A Comparison between Normal and Non-Normal Data in Bootstrap

In the area of statistics, bootstrapping is a general modern approach to resampling methods. Bootstrapping is a way of estimating an estimator such as a variance when sampling from a certain distribution. The approximating distribution is based on the observed data. A set of observations is a population of independent and observed data identically distributed by resampling; the set is random with replacement equal in size to that of the observed data. The study starts with an introduction to bootstrap and its procedure and resampling. In this study, we look at the basic usage of bootstrap in statistics by employing R. The study discusses the bootstrap mean and median. Then there will follow a discussion of the comparison between normal and non-normal data in bootstrap. The study ends with a discussion and presents the advantages and disadvantages of bootstraps

Pengaruh Metode Bermain Peran Terhadap Kecerdasan Kinestetik Anak Usia 5-6 Tahun Di RA Nurul Fadilah Desa Bandar Setia Kab. Deli Serdang Tahun Ajaran 2019/2020

Penelitian ini dilaksanakan di Raudhatul Atfal Nurul Fadilah Desa Bandar Setia Kab, Deli Serdang. Pada Tanggal 06 sampai 17 juli 2020. Jenis penelitian ini adalah kuantitatif eksperimen dengan desain Quasi Eksperimental Design dengan tipe Non Equivalent Control Group Desain. Populasi berjumlah 32 orang anak, karena jumlah populasi kurang dari 100 maka penentuan sampel menggunakan teknik total sampling. Rumusan masalah penelitian ini: Bagaimana kecerdasan kinestetik anak, Bagaimana metode bermain peran, dan apakah terdapat pengaruh yang signifikan antara metode bermain peran terhadap kecerdasan kinestetik anak. Penelitian ini bertujuan untuk mengetahui : (1) kecerdasan kinestetik anak (2) metode bermain peran (3) Pengaruh yang signifikan antara metode bermain peran terhadap kecerdasan kinestetik anak usia 5-6 tahun di RA Nurul Fadilah Desa Bandar Setia Kab, Deli Serdang T.A 2019/2020. Hasil penelitian rata-rata kelas eksperimen pre test 55,125 dan rata-rata post test 79,56, dengan nilai tertinggi post test 93 dan nilai terendah post test 65. Kelas kontrol dengan rata-rata pre test 53,125 dan rata-rata nilai post test 73,87, dengan nilai tertinggi 87 dan terendah 65. Hasil uji hipotesis diperoleh thitung > ttabel yaitu 42,543> 2,145 dengan angka signifikan α= 0,05. Dengan demikian hipotesis Ho ditolah dan Ha diterima sehingga dinyatakan ada pengaruh yang signifikan antara metode bermain peran terhadap kecerdasan kinestetik anak usia 5-6 tahun di RA Nurul Fadilah Desa Bandar Setia Kab, Deli Serdang T.A 2019/202

Classification Simulation of RazakSAT Satellite

This study presents simulation of land cover classification for RazakSAT satellite. The simulation makes use of the spectral capability of Landsat 5 TM satellite that has overlapping bands with RazakSAT. The classification is performed using Maximum Likelihood (ML), a supervised classification method that is based on the Bayes theorem. ML makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and are estimated from the training pixels of a particular class. The accuracy of the classification for the simulated RazakSAT data is accessed by means of a confusion matrix. The results show that RazakSAT tends to have lower overall and individual class accuracies than Landsat mainly due to the unavailability of mid-infrared bands that hinders separation between different plant types

Kiertoa sulkemassa : typen ja fosforin virrat Suomen merialueiden kalankasvatuksessa

Rehevöityminen on Itämeren näkyvin ympäristöongelma. Itämereen päätyvät typpi- ja fosforivirrat lisäävät perustuotantoa, jonka seurauksena lajisto yksipuolistuu ja pohjalle vajoavan biomassan määrä kasvaa. Pohjan hajotustoiminta kuluttaa laajoilla alueilla vedestä kaiken käytössä olevan hapen, jonka seurauksena vain anaerobinen eliöstö pystyy näillä alueilla selviytymään. Kalan merkitys ihmisravintona kasvaa jatkuvasti. Muun eläintuotannon eettiset kysymykset ja kalaruoan terveellisyys ovat etenkin länsimaissa nostaneet kalan suosiota muiden eläintuotteiden kustannuksella. Yhä suurempi osa kulutetusta kalasta on kasvatettua, myös Suomessa. Kalankasvatuksen kuormitus Suomessa on pienentynyt viimeisen 15 vuoden ajan, pääasiassa rehujen kehityksen myötä. Kalankasvatuksesta aiheutuu kuitenkin edelleen paikallisesti merkittäviä rehevöittäviä ravinnepäästöjä, tuotannon tehokkuudesta huolimatta. Teolliseen ekologiaan sisältyvän teollisen metabolian periaatteiden mukaan teollisten prosessien ainevirtoja tulisi sulkea. Tässä tutkimuksessa tarkasteltiin ainevirta-analyysin avulla Suomen merialueiden kalankasvatusjärjestelmän keskimääräisiä vuotuisia typpi- ja fosforivirtoja vuosien 2004-2006 tuotantotietojen perusteella. Kalankasvatusjärjestelmään tulee typpeä 849 t ja fosforia 118 t kalojen rehusta. Ravinteet sitoutuvat kasvatettavaan kalaan tai kulkeutuvat ympäröivään vesistöön. Kaksi kolmasosaa kalankasvatusjärjestelmän ravinnepäästöistä päätyy kasvatusaltaasta suoraan veteen, kolmasosan sitoutuessa kasvuun. ImPACT-analyysillä todettiin vuosien 1980-2006 välillä kulutustottumusten ja teknologian vaikuttaneen merkittävästi kalankasvatuksen typen ja fosforin aiheuttamaan vesistökuormitukseen, sen sijaan väestön ja varallisuuden muutoksilla ei ollut merkitystä. Tutkimuksessa tarkasteltiin myös mahdollisia muutoksia ravinnevirroissa, jos rehun sisältämä Itämeren valuma-alueen ulkopuolelta peräisin olevasta kalasta tehty kalajauho korvattaisiin Itämeren kalasta tehdyllä kalajauholla. Menetelmällä saavutettaisiin huomattavat vähennykset Itämeren altaaseen päätyvissä ravinnevirroissa: typen osalta vähennys olisi 420 t, fosforin osalta nettokuormitus muuttuisi negatiiviseksi, -10 t. Ravinteet konsentroituisivat intensiivisen kasvatuksen alueille, mutta kyseessä on kustannustehokas keino Suomessa Itämereen kohdistuvan ravinnekuormituksen pienentämiseksi

Analysis of Maximum Likelihood Classification Technique on Landsat 5 TM Satellite Data of Tropical Land Covers

The aim of this paper is to carry out analysis of Maximum Likelihood (ML) on Landsat 5 TM (Thematic Mapper) satellite data of tropical land covers. ML is a supervised classification method which is based on the Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and can be estimated from the training pixels of a particular class. In this study, we used ML to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of ML

Classification Simulation of RazakSAT Satellite

This study presents simulation of land cover classification for RazakSAT satellite. The simulation makes use of the spectral capability of Landsat 5 TM satellite that has overlapping bands with RazakSAT. The classification is performed using Maximum Likelihood (ML), a supervised classification method that is based on the Bayes theorem. ML makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and are estimated from the training pixels of a particular class. The accuracy of the classification for the simulated RazakSAT data is accessed by means of a confusion matrix. The results show that RazakSAT tends to have lower overall and individual class accuracies than Landsat mainly due to the unavailability of mid-infrared bands that hinders separation between different plant types

Analysis of Maximum Likelihood Classification on Multispectral Data

The aim of this paper is to carry out analysis of Maximum Likelihood (ML)classification on multispectral data by means of qualitative and quantitative approaches. ML is a supervised classification method which is based on the Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and can be estimated from the training pixels of a particular class. In this study, we used ML to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of ML

The Effects of Haze on the Spectral and Statistical Properties of Land Cover Classification

Haze occurs almost every year in Malaysia and is caused by smoke which originates from forest fire in Indonesia. It causes visibility to drop, therefore affecting the data acquired for this area using optical sensor such as that on board Landsat satellite. The effects of haze on the data can be observed from the spectral and statistical properties of land cover classification. The work presented in this thesis is meant to analyse the statistical properties of land cover classification of hazy dataset. Maximum Likelihood (ML) was found to be a preferable classification scheme in which the effects of haze can be investigated. The study made use of hazy dataset that were simulated based on real haze spectral and statistical properties. By investigating these dataset, the spectral and statistical properties of the land classes can be systematically analysed, in which showing that haze modifies the class spectral signatures and statistical properties, consequently causing the data quality to decline

The Effects of Haze on the Accuracy of Maximum Likelihood Classification

This study aims to investigate the effects of haze on the accuracy of Maximum Likelihood classification. Data containing eleven land covers recorded from Landsat 5 TM satellite were used. Two ways of selecting training pixels were considered which are choosing from the haze-affected and haze-free data. The accuracy of Maximum Likelihood classification was computed based on confusion matrices where the accuracy of the individual classes and the overall accuracy were determined. The result of the study shows that classification accuracies declines with faster rate as visibility gets poorer when using training pixels from clear compared to hazy data

Comparative Analysis of Supervised and Unsupervised Classification on Multispectral Data

The aim of this study is to compare two methods of image classification, i.e. ML (Maximum Likelihood), a supervised method, and ISODATA (Iterative Self- Organizing Data Analysis Technique), an unsupervised method. The former is knowledge-driven, while the latter is data-driven. The former needs a priori knowledge about the study area but the latter does not. In practice, the former can classify land covers with a higher accuracy and therefore is more widely used but there have been very few attempts to investigate this. Here we use both methods in our study area, Selangor, Malaysia and compare the outcomes by means of qualitative and quantitative analyses to have a better understanding of the underlying reasons that drive the performance of both methods