Measure-of-Position.pptx
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Quartiles divide a data set into four equal parts. The first quartile (Q1) is the median of the lower half of the data. The second quartile (Q2) is the median of the entire data set. The third quartile (Q3) is the median of the upper half of the data. To find the quartiles, the data must be sorted in ascending order and the values that divide it into four parts are the quartiles. The interquartile range is calculated as Q3 - Q1 and measures the spread of the middle 50% of the data. Examples are provided to demonstrate calculating quartiles and the interquartile range.
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This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Literature Review Basics and Understanding Reference Management.pptx
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
22CYT12-Unit-V-E Waste and its Management.ppt
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Low power architecture of logic gates using adiabatic techniques
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
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Generative AI leverages algorithms to create various forms of content
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Literature Review Basics and Understanding Reference Management.pptx
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Low power architecture of logic gates using adiabatic techniques
Low power architecture of logic gates using adiabatic techniques
Measure-of-Position.pptx
- 1. Measure of Position: Quartile ERIC D. VALLE Discussant
- 2. Measure of Position • It is a number that tells where score strands relative to the others in asset of data. • It is a measure whether a value is about the average, or whether its unusually high or low. • It is used for quantitative data that falls on some numerical scale. • It can be applied to ordinal variables.
- 3. Percentile Decile Quartile - Values that divide a set of data into 100 equal parts. - Values that divide a set of data into 10 equal parts. - Values that divide a set of data into 4 equal parts.
- 4. Quartiles of Ungrouped Data The quartiles are the score points which divide a distribution into four equal parts. 25% 50% 75% Median = Q2 Q1 Q3 25% of the data fall below the first quartile. 50% are below the second quartile 75% are less than the third quartile Interquartile Range The difference between Q3 and Q1
- 5. The owner of a coffee shop recorded the number of customer who came into his café each hour in a day. The result were 14, 10, 12, 9, 17, 5, 8, 9, 14, 10, 11. Find the lower quartile and upper quartile of the data. Arrange the scores in ascending order: n = 11 5 , 8 , 9 , 9 , 1 0 , 1 0 , 1 1 , 1 2 , 1 4 , 1 4 , 1 7 middle value The lower quartile is 9. The upper quartile is 14. To compute the interquartile range: IQR = Q3 – Q1 = 14 – 9 IQR = 5
- 6. Consider the set of scores in a quiz in Math 10 of Section Rizal: 11, 13, 14, 15, 15, 16, 19, 19, 20. Find Q1, Q2, Q3. Arrange the scores in ascending order: n = 9 1 1 , 1 3 , 1 4 , 1 5 , 1 5 , 1 6 , 1 9 , 1 9 , 2 0 middle value IQR = Q3 – Q1 = 19 – 13.5 IQR = 5.5 Q1 = 13 + 14 2 = 27 2 = 13.5 Q3 = 19 + 19 2 = 38 2 = 19