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EEE / Re: ডেঙ্গু জ্বর, যা জানতে ও মানতে হবে
« on: July 16, 2019, 01:57:54 PM »
Thanks for the information
News:
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Teaching & Research Forum / How similar to data mining is statistics – and how different?
« on: April 08, 2018, 04:05:26 PM »
Both data mining and statistics are related to learning from data. They are all about discovering and identifying structures in them, thus aimed at turning data to information. And although the aims of both these techniques overlap, they have different approaches.
Statistics is only about quantifying data. While it uses tools to find relevant properties of data, it is a lot like math. It provides the tools necessary for data mining.
Data mining, on the other hand, builds models to detect patterns and relationships in data, particularly from large data bases.
To demystify this further, here are some popular methods of data mining and types of statistics in data analysis.
Statistics is only about quantifying data. While it uses tools to find relevant properties of data, it is a lot like math. It provides the tools necessary for data mining.
Data mining, on the other hand, builds models to detect patterns and relationships in data, particularly from large data bases.
To demystify this further, here are some popular methods of data mining and types of statistics in data analysis.
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A Good CV/Resume / Re: How to make an effective video CV
« on: April 08, 2018, 03:41:31 PM »
Thanks for sharing the links
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Teaching & Research Forum / Ten simple rules for structuring paper
« on: October 31, 2017, 08:24:50 PM »
Good scientific writing is essential to career development and to the progress of science. A well-structured manuscript allows readers and reviewers to get excited about the subject matter, to understand and verify the paper’s contributions, and to integrate these contributions
into a broader context. However, many scientists struggle with producing high-quality manuscripts and are typically untrained in paper writing. Focusing on how readers consume information, we present a set of ten simple rules to help you communicate the main idea of your paper. These rules are designed to make your paper more influential and the process of writing more efficient and pleasurable.
Rule 1: Focus your paper on a central contribution, which you communicate in the title
Rule 2: Write for flesh-and-blood human beings who do not know your work
Rule 3: Stick to the context-content-conclusion (C-C-C) scheme
Rule 4: Optimize your logical flow by avoiding zig-zag and using parallelism
Rule 5: Tell a complete story in the abstract
Rule 6: Communicate why the paper matters in the introduction
Rule 7: Deliver the results as a sequence of statements, supported by figures, that connect logically to support the central contribution
Rule 8: Discuss how the gap was filled, the limitations of the interpretation, and the relevance to the field
Rule 9: Allocate time where it matters: Title, abstract, figures, and outlining
Rule 10: Get feedback to reduce, reuse, and recycle the story
Details of the rule is in attachment.
into a broader context. However, many scientists struggle with producing high-quality manuscripts and are typically untrained in paper writing. Focusing on how readers consume information, we present a set of ten simple rules to help you communicate the main idea of your paper. These rules are designed to make your paper more influential and the process of writing more efficient and pleasurable.
Rule 1: Focus your paper on a central contribution, which you communicate in the title
Rule 2: Write for flesh-and-blood human beings who do not know your work
Rule 3: Stick to the context-content-conclusion (C-C-C) scheme
Rule 4: Optimize your logical flow by avoiding zig-zag and using parallelism
Rule 5: Tell a complete story in the abstract
Rule 6: Communicate why the paper matters in the introduction
Rule 7: Deliver the results as a sequence of statements, supported by figures, that connect logically to support the central contribution
Rule 8: Discuss how the gap was filled, the limitations of the interpretation, and the relevance to the field
Rule 9: Allocate time where it matters: Title, abstract, figures, and outlining
Rule 10: Get feedback to reduce, reuse, and recycle the story
Details of the rule is in attachment.
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Teaching & Research Forum / If you want to be a writer...
« on: November 05, 2016, 06:48:49 PM »
If you want to be a writer then you can follow the link for insperational talk
https://wri.tt/blog/6-ted-talks-that-will-expand-your-mind-and-make-you-a-better-writer
https://wri.tt/blog/6-ted-talks-that-will-expand-your-mind-and-make-you-a-better-writer
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Teaching & Research Forum / Data mining
« on: November 01, 2016, 10:43:07 AM »
If anyone interested on Decision Tree
http://www.datasciencecentral.com/profiles/blogs/price-optimisation-using-decision-tree-regression-tree
http://www.datasciencecentral.com/profiles/blogs/price-optimisation-using-decision-tree-regression-tree
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Teaching & Research Forum / Re: List of Journals removed by SCOPUS from their index
« on: October 27, 2016, 11:29:24 PM »
Can you please attach list of Scopus and ISI indexed journel?
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Teaching & Research Forum / Re: List of Journals removed by SCOPUS from their index
« on: October 27, 2016, 11:26:25 PM »
Thanks for sharing
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Teaching & Research Forum / international-prize-in-statistics
« on: October 20, 2016, 09:56:22 PM »10
Teaching & Research Forum / Re: an interesting math
« on: October 20, 2016, 09:55:12 PM »
Thank you sir for sharing interesting matg
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Teaching & Research Forum / All You Need to Know about Research Methodology
« on: September 25, 2016, 12:37:32 PM »12
Teaching & Research Forum / Problems Using Data Mining to Build Regression Models
« on: September 22, 2016, 06:52:30 PM »13
Teaching & Research Forum / Steps of Research
« on: September 21, 2016, 02:01:20 PM »
Research Process
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Teaching & Research Forum / Tobit Regression
« on: March 06, 2016, 06:27:27 PM »
The Tobit model, also called a censored regression model, is designed to estimate linear relationships between variables when there is either left- or right- or both censoring in the dependent variable (also known as censoring from below and above, respectively).
When dependent variable has a certain limit then to predict this dependent variable, need to use Tobit regression model.
Suppose dependent variable is performance which lies between 0 to 1. In this case, to predict performance better to use Tobit regression.
When dependent variable has a certain limit then to predict this dependent variable, need to use Tobit regression model.
Suppose dependent variable is performance which lies between 0 to 1. In this case, to predict performance better to use Tobit regression.
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Teaching & Research Forum / Type of Regression Analysis
« on: March 05, 2016, 12:59:16 PM »
Regression analysis is a most commonly use statistical tools for modelling or forecasting.
What is Regression Analysis?
Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables. For example, relationship between income (independent variable) and expenditure (dependent variable) is best studied through regression.
Why do we use Regression Analysis?
There are multiple benefits of using regression analysis. They are as follows:
1. It indicates the significant relationships between dependent variable and independent variable.
2. It indicates the strength of impact of multiple independent variables on a dependent variable.
Which type of Regression analysis is appropriate for your data?
Linear and Logistic regressions are usually the common techniques people learn in regression analysis. Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions.
Based on the nature of dependent variable, technique of regression analysis is varied. Types of regression analysis are Linear Regression, Logistic Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Bayesian regression, Jackknife regression and so on .
What is Regression Analysis?
Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables. For example, relationship between income (independent variable) and expenditure (dependent variable) is best studied through regression.
Why do we use Regression Analysis?
There are multiple benefits of using regression analysis. They are as follows:
1. It indicates the significant relationships between dependent variable and independent variable.
2. It indicates the strength of impact of multiple independent variables on a dependent variable.
Which type of Regression analysis is appropriate for your data?
Linear and Logistic regressions are usually the common techniques people learn in regression analysis. Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions.
Based on the nature of dependent variable, technique of regression analysis is varied. Types of regression analysis are Linear Regression, Logistic Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Bayesian regression, Jackknife regression and so on .
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