Data analysis presentation

Canada quality analysis and analysis is the process of developing answers to questions through the examination and interpretation of data. The basic steps in the analytic process consist of identifying issues, determining the availability of suitable data, deciding on which methods are appropriate for answering the questions of interest, applying the methods and evaluating, summarizing and communicating the ical results underscore the usefulness of data sources by shedding light on relevant issues. Some statistics canada programs depend on analytical output as a major data product because, for confidentiality reasons, it is not possible to release the microdata to the public. Data analysis also plays a key role in data quality assessment by pointing to data quality problems in a given survey. Analysis can thus influence future improvements to the survey analysis is essential for understanding results from surveys, administrative sources and pilot studies; for providing information on data gaps; for designing and redesigning surveys; for planning new statistical activities; and for formulating quality s of data analysis are often published or summarized in official statistics canada releases. Statistical agency is concerned with the relevance and usefulness to users of the information contained in its data. Analysis is the principal tool for obtaining information from the from a survey can be used for descriptive or analytic studies. The study of background information allows the analyst to choose suitable data sources and appropriate statistical methods. Any conclusions presented in an analysis, including those that can impact public policy, must be supported by the data being to conducting an analytical study the following questions should be addressed:Objectives.

This requires investigation of a wide range of details such as whether the target population of the data source is sufficiently related to the target population of the analysis, whether the source variables and their concepts and definitions are relevant to the study, whether the longitudinal or cross-sectional nature of the data source is appropriate for the analysis, whether the sample size in the study domain is sufficient to obtain meaningful results and whether the quality of the data, as outlined in the survey documentation or assessed through analysis is more than one data source is being used for the analysis, investigate whether the sources are consistent and how they may be appropriately integrated into the riate methods and an analytical approach that is appropriate for the question being investigated and the data to be analyzing data from a probability sample, analytical methods that ignore the survey design can be appropriate, provided that sufficient model conditions for analysis are met. However, methods that incorporate the sample design information will generally be effective even when some aspects of the model are incorrectly whether the survey design information can be incorporated into the analysis and if so how this should be done such as using design-based methods. See binder and roberts (2009) and thompson (1997) for discussion of approaches to inferences on data from a probability chambers and skinner (2003), korn and graubard (1999), lehtonen and pahkinen (1995), lohr (1999), and skinner, holt and smith (1989) for a number of examples illustrating design-based analytical a design-based analysis consult the survey documentation about the recommended approach for variance estimation for the survey. If the data from more than one survey are included in the same analysis, determine whether or not the different samples were independently selected and how this would impact the appropriate approach to variance data files for probability surveys frequently contain more than one weight variable, particularly if the survey is longitudinal or if it has both cross-sectional and longitudinal purposes. Consult the survey documentation and survey experts if it is not obvious as to which might be the best weight to be used in any particular design-based analyzing data from a probability survey, there may be insufficient design information available to carry out analyses using a full design-based approach. Assess the t with experts on the subject matter, on the data source and on the statistical methods if any of these is unfamiliar to determined the appropriate analytical method for the data, investigate the software choices that are available to apply the method. If analyzing data from a probability sample by design-based methods, use software specifically for survey data since standard analytical software packages that can produce weighted point estimates do not correctly calculate variances for survey-weighted is advisable to use commercial software, if suitable, for implementing the chosen analyses, since these software packages have usually undergone more testing than non-commercial ine whether it is necessary to reformat your data in order to use the selected e a variety of diagnostics among your analytical methods if you are fitting any models to your sources vary widely with respect to missing data. At one extreme, there are data sources which seem complete - where any missing units have been accounted for through a weight variable with a nonresponse component and all missing items on responding units have been filled in by imputed values. At the other extreme, there are data sources where no processing has been done with respect to missing data.

It should be noted that the handling of missing data in analysis is an ongoing topic of to the documentation about the data source to determine the degree and types of missing data and the processing of missing data that has been performed. This information will be a starting point for what further work may be er how unit and/or item nonresponse could be handled in the analysis, taking into consideration the degree and types of missing data in the data sources being used. Consider whether imputed values should be included in the analysis and if so, how they should be handled. If imputed values are not used, consideration must be given to what other methods may be used to properly account for the effect of nonresponse in the the analysis includes modelling, it could be appropriate to include some aspects of nonresponse in the analytical model. Report any caveats about how the approaches used to handle missing data could have impact on retation of most analyses are based on observational studies rather than on the results of a controlled experiment, avoid drawing conclusions concerning studying changes over time, beware of focusing on short-term trends without inspecting them in light of medium-and long-term trends. Instead, use meaningful points of reference, such as the last major turning point for economic data, generation-to-generation differences for demographic statistics, and legislative changes for social tation of the article on the important variables and topics. Always help readers understand the information in the tables and charts by discussing it in the tables are used, take care that the overall format contributes to the clarity of the data in the tables and prevents misinterpretation. In the presentation of rounded data, do not use more significant digits than are consistent with the accuracy of the y any confidentiality requirements (e. Minimum cell sizes) imposed by the surveys or administrative sources whose data are being e information about the data sources used and any shortcomings in the data that may have affected the analysis.

Either have a section in the paper about the data or a reference to where the reader can get the e information about the analytical methods and tools used. Standard errors, confidence intervals and/or coefficients of variation provide the reader important information about data quality. Check details such as the consistency of figures used in the text, tables and charts, the accuracy of external data, and simple that the intentions stated in the introduction are fulfilled by the rest of the article. As a good practice, ask someone from the data providing division to review how the data were used. Always do a dry run of presentations involving external to available documents that could provide further guidance for improvement of your article, such as guidelines on writing analytical articles (statistics canada 2008 ) and the style guide (statistics canada 2004). As well, sufficient details must be provided that another person, if allowed access to the data, could replicate the an analytical product to be accurate, appropriate methods and tools need to be used to produce the an analytical product to be accessible, it must be available to people for whom the research results would be , d. Last updated september 16, a problem or mistake on this analysis and presentation skills: the pwc approach specializationstarted nov 27enrollabout this specializationcoursescreatorsfaqdata analysis and presentation skills: the pwc approach specializationenrollstarted nov 27financial aid is available for learners who cannot afford the fee. Learn more and analysis and presentation skills: the pwc approach specializationmake smarter business decisions with data analysis. Understand data, apply data analytics tools and create effective business intelligence presentationsabout this specializationif you are a pwc employee, gain access to the pwc specialization and courses for free using the instructions on the pwc l&d spark page or simply search "coursera" on pwc specialization will help you get practical with data analysis, turning business intelligence into real-world outcomes.

We'll explore how a combination of better understanding, filtering, and application of data can help you solve problems faster - leading to smarter and more effective decision-making. You’ll learn how to use microsoft excel, powerpoint, and other common data analysis and communication tools, and perhaps most importantly, we'll help you to present data to others in a way that gets them engaged in your story and motivated to 's more, should you enroll in these courses, you will be invited to join pwc's talent network. In the first module you'll plan an analysis approach, in the second and third modules you will analyze sets of data using the excel skills you learn. Coursesbeginner prior experience 1data-driven decision makingcurrent session: nov 27subtitlesenglish, japaneseabout the coursewelcome to data-driven decision making. In this course you'll get an introduction to data analytics and its role in business decisions. Learn 3data visualization with advanced excelupcoming session: dec 4commitment4 weeks of study, 3-4 hours per weeksubtitlesenglishabout the coursein this course, you will get hands-on instruction of advanced excel 2013 functions. We’ll show you how to perform different types of scenario and simulation analysis and you’ll... Learn 4effective business presentations with powerpointupcoming session: dec 4commitment4 weeks of study, 3-4 hours a weeksubtitlesenglishabout the coursethis course is all about presenting the story of the data, using powerpoint. Learn 5data analysis and presentation skills: the pwc approach final projectupcoming session: jan 29subtitlesenglishabout the capstone projectin this capstone project, you'll bring together all the new skills and insights you've learned through the four courses.

Related slideshares at r 10-data analysis & mae nalzaro,bsm,bsn,mn, registered hed on jun 9, you sure you want message goes you sure you want message goes you sure you want message goes r at victoria email a copy of the ppt to my email address. 10-data analysis & analysis ng for analysis  the purpose  to answer the research questions and to help determine the trends and relationships among the in data analysis  before data collection, the researcher should accomplish the following:  determine the method of data analysis  determine how to process the data  consult a statistician  prepare dummy tables  after data collection:  process the data  prepare tables and graphs  analyze and interpret findings  consult again the statistician  prepare for editing  prepare for fication of descriptiveanalysiskinds of data analysis 1. Descriptive analysis  refers to the description of the data from a particular sample;  hence the conclusion must refer only to the sample. Descriptive statistics  are numerical values obtained from the sample that gives meaning to the data fication of descriptiveanalysis a. Formula: ef = n  where: e = sum of f = frequency n= sample fication of descriptiveanalysis b. Formula: where: x= ς___ x = the mean n ς = the sum of x = each individual raw score n = the number of fication of descriptiveanalysis c. Standard deviation - the most commonly used measure of variability that indicates the average to which the scores deviate from the fication of descriptiveanalysis d. Bivariate descriptive statistics  derived from the simultaneous analysis of two variables to examine the relationships between the variables. Correlation - the most common method of describing the relationship between two fication of descriptiveanalysiskinds of data analysis 1.

Inferential analysis  the use of statistical tests, either to test for significant relationships among variables or to find statistical support for the hypotheses. The level of significance is a numerical value selected by the researcher before data collection to indicate the probability of erroneous findings being accepted as true. Analysis of variance (anova) - is used to test the significance of differences between means of two or more groups. The parts of tabular data are presented in the following:  rows - horizontal entries (indicates the outcome or the dependent variable)  columns - vertical entries (indicates the cause or the independent variable)  cells - are boxes where rows and columns intersect. Interpretation of data  after analysis of data and the appropriate statistical procedure, the next chapter of the research paper is to present the interpretation of the data, which is the final step of research process. The best thing is to review the stated problem and tie up with the result of your data analysis. Chapter ii review of related literature and studies  literature (foreign/local)  studies (foreign/local)  justification of the present study chapter iii research design and methodology  research design  research subject  instrumentation  data gathering procedure  statistical treatment of data chapter iv analysis and interpretation of data chapter v summary, conclusion and recommendations bibliography appendix curriculum of contents  indicates all the contents of research paper and the page number for each section is placed at the right-hand margin. In numbering the tables, use arabic ng the basics of course - linkedin ional technology for student course - linkedin ication in the 21st century course - linkedin tation, analysis and interpretation of analysis tative data ative data n nigatu analysis, presentation and interpretation of r 4 presentation of chnic university of the sent successfully.. Clipboards featuring this public clipboards found for this the most important slides with ng is a handy way to collect and organize the most important slides from a presentation.

In numbering the tables, use arabic 365 for course - linkedin cation for interactive course - linkedin ng online: synchronous course - linkedin tation, analysis and interpretation of analysis tative data ative data n nigatu analysis, presentation and interpretation of r 4 presentation of chnic university of the sent successfully.. Now customize the name of a clipboard to store your can see my video is queuequeuewatch next video is analysis cribe from michael hines? Please try again hed on jul 21, 2014data analysis, narrated presentation rd youtube autoplay is enabled, a suggested video will automatically play s: ways of presenting bdubs math and analysis presentations hd ing data analysis to do a presentation - 5 steps to a killer uction to quantitative data 1: data analysis in beauty of data visualization - david analyst - interview analysis - 10 things i wish someone had told me about data tation tools- ting statistical to prepare an oral research an state university - undergraduate analysis and analysis & ting the story of your data with microsoft powerpoint mvp, nolan mistakes in data analysis and strategies to address to analyze satisfaction survey data in - time series forecasting - part 1 of e 8 data analysis interpretation and presentation 2 to analyze a case study?