Applied multivariate analysis of variance in experiment of randomized design

The general aim of an experimental design in this paper was to estimate the different treatments effects on the responses by statistical methods. The estimates must be averting biases and the random errors minimized as much as possible. We used multivariate analysis of variance (MANOVA) to analyze design of experiments for several responses. In this paper, we provided three fertilizers (mineral, humic, micro-elements) applied on Yellow Maize experiment. This experiment was conducted by completely randomized design (CRD). We tested four responses (Chlorophyll in paper, total ton / ha, paper area / cm2 and plant height / cm) together to find significant test between them. The partial correlations are between Chlorophyll in paper and total ton of 0.77727. The difference between first fertilizers (mineral) and 3rd fertilizers (micro-elements) are significantly different for the total ton

Modeling the trend of Iraqi GDP for 1970-2020

The study of economic growth indicators is of fundamental importance in estimating the effectiveness of economic development plans, as well as the great role it plays in determining appropriate economic policies in order to optimally use the factors that lead to the dynamics of growth in Iraq, especially during a certain period of time. The gross domestic product (GDP) at current prices), which is considered a part of the national accounts, which is considered as an integrated dynamic of statistics that produces in front of policy makers the possibility of determining whether the economy is witnessing a state of expansion or evaluating economic activity and its efficiency in order to reach the size of the overall economy. The research aims to determine the best and most efficient statistical model to be used in forecasting the GDP in Iraq based on time series data for the period from (1970-2020) years. Where the general trend models (Linear trend, Quadratic trend and Exponential Trend) were applied, and the three models were compared to choose the best model using some statistical criteria, including the Akiaki Information Standard (AIC) and Schwartz Standard (SBS). The results showed that the appropriate model is the Quadratic trend model, were predicting and forecasting values are close to the real values of the GDP series

Tuning parameter selectors for bridge penalty based on particle swarm optimization method

The bridge penalty is widely used as a penalty for selecting and shrinking predictors in regression models. Although its effectiveness is sensitive to the parameters you decide to use for shrinking and adjusting. The shrinkage and tuning parameters of the bridge penalty are chosen concurrently, and a continuous optimization process called particle swarm optimization is proposed as a means to do this. If implemented, the proposed method will greatly facilitate regression modeling with superior prediction performance. The results show that the proposed method is effective in comparison to other well-known methods, but this varies greatly depending on the simulation setup and the real data application

The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq

Cancer diseases are considered one of the most critical problems facing the world's countries, especially the State of Iraq. Many local and international reports indicated that the weapons used in wars and the accompanying nuclear and chemical radiation are among the most prominent reasons for the spread of cancerous diseases in Iraq. This study found that Gender has the highest discriminating power, whereas the Grade variable has the least discriminatory power. Similarly, Behavior has the highest discriminatory power, whereas the Government has the least biased power. It became clear that the third group (those with breast cancer) had the highest probability of the correct classification. The probability of correct classification reached 92%, followed by the second group with brain cancer, where the probability of correct classification was 64%. Finally, the first group with bladder cancer had the lowest probability of correct classification. We conclude that increasing the sample size has a significant impact on the correct classification of observations. The effects of these weapons were tremendously harmful to public health and the environment. Its effect persisted after many years, so three groups of cancer patients (bladder, brain, and breast cancer) were analyzed from 2012 to 2017 using a statistical method to analyze multivariate data. The results showed gender and the nature of the tumor (Behavior) have the highest discriminating power. The results were entirely satisfactory, as the discriminatory predictive capacity obtained a level of success of 72.2%