Statistical Data Analysis Services

Big Data And Artificial Intelligence In Drug Discovery Drug discovery is a time consuming and multifaceted journey, with extraordinary insecurity that a drug can succeed. In drug development, the evolution of Big Data and Artificial Intelligence (AI) methodology has revolutionized the methods to block long-standing challenges. In Brief: The integration of big data and AI is making a significant difference in the discovery of a targeted drug. An overview of the currently available advanced methods for drug discovery using Big Data and AI and essential aspects of exploiting varieties of databases for drug discovery. Big Data In Drug Discovery Data can be cast-off as a tool to recognize formerly undiagnosed patients, even before their indicators are evident. By the use of algorithms and data mining , the research identifies high-risk entities, especially for less noticeable disease symptoms. Data mining is also the least hostile way to govern a diagnosis. The chal...

Here are some examples of real-world statistical analyses that don’t use p-values and significance testing Your results from a hypothesis test are statistically significant. Bravo! Are these results important? Not so. Significance not necessarily mean the results are practically significant in real world Statistical Analysis . In this blog post, I will list out few examples of real world statistical analyses that do not use p-value and significance testing. Before that, let us understand what exactly the p-value mean?                             Statswork is one among the country’s leader in providing  Data Analysis Services  and Statistical Consulting Services.  Contact Statswork for availing our services. P-value The same scenario as above have been faced by many data scientist when we talk about the p-value, isn’t it? Well, sometimes the simplest definition of p-value tend to ...

Factor Analysis The analysis of variance is not a mathematical theorem, but rather a convenient method of arranging the arithmetic.- Ronald Fisher The inexpensive Factor Analysis is a prominent statistical tool to identify a lot of underlying dormant factors. For more than a century it is used in psychology and also in a wide variety of situations. Factor analysis explains correlations among multiple outcomes as a result of one or more factors.   As it attempts to represent a set of variables by a smaller number, it involves data reduction.   It explores unexplained factors that represent underlying concepts that cannot be adequately measured by a single variable.   It is most popular nowadays in survey research where the responses to each question represent an outcome.   It is because multiple questions are often related and the underlying factor may influence the subject responses. The reduction technique of factor analysis in reducing a large number of...