There is no “mantra” to simplify statistics and data analysis   for learning; however; statisticians at Statswork offer some keys to circumvent pitfalls and ensure smooth flow of your research work.  Here are some top tips to ease your research phase. First Review Descriptive Stats: Many folks do things the other way round; for example, they do complex analysis prior to analyzing the data.  Most of the time, descriptive statistics provide pivotal background for advanced analysis thereby providing clarity of interpretation . Second, Prune Data Before Analysis: Doing this helps you focus on analysis; you can manually delete unwanted variables after taking a backup or leverage the “Define Variable Sets” feature. Third, Refrain Analyzing The Master File: Ensure you work on a copy of the data. Normally, things may not go wrong but better be safe than sorry. You never know what can happen; hence, backup your master copy of data. Fourth, Anchor Hypothesis On Theory: Don’t

Reacquaint yourself with simple yet important data analysis tools to transition from abstract numbers to quantifiable success. In today’s digital era, we are inundated with information—it’s overwhelming. Leaders at the top helm make decisive decisions daily. Sorting through avalanche of data to assist management’s decision making is the key. Objective is to sort and  interpret  and report findings. Toward this end, tools or techniques for statistical data analyses achieve this objective. Buzzwords we hear today are big data, hadoop, and AI; however, rudimentary tools need to be deployed to their maximum capability, which is not the case in most firms. Propel your  data analysis   program by mastering the following 5 basic techniques. Review these mundane statistical techniques, yet so vital. Techniques Merits Demerits 1. Hypothesis Testing Hypothesis tests are used in multiple disciplines such as science, research, economics, etc. Popularly known as t-testing, h

Data Analysis Plan for Quantitative and Qualitative Research: Data Analytics is the quest of excavating an understanding or inference from unprocessed data via dedicated computer applications. These applications alter, shape, and model the data to infer deductions and ascertain patterns. Although Data Analytics can be uncomplicated, nowadays the term is most frequently used to define the study of huge capacities of data ( quantitative and qualitative data ) and/or high-speed data, which offers unique mathematical and data-juggling obstacles. Proficient data analytics pros who essentially possess a strong know-how in business statistics, are known as data scientists. Data Analytics is a generic word for any type of data manipulation that considers historical data over a time frame; however, as the volume of organizational data increases, the concept of data analytics is developing to support big data-capable systems. The dawn of big data dramatically altered the need f

With more and more companies using big data, demand for professional data analysts has witnessed exponential growth in the recent years. “Learners of data analysis and interpretation need to have an in-depth understanding of the subject along with the statistical acumen and working knowledge of tool sets to make significant progress in the field of data analytics” – Dr. Venkatesh, Statistician. To gain good expertise in Data analysis and interpretation, the beginner learner requires good foundation in basic statistics, and better skills of how to input, process and analyze data by employing a range of data analytical and visualization tools. The objectives of data analysis and interpretation are to manipulate the data, both qualitative as well as quantitative, to obtain utilizable information that is critical to making informed decisions. It involves the usage of many different methods and procedures that can, Describe and summarize data Evaluate and enhance qua

Statistics is an integral part of all research studies, a discipline that all researchers and scientists commonly use to analyze their data and arrive at the desired research findings and effectively present meaningful conclusions. Statistical techniques , presently, play a central part in the majority of scientific disciplines and are frequently utilized to support hypotheses in order to provide credibility to research methodology and conclusions arrived from these studies. Thus, many researchers and scholars are keenly interested In Statistics and its various methods and analytical techniques to ascertain the credibility and usefulness of the data and information collected from their respective research studies. This short guide provides essential information relating to important statistical concepts like sampling methods, the role of statistics in scientific research to facilitate them to conduct a well-designed statistical research. What is Statistics? Statistics

Significance of Statistics in Research Being an integral part of any research related to most subjects, Statistics and its importance in research have been recognized since long. Quality research is invariably in most topic cover portions of statistics. Statistics is linked to a wide range of research activities. This is where scholars feel the need for effective  Ph.D. statistics help  for pursuing their research. Apart from subject knowledge, researchers need to have expertise in statistical methods also. For validating the findings, statistical portions are being made mandatory by institutions across the globe. It is by having theoretical knowledge as well as skill in the application of statistics that researchers will be able to gain an extra edge, making  PhD statistics help  a vital factor. “The goal is to turn data into information, and information into insight.” – Carly Fiorina, former CEO, Hewlett-Packard Co. Conducting Statistical Tests and Interpreting Re

Significance of Quantitative Statistical Data Analysis Statistical Data analysis in quantitative research  generally involves varied statistical techniques such as regression analysis, multivariate analysis, significance testing, and so on. These can be efficiently performed by expert analysts having quantitative skills and extensive knowledge in statistics. Data can statistically be inferred only after performing the  Quantitative Data Analysis . You have to turn raw numbers into meaningful data in  Quantitative Data Analysis  by applying critical and rational thinking. As same figure within a data-set may be interpreted in different ways, it becomes vital to apply careful and fair judgment.  Data analysis in quantitative research  must be performed by professionals having relevant experience and skill. “Statistics is the grammar of science.” Karl Pearson Quantitative Research Methods for PhD Students – the Challenges: It is usual for dissertation committees to a

Statistical Regression analysis  provides an equation that explains the nature and relationship between the predictor variables and response variables. For a linear regression analysis, following are some of the ways in which inferences can be drawn based on the output of p-values and coefficients. While interpreting the p-values in  linear regression analysis in statistics , the p-value of each term decides the coefficient which if zero becomes a null hypothesis. A low p-value of less than .05 allows you to reject the null hypothesis. This could mean that if a predictor has a low p-value, it could be an effective addition to the model as the changes in the value of the predictor are directly proportional to the changes in the response variable. On the contrary, a p-value that is larger does not affect the model as in that case, the changes in the value of the predictor and the changes in the response variable are not directly linked. If you are to take an output

Tremendous amount of data has been collected by organizations all around the world thanks primarily to the World Wide Web! This collection is useless unless it is analyzed to see a pattern. It is important for decision makers, researchers and policy makers throughout industry, academia and Governments to integrate the huge collected data to suit their needs.  A good software  provider will seek to understand your organization’s unique needs and work with you to find the most cost-effective solution. How does one decide the best statistical software to use? There are questions of price, time available to learn to use it and most importantly the nature of data. Excel is quite a powerful instrument often used in biomedical research to combine large volumes of data. It does contain some  basic statistical analysis  and could serve for simple inferences. Data can be exported into an excel spreadsheet, making it easy to test or determine the dispersion of the data. Excel fits the

Publishing a research paper in a peer reviewed journal seems to be one of the daunting tasks that require tackling multiple challenges on a technical and personal level. Researchers without prior experience in publishing would have a tough time understanding the nuances of acing journal acceptance rates for their research work. It is advisable for the early stage researchers to seek the expertise of Statistical peer reviewers who can help in incorporating the nuances to significantly improve journal acceptance rates. Read on to know how the below mentioned factors handled by the expert Statistical peer review help s in journal acceptance. The Publisher Expert peer reviewers clearly understand the acceptance knacks and specifications of each publishing house and provide suggestions to modify the research paper accordingly to reap the best results. Expectation of the audience As these statistical peer reviewers have exceptional domain knowledge and exposure to the ind

The main purpose of this blog is to understand the Testing Group Differences using T-tests, ANOVA, and Non parametric Measures. Choosing the right test for your data analysis is a very difficult task particularly identifying the Different methods from testing group differences is the Biggest challenging task. It is important to have to in-depth Knowledge to understand and calculate T-tests, ANOVA, and Nonparametric, brief interpretation of the output. In order to choose the right statistical test, when analyzing the data from an experiment, we must have a good understanding of some basic statistical terms and concepts: Test for Normality Every data must follow certain distribution. But we have to find the appropriate distribution from goodness of fit test. So, our data is checked through each and every distribution. Hence, goodness of fit test is very tedious. This way of estimation of data is called by parametric tests. Parametric tests always give the reliable estimated

Where? When? Why? Who? What? How? Confused? Frustrated? Meet a consultant and get your data accurately analyzed Why consulting is a must? Quality statistical consulting assures you with high-quality research methods, effective data collection and precisely analyzed findings from your research. Why consulting is a must? Quality statistical consulting assures you with high-quality research methods, effective data collection services and precisely analyzed findings from your research. Become a master learner With the help of statistical consulting, you will be highly skilled in learning the most significant concepts of statistics. From the scholars who can transform your career life, you will learn how you can collect, summarize, interpret and analyze numerical data. The learning aspects you are taught may lead you to become a master learner because you get ideas from the most experienced scholars. Get assistance in the process of collecting data As collecti

Statistics are handled efficiently in myriad ways in the field of data analysis. Statistical analysis is a module of data analytics. A representative selection from a total population is drawn and the data collected is scrutinized so that the sample data speak for a set of items dealt with, in the framework of business intelligence. It involves a description of the nature of data that is to be analysed, drawing a parallel between the sample data selected and the underlying population, coming up with a model that recapitulates the understanding of how the sample data is in regard to the total population, substantiating the standpoint that the model is reliable, and making use of predictive analytics in order to enlist possible scenarios that make the study relevant, paving the way for further research. Demography and statistics are yet another link that is often explored, making utmost use of statistical data analysis services for the population study, research, administratio