Advantages of using Advanced Excel for a large data set

In recent years, big data has become a buzz word for them who are relying on digital technologies as it allows storage and analysis  of massive data. Here, big data includes both structured and unstructured data sets which require processing capabilities of modern tools. One of the most popular tools to handle big data is Advanced Excel and its relevant add-ons (Rose, Spinks, & Canhoto, 2015).

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Extraction of TGFβ1 protein information using UniProt

UniProt is a protein database containing information on the TGFβ1 protein. In order to obtain relevant information, the researcher needs to type in keywords in the search tab as shown in the image below. After clicking the entry link i.e. P01137 (entry id), a new page will open. Read more »

Using Data Envelopment Analysis (DEA) program for measuring efficiency

Measuring efficiency to benchmark the performance of firms and organisations is important. In one of the recent projects, the analyst found Statistical Package for Social Science (SPSS) and MS Excel to be unsuitable to process data collected to measure efficiency. Thus, application of specific efficiency testing software is important and data envelopment analysis (DEA) program is one of them.

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Advantages of using R statistical software for predictive modelling

Predictive modelling is a data driven, induction based modelling that is continuously used by big sized companies to gain useful insights into trends and risks budding in the future. The modelling on the basis of data extraction, cleansing and analysis helps in predicting the value of a target variable (Fortuny, Martens, & Provost, 2013). Most of the analytical softwares developed are used to efficiently understand how things move for an organisation as per trends indicated by a relevant factor. One of the software that helps in prediction is R, summarization and estimation of the target variable with respect to different factors (Varian, 2014). The software holds a wide scope to develop predictive models.

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Can you suggest me few topics in my area of research?

We generally request customers to finalize their research topic with their mentor and send it to us. But at times we can also suggest you topics if you require. It is advisable that you should send us as many research areas from your course and from the area of interest of your mentor, because you will be the one to represent the final draft to your university. Also visit our column Knowledge Tank, where we have published thousands of articles in different areas. This should help you finalise your research topic.

Variable management in STATA

Data entered in STATA can be classified either as numeric or string type. Associated with each type of data is its storage type i.e. the numbers are stored as byte, int, long, float, or double. STATA takes “float” as the default storage type for its variables. Similarly byte, int and long are usually used to hold integers. The table given below defines the storage type with minimum and maximum value for each variable along with byte size. Read more »

Creating a do-file

Do-file is an interface in Stata which allows the researcher to compile all the commands and results at one place. Once the commands are stored in do file, one do not need to enter the command again. One can simply run the do file and get all the results at once. Unlike SPSS wherein, the output sheet is automatically generated every time you run a test. All the commands and results in this file can be mailed together. Read more »

Correlation analysis using STATA

Correlation analysis is conducted to examine the relationship between dependent and independent variables. There are two types of correlation analysis in STATA.

  1. Pairwise correlation which treat each pair of variables separately and only includes observations which have valid values for each pair in the data set.
  2. The second type of correlation is the normal correlation which takes the entire data set as one and calculates the correlation for all valid values.

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