyzes hydrogen-deuterium exchange mass spectrometry data to estimate protein flexibility. It computes protection factors, compares groups, and maps results to protein structures. DRAP fits logistic dose-response curves to drug screening data from multiple plates. It automates curve fitting, compares results, and exports summaries. Both tools were created with R on the backend for analysis and web interfaces for usability. This allows researchers to perform complex analyses without programming expertise.
This document discusses several common problems with data handling and quality including building and testing models with the same data, confusion between biological and technical replicates, and identification and handling of outliers. It provides examples and explanations of key concepts such as experimental and sampling units, pseudo-replication, outliers versus high influence points, and leverage plots. The importance of proper data handling techniques like dividing data into training, test, and confirmation sets and using cross-validation is emphasized to avoid overfitting models and generating spurious findings.
The document provides an overview of statistical testing, including:
- When to use parametric vs. nonparametric tests
- When large sample tests or exact tests are needed
- When adjustments for multiple testing are required
It discusses key concepts like null and alternative hypotheses, test statistics, p-values, and type I and II errors. Examples of the Student's t-test and Wilcoxon rank sum test are provided.
This document summarizes a presentation on curve fitting using GraphPad Prism. It discusses nonlinear regression techniques for analyzing dose-response and binding curve data commonly used by biologists. Specific nonlinear regression models like sigmoidal dose-response curves are described. The document provides guidance on choosing and fitting appropriate models, evaluating model fit, and improving model fit if needed.
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A presentation that explain the Power BI Licensing
Intro to JMP for statistics
1. Introduction
t JMP fto JMP for
StatisticsStatistics
Jeff Skinner, M.S.
Biostatistics Specialist
Bioinformatics and Computational Biosciences Branch (BCBB)
NIH/NIAID/OD/OSMO/OCICB
http://bioinformatics.niaid.nih.gov
S i A @ i id ihScienceApps@niaid.nih.gov
2. JMP Statistical Discovery
• NIAID researchers can request JMP 8.0 for Mac or
Windows PC at http://bioinformatics niaid nih govWindows PC at http://bioinformatics.niaid.nih.gov
• Desirable combination of features:Desirable combination of features:
Spreadsheet data tables with point-and-click user interface
Deep statistical modeling capabilities
Edit bl d i t ti hi h lit hi Editable and interactive high quality graphics
“Dynamic variable linking” feature helps you explore your data
Comprehensive Design of Experiments (DOE) platform
Free NIAID training and support: ScienceApps@niaid.nih.gov
3. JMP Starter Window
JMP Starter Window provides shortcuts for useful functions:• JMP Starter Window provides shortcuts for useful functions:
Opening data tables, scripts, journals or projects
Opening analysis platforms like Design of Experiments, modeling, etc.
Setting user preferences for file locations, displayed output, etc.
• Tip of the Day introduces JMP features from tutorials and help menu
4. Five Types of JMP Files
• JMP Data Tables
Spreadsheet files that store data scripts etc Spreadsheet files that store data, scripts, etc.
• JMP Scripts
Text files that store JMP Script Language (JSL) codep g g ( )
• JMP Reports
Interactive graphs and results from a statistical test
• JMP Journals
Publishing files that store read-only data tables, url links, descriptive
figures graphs statistical test results etcfigures, graphs, statistical test results, etc.
• JMP Projects
Collection of data tables, scripts, journals and reports, p , j p
5. Preferences in JMP
• General preference settings
Show JMP Starter Window and Tip of the
Day at startupDay at startup
• Platforms preferences
Customize the output from any statistical
analysis or graph
• File location preferencesFile location preferences
Choose default locations for opening and
saving files in Windows
6. Importing Data
• MS Excel® worksheets
can be imported directlycan be imported directly
Options for first row header,
multiple sheets, etc.
Remember to format your dataRemember to format your data
in MS Excel® first
• Use Text Import PreviewUse Text Import Preview
option to import text files
Options for delimited and fixed
width fileswidth files
Preview your data columns
before import to save time
7. Importing from a Database
• Windows PC users can open
dBASE and MS Access files
di l f JMPdirectly from JMP
ODBC drivers are required for
Mac users and other databases
• Open subsets of the database
files using click through menus
or specific SQL statementsor specific SQL statements
Query specific columns or even
select rows of data matching a
set of conditions (e.g. sex = male Tip: MS Excel files can be openedset of conditions (e.g. sex male
or height < 68 inches).
Click through windows generate
SQL statements automatically
Tip: MS Excel files can be opened
as database files to import subsets
of large files, but subsets of smaller
files are best created within JMP
8. JMP Data Tables
• Table, Columns and Rows menus on the left hand side of the data table
• Three places to access Columns and Rows menus:
Columns and rows windows (LHS of the data table)
Spreadsheet hotspots (upperleft corner of spreadsheet)
D d i JMP t l b (t f JMP i d ) Drop-down menus in JMP tool bar (top of JMP window)
9. JMP “Hotspots”
• JMP “Hotspots” are clickable red
triangles that provide access totriangles that provide access to
additional features in JMP
• Always check the hotspots in your
test reports to find additional tests
and figures for your analyses
• JMP hotspots make tests more
interactive and customizable
10. Variable Types in JMP
• Three types of variables in JMP:
Continuous (blue triangle symbols)
Ordinal (ordered green bar symbols)
Nominal (unordered red bar symbols)
Ch t t t i bl l b• Character or text variables can only be
ordinal or nominal variables in JMP
• Numeric variables can be anything
• Change variable types in the column info• Change variable types in the column info
menu or by clicking symbols in the column
window (shown right)
11. JMP Columns (Cols) Menu
• Use the column menu to add,
delete, select (go to) or reorder, (g )
columns in the data table
• The Column Info menu allowsThe Column Info menu allows
you to define the general
attributes of your variables
• The Preselect Role menu
allows you to specify variable
attributes within the analysesattributes within the analyses
E.g. ___ is always a response
12. JMP Columns (Cols) Menu
• Label / Unlabel option produces and
removes special labeling in figures for
l t d l (t b l)selected columns (tag symbol)
• Hide / Unhide removes columns from view
i th d t t bl b t d t thin the data table, but does not remove them
from analyses (blindfold symbol)
E l d /U l d t i• Exclude /Unexclude removes certain
columns from analyses, but not from the
data table (strike through symbol)
• Scroll Lock / Unlock secures the locations
of the columns in the data table
13. JMP Columns (Cols) Menu
• The Validation window allows you verify the values
recorded in your data table
List Check produces a list of all text responses List Check produces a list of all text responses
Range Check reports the min and max for numeric variables,
with a list of possible constraints to remove outliers
• The Recode window allows you to recode text and
numeric variables quickly and automatically
The Formula window allows you to create• The Formula window allows you to create
transformed variables (e.g. ln(x) ) and generate
random data from theoretical distributions
• The Standardize Attributes window allows you to
specify identical properties among many columns
14. JMP Rows Menu
• Add, delete, move and select rows
• Exclude/unexclude, hide/unhide and
label/unlabel options same as Cols
• Use Colors and Markers options to
identify selected rows in a graphidentify selected rows in a graph
• Powerful data management with RowPowerful data management with Row
Editor, Data Filter and Row Selection
15. Select Where … Option
• Click > Rows > Row Selection >
Select Where to open theSelect Where … to open the
Select Where window in JMP
• Select rows meeting multiple user-
specified conditions
Character variable conditions
e.g. Gender = “female”
Numeric variable inequalitiesNumeric variable inequalities
e.g. 20% < Percent Body Fat < 45%
16. JMP Tables Menu
• Summary option creates
table of column statistics
• Concatenate to join two
tables top-to-bottom iftable of column statistics
E.g. mean age, etc.
• Subset option creates a
p
they share columns
• Join tables with unique
columns side by side
new table from selected
rows and columns
Sort by column values
columns side-by-side
• Update missing values
from a new table
• Sort by column values
• Stack and Split columns
to rearrange data tables
• Tabulate table stats
using drag and drop
E g mean age etcto rearrange data tables
• Transpose rows and
columns to rotate tables
E.g. mean age, etc.
• Missing Data Pattern
finds sampling errors
17. JMP Toolbar Options
• Arrow tool • Lasso tool
Regular cursor arrow
• Help tool
Click any JMP object and be
directed its help menus
Select an irregularly-shaped
group of points on a graph
• Magnifier tool
Zoom in or zoom outdirected its help menus
• Selection tool
Select elements of a report or
journal to copy and paste
Zoom in or zoom out
• Crosshairs tool
Identify the location of points in
a figure or plotj py p
• Scroller tool
Precisely scroll in your report
• Grabber tool
g p
• Annotate tool
Add notes or captions to a figure
or report
Li t l P l t l d Grab and drag axes or other
features of a figure
• Brush tool
Highlight points on a plot
• Line tool, Polygon tool and
Simple Shape tool
Draw lines, polygons and simple
shapes on reports
Highlight points on a plot
p p
18. JMP Graphs Menu
• Use Graph Builder for drag-and-drop
200
250
Bubble Plot of Weight by Calories Sized by Percent Body Fat
• Use Chart for pie charts and bar charts
• Use Overlay and Scatterplot 3D to
100
150
Weight
1000 1500 2000 2500 3000 3500
Calories
Circle SizeUse Overlay and Scatterplot 3D to
create customizable scatter plots
• Animate a Bubble Plot over time
Circle Size
• Animate a Bubble Plot over time
• Use Cell Plot to create “heat maps” Tree Map of Region, Adverse Event
• Use Tree Map to view relationships
among categorical variables
Anemia Erythema
Headache
Induration
Leukop
enia
Malaise Nause
a
Pain Papule
Swelling
Anemia
Ecchymosis
Leukop
enia
Malaise Nodule
Pain Swelling
Anemia Headach
e
Indur
ation
Papule
Anemia
Ecchymosi
s
Erythema
Headach
e
Leukope
nia
Malai
se
Mylagia
Tenderness
Anem
ia
Arthralgia
Dimpling Headach
Myalgia Nausea
Midwest
Northwest Southeast
Southwest
among categorical variables Elavat
ed CH
50
Erythema
Pain Swelling
Tenderness
e
Induratio
n
Leukope
nia
Pain Swell
ing
TendernessNortheast
Southwest
19. JMP Analyze Menu
• Distribution procedure
Collect descriptive statistics, create
histograms and run hypothesis tests
• Modeling procedures
Nonlinear procedures for curve fitting
P titi f d t l tihistograms and run hypothesis tests
on individual variables
• Fit Y by X procedure
Automatically fits the appropriate
relationship between two variables
Partition for data exploration
Neural nets and time series models
Categorical platform for log-linear
models and share charts (JMP 7.0)
Gaussian processes and screeningrelationship between two variables
Simple linear regression, one-way
ANOVA, logistic regression and chi-
square tests for table data
• Matched pairs procedure
Gaussian processes and screening
platforms (JMP 7.0)
• Multivariate procedures
Correlations, PCA, clustering,
discriminant analysis item analysis• Matched pairs procedure
Paired t-tests and related figures
• Fit model procedure
Used to fit more complicated models
discriminant analysis, item analysis
and partial least squares (PLS)
• Survival and Reliability
Kaplan-Meier and log-rank testsp
with multiple factors, etc.
Includes general linear models (OLS),
stepwise and multivariate procedures,
generalized linear models (GLS), etc.
Parametric survival
Proportional hazards
Recurrence analysis
20. The Distribution Platform
• Descriptive Statistics
Moments: mean, variance, standard deviation, etc.
Quantiles: median, interquartile range (IQR), etc
• Descriptive Figuresp g
Histograms and boxplots
Stem and leaf, empirical cumulative distribution
function (ecdf) and normal quantile-quantile plots
• One-sample Inferences
Hypothesis tests for means and standard
deviationsdeviations
Confidence, prediction and tolerance intervals
Distribution fitting with goodness-of-fit testing
21. “Broadcasting” Your Hotspot
• Sometimes you want to perform several of the
same tests all at the same time
• Two options:p
Set your platform preferences to request special tests from the
hotspot every time
“Broadcast” your Hotspot options by control clicking any of
the individual results
• Speed up workflow and reduce “click thru”• Speed up workflow and reduce click-thru
22. JMP Script Features
• JMP scripting mostly for “power users” who want
to simplify processing or create new procedures
• Scripting Window also used for “SAS Integration”
to access SAS features
T h l f l f t f l• Two helpful features for casual users
Save analyses to their respective data tables
Leave helpful notes in data tablesLeave helpful notes in data tables
23. JMP Fit Y by X Platform
• Fit Y by X chooses the correct
analysis for any pair of variablesy y p
Simple linear regression for continuous
response and predictor variables
Logistic regression for categorical responseg g g p
and continuous predictor
One-way ANOVA for a continuous response
and categorical predictor
Contingency tables for categorical response
and predictor variables
30
35
40
45
50
BodyFat
Bivariate Fit of Percent Body Fat By Calories
• Fit Y by X often has both parametric
and nonparametric tests in hotspots
5
10
15
20
25
PercentB
1000 1500 2000 2500 3000 3500
Calories
24. JMP Fit Model Platform
• Used to fit most models with three or
more predictor variablesp
• Choose the response variables Y,
the model effects X and the model
personality to determine the model
JMP will help guide you by providing
appropriate options for your variablesappropriate options for your variables
Model types determined by selected
variables and model personality
Multiple regression
Multifactor ANOVA
Linear mixed models
• Contact ScienceApps@niaid.nih.gov
for help building models
Generalized linear
models (GLIM)
Proportional hazards
d i i land parametric survival
25. Construct Model Effects
• Cross two predictors to evaluate an interaction
I.e. synergy of fertilizer and hydration levels on plant yieldI.e. synergy of fertilizer and hydration levels on plant yield
• Nest two predictors when the levels of one
variable depend on the levels of anothervariable depend on the levels of another
I.e. auto make and model effects (e.g. Ford Mustang)
• Choose the Random effect attribute when
factor levels represent a random sample from
the larger populationthe larger population
E.g. Hospital and subject are random effects drawn from
populations of all possible hospitals and patients
26. Model Personalities
• Standard Least Squares used for most ANOVA and
regression models with continuous response variables
• Use Stepwise personality for model selection
• Use MANOVA for multivariate response variables
• Nominal or Ordinal Logistic for categorical responses
Use Generalized linear models to test for overdispersion• Use Generalized linear models to test for overdispersion
effects in least squares, logistic or log-linear models
27. SAS Integration
• Use JMP to access procedures
from a SAS license
E.g. NLMixed, GLIMMIX, etc.
• Click > File > SAS > Server• Click > File > SAS > Server
connections to access a SAS
license on the local machine or
on a remote serveron a remote server
• Submit SAS code directly fromSubmit SAS code directly from
a JSL script window
28. JMP DOE Menu
• Design of Experiments (DOE) Platform in
JMP allows you to utilize statistics conceptsy p
while planning your experiments
Estimate power and necessary sample sizes
Plan efficient experiments intended to find important effects Plan efficient experiments intended to find important effects
or optimize responses
• Designed Experiments in JMP presentations
are available for any who are interested
29. Thank You
For questions or comments please contact:
ScienceApps@niaid.nih.gov
301.496.4455
29