Department of Statistics

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Author: search.filters.author.Dr. Aamir SanaullahSubject: search.filters.subject.FA20

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    Statistical Identification of Factors Responsible for Rice Yield: Regression Analysis Approach
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) Muhammad Faheem Javaid; CIIT/FA19-BST-009/LHR; Dr. Aamir Sanaullah; LHR TP 9944
    The importance of agriculture in our economy is underscored by the fact that it provides essential food supplies to meet the needs of our growing population. In Pakistan, agriculture is a vital contributor to our economic growth, Accounting for 22.7% of the GDP and employing approximately 37.4% of the Labor force. The rice ranked at 3rd place with 5.03 billion metric tons production worldwide. Pakistan came at 9th place all over the world in producing milled rice with a production of 9.32 million metric tons, rice contributes 2.4% to the value addition and 0.5% to the GDP. In this study we have collected data of Rice yield from Agriculture Department with various factors that contribute in the yield of rice. Then we use regression approach to identify the factors that are contributing in the rice yield significantly and the factors that contributing insignificantly. From the multiple linear regression, we got the R2 = 58.1% and Adjusted R Squared = .553. And then we see that, some predictor variables were found to be statistically significant while others were not as they are important for the rice yield. We suspected multicollinearity and applied factor analysis to address it. Factor analysis is applied to the independent variables, generating factors that were then used in a multiple regression analysis. The results of this analysis showed an improvement over the previous model, by making factors from all the independent variables show that all the factors are now contributing significantly in the model and the R2 has also improved from 58.1% to 68.7%.
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    Estimation of Population Mean Using Estimator Based on Auxiliary Variable in Simple Random Sampling
    (2020-04-01) Umme Habiba; CIIT/FA19-BST-007/LHR; Dr. Aamir Sanaullah; LHR TP 9951
    Survey sampling is a well-established methodology employed to choose a sample of individuals from a larger population. The primary objective of this study is to estimate the population mean efficiently, by employing a comprehensive approach. To achieve this, we proposed a generalized estimator up to first degree of approximation by use of one auxiliary variable under simple random sampling framework. By deriving expressions for both bias and mean square error (MSE) of the proposed generalized estimator, we are able to ascertain its effectiveness. Remarkably, our analysis reveals that the proposed generalized estimator outperforms. Furthermore, we obtained the minimum MSE of the proposed estimator, and empirical studies demonstrated that our estimator outperform than the existing estimator found in the literature in terms of efficiency. Finally it is shown that the proposed generalized estimator efficiently work.