Top Mathematics discussions
NishMath - #statistics
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@Math Blog
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medium.com
, Math Blog
Percentages are a fundamental concept in mathematics, representing a fraction with a denominator of 100. The term "percent" comes from the Latin phrase "per centum," meaning "by the hundred". A percentage is denoted by the symbol %, and is used to express a part of a whole. For example, if a student scores 65 percent on a test, it means they obtained 65 marks for every 100 marks. Understanding percentages is crucial as they frequently appear in daily life, from calculating discounts to understanding statistics.
Percentages offer a standardized way to compare different quantities or proportions. To convert a fraction to a percentage, the goal is to express the fraction with a denominator of 100. If David secures 475 marks out of 500, this can be converted to a percentage by dividing both the numerator and the denominator by 5, resulting in 95/100, or 95%. Conversely, 9% is equivalent to 9/100. Visual representations can also aid in understanding percentages, such as imagining a battery made up of 100 small cells, where each cell represents 1%. If all cells are charged then the battery is at 100%. In addition to understanding percentages, other mathematical concepts like linear regression are important in more advanced applications. Linear regression is a fundamental machine learning model used to find correlations between variables and make predictions. For instance, it can be used to predict ice cream sales based on temperature data. The model identifies a relationship between the input feature (temperature) and the target feature (ice cream sales) and uses a general line to make predictions. The equation of this line, f(x) = mx + b, where 'm' is the slope and 'b' is the y-intercept, helps in understanding how changes in the input feature affect the predicted output. Recommended read:
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Haden Pelletier@Towards Data Science
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Recent discussions in statistics highlight significant concepts and applications relevant to data science. A book review explores seminal ideas and controversies in the field, focusing on key papers and historical perspectives. The review mentions Fisher's 1922 paper, which is credited with creating modern mathematical statistics, and discusses debates around hypothesis testing and Bayesian analysis.
Stephen Senn's guest post addresses the concept of "relevant significance" in statistical testing, cautioning against misinterpreting statistical significance as proof of a genuine effect. Senn points out that rejecting a null hypothesis does not necessarily mean it is false, emphasizing the importance of careful interpretation of statistical results. Furthermore, aspiring data scientists are advised to familiarize themselves with essential statistical concepts for job interviews. These include understanding p-values, Z-scores, and outlier detection methods. A p-value is crucial for hypothesis testing, and Z-scores help identify data points that deviate significantly from the mean. These concepts form a foundation for analyzing data and drawing meaningful conclusions in data science applications. Recommended read:
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Amir Najmi@unofficialgoogledatascience.com
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Data scientists and statisticians are continuously exploring methods to refine data analysis and modeling. A recent blog post from Google details a project focused on quantifying the statistical skills necessary for data scientists within their organization, aiming to clarify job descriptions and address ambiguities in assessing practical data science abilities. The authors, David Mease and Amir Najmi, leveraged their extensive experience conducting over 600 interviews at Google to identify crucial statistical expertise required for the "Data Scientist - Research" role.
Statistical testing remains a cornerstone of data analysis, guiding analysts in transforming raw numbers into actionable insights. One must also keep in mind bias-variance tradeoff and how to choose the right statistical test to ensure the validity of analyses. These tools are critical for both traditional statistical roles and the evolving field of AI/ML, where responsible practices are paramount, as highlighted in discussions about the relevance of statistical controversies to ethical AI/ML development at an AI ethics conference on March 8. Recommended read:
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admin@ICNAAM 2025
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References:
ICNAAM 2025
, medium.com
The International Conference of Numerical Analysis and Applied Mathematics (ICNAAM) 2025 will feature a symposium on Statistical Modeling and Data Analysis. The event, organized by Luis M. Grilo from the University of Évora and the Research Centre for Mathematics and Applications in Portugal, aims to gather researchers from various fields with expertise in statistical models and data analysis. Academics, professionals, and students interested in these areas are encouraged to submit original, unpublished results for peer review.
Applications with real-world data are particularly welcome, spanning disciplines such as Health Sciences, Natural and Life Sciences, Social and Human Sciences, Economics, Engineering, Education, Sports, and Tourism. The conference aims to foster collaboration and knowledge sharing within the international Numerical and Applied Mathematics community. It is organized with the cooperation of the European Society of Computational Methods in Sciences and Engineering (ESCMCE). Recommended read:
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Megan Murphy@Stattr@k
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References:
Stattr@k
The Causality in Statistics Education Award, established by Judea Pearl, is seeking nominations. This award acknowledges the growing importance of incorporating causal inference into undergraduate and lower-division graduate statistics courses. The award provides a $5,000 cash prize annually, and nominations are due by April 5th. Further details regarding selection criteria and nomination requirements can be found on the American Statistical Association's STATtr@k website.
In other news, the Carnival of Maths 237, which rounds up various mathematics blog posts, is now available online. A mathematics conference organized by DEA SCUOLA is scheduled for secondary school mathematics teachers which aims to enhance mathematics teaching through innovative strategies and the integration of AI. Musically, Day 30 of a practice series features bass drum exercises from Mark Guiliana's book, highlighting complex coordination challenges in alternating snare and bass drum strokes. Recommended read:
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