![]() ![]() This applet illustrates the Central Limit Theorem by allowing you to generate thousands of samples with various sizes n from a exponential, uniform, or Normal population distribution. More specifically, for a population of individual observations with mean μ and standard deviation σ, the Central Limit Threorem says that the means of samples of size n drawn from this population will approximate a Normal distribution whose mean is also μ and whose standard deviation is. The Central Limit Theorem says that the distribution of sample means of n observations from any population with finite variance gets closer and closer to a Normal distribution as n increases. Click "Show Normal curve" to compare this distribution with the Normal curve predicted by the Central Limit Theorem.Ĭlick the "Quiz Me" button to complete the activity. The key findings showed that the independent variables had a strong and significant relationship with the GWP.Choose a population distribution (Exponential, Uniform, or Normal) and a sample size, then click the button to generate 10,000 samples and plot the distribution of sample means. Data were analyzed using SPSS software in terms of inferential analysis. Quantitative methods used to emphasize the objective measurements and to identify how closed the relationship are. ![]() The survey was established for warehouse’s employees with superior or managerial position based in Malaysia. The relationship between government engagement, customer, supplier, manager, employee’s engagement, technology innovation and green warehouse practice has been analyzed. The purpose of the study is to investigate the factors of adopting green warehousing (GWH) with the main aim of exploring current green warehouse practice (GHP) in Malaysia and the factors affecting them. This study described about the green practice and the drivers in the Malaysia warehousing industry. Going green in logistics business operation has been the main initiatives to reduce the carbon footprint while generating profit. The suggested precise result and data received by IoT devices will not degrade, and all connected nodes will assist in resolving the issue. Component-based Blockchain will also significantly boost the system's scalability and security. However, because IoT devices have a central server, attackers target the system, use Distributed Denial of Service, and temper exploit its flaws. The system is built on an IoT sensor and associated sensors to detect the amount of alcohol in a driver's breath, gather data for accuracy, and make a judgment using machine learning. In the proposed model on the embedded Blockchain, devices are smartly controlled. This study offered a model and concept for preventing such human suffering by detecting, preventing, and informing the system using today's dominating technologies, such as IoT, Machine Learning, and Blockchain. Sometimes a person has drowsiness during driving, whether the person is drunk or not, so we have to check the eye position of the person with the help of IoT (internet of Things) devices. During the drunk and drive, there is a chance person will move very fast, so we have to check the acceleration speed of the vehicle. Drunk and driving alcohol is the momentous root cause of the accidental tragedy. According to WHO (World Health Organization) reports, 1.3 million people died in road traffic crashes, and in India, the total number of 449,002 accidents in the year 2019 led to 151,113 deaths and 451,361 injuries this will be increasing by the day. Road Accident is a significant concern in every county. ![]()
0 Comments
Leave a Reply. |