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• W256380213 abstract "Oftentimes, application of regression models to analyse cereals data is limited to estimating and predicting crop production or yield. The general approach has been to fit the model without much consideration of the problems that accompany application of regression models to real life data, such as collinearity, models not fitting the data correctly and violation of assumptions. These problems may interfere with applicability and usefulness of the models, and compromise validity of results if they are not corrected when fitting the model. We applied regression models and diagnostics on national and household data to model availability of main cereals in Lesotho, namely, maize, sorghum and wheat. The application includes the linear regression model, regression and collinear diagnostics, Box-Cox transformation, ridge regression, quantile regression, logistic regression and its extensions with multiple nominal and ordinal responses. The Linear model with first-order autoregressive process AR(1) was used to determine factors that affected availability of cereals at the national level. Case deletion diagnostics were used to identify extreme observations with influence on different quantities of the fitted regression model, such as estimated parameters, predicted values, and covariance matrix of the estimates. Collinearity diagnostics detected the presence of more than one collinear relationship coexisting in the data set. They also determined variables involved in each relationship, and assessed potential negative impact of collinearity on estimated parameters. Ridge regression remedied collinearity problems by controlling inflation and instability of estimates. The Box-Cox transformation corrected nonconstant variance, longer and heavier tails of the distribution of data. These increased applicability and usefulness of the linear models in modeling availability of cereals. Quantile regression, as a robust regression, was applied to the household data as an alternative to classical regression. Classical regression estimates from ordinary least squares method are sensitive to distributions with longer and heavier tails than the normal distribution, as well as to outliers. Quantile regression estimates appear to be more efficient than least squares estimates for a wide range of error term distribution. We studied availability of cereals further by categorizing households according to availability of different cereals, and applied the logistic regression model and its extensions. Logistic regression was applied to model availability and non-availability of cereals. Multinomial logistic regression was applied to model availability with nominal multiple categories. Ordinal logistic regression was applied to model availability with ordinal categories and this made full use of available information. The three variants of logistic regression model gave results that are in agreement, which are also in agreement with the results from the linear regression model and quantile regression model." @default.
• W256380213 created "2016-06-24" @default.
• W256380213 creator A5072861066 @default.
• W256380213 date "2012-01-01" @default.
• W256380213 modified "2023-09-26" @default.
• W256380213 title "Statistical modelling of availability of major food cereals in Lesotho : application of regression models and diagnostics." @default.
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• W256380213 cites W1490229695 @default.
• W256380213 cites W1501835916 @default.
• W256380213 cites W1528905581 @default.
• W256380213 cites W1575013331 @default.
• W256380213 cites W1592680957 @default.
• W256380213 cites W1603714290 @default.
• W256380213 cites W164158064 @default.
• W256380213 cites W1668720348 @default.
• W256380213 cites W1691300750 @default.
• W256380213 cites W18129694 @default.
• W256380213 cites W195784277 @default.
• W256380213 cites W1964894347 @default.
• W256380213 cites W1968167212 @default.
• W256380213 cites W1973948212 @default.
• W256380213 cites W1977994906 @default.
• W256380213 cites W1980703241 @default.
• W256380213 cites W1982392243 @default.
• W256380213 cites W1984363050 @default.
• W256380213 cites W1988791836 @default.
• W256380213 cites W1989898472 @default.
• W256380213 cites W1992015234 @default.
• W256380213 cites W1992129502 @default.
• W256380213 cites W1994444215 @default.
• W256380213 cites W1996640786 @default.
• W256380213 cites W1996905516 @default.
• W256380213 cites W1998613841 @default.
• W256380213 cites W2002374079 @default.
• W256380213 cites W2020214980 @default.
• W256380213 cites W2020851265 @default.
• W256380213 cites W2024424774 @default.
• W256380213 cites W2025837725 @default.
• W256380213 cites W2027506843 @default.
• W256380213 cites W2031687681 @default.
• W256380213 cites W2040317477 @default.
• W256380213 cites W2041844305 @default.
• W256380213 cites W2046033161 @default.
• W256380213 cites W2047028564 @default.
• W256380213 cites W2047827159 @default.
• W256380213 cites W2048245744 @default.
• W256380213 cites W2049667592 @default.
• W256380213 cites W2053834050 @default.
• W256380213 cites W2054038456 @default.
• W256380213 cites W2057154121 @default.
• W256380213 cites W2059053591 @default.
• W256380213 cites W2060109619 @default.
• W256380213 cites W2060758175 @default.
• W256380213 cites W2061960194 @default.
• W256380213 cites W2063405905 @default.
• W256380213 cites W2066796151 @default.
• W256380213 cites W2073968193 @default.
• W256380213 cites W2075754144 @default.
• W256380213 cites W2077505780 @default.
• W256380213 cites W2077866122 @default.
• W256380213 cites W2083918790 @default.
• W256380213 cites W2097679562 @default.
• W256380213 cites W2106316381 @default.
• W256380213 cites W2122450421 @default.
• W256380213 cites W2129249398 @default.
• W256380213 cites W2142635246 @default.
• W256380213 cites W2145147745 @default.
• W256380213 cites W2147646644 @default.
• W256380213 cites W214995755 @default.
• W256380213 cites W2152701363 @default.
• W256380213 cites W2266995761 @default.
• W256380213 cites W2316424545 @default.
• W256380213 cites W2380161250 @default.
• W256380213 cites W2466564333 @default.
• W256380213 cites W2518089210 @default.
• W256380213 cites W2592974517 @default.
• W256380213 cites W2611147814 @default.
• W256380213 cites W2767905780 @default.
• W256380213 cites W2797021786 @default.
• W256380213 cites W2797431976 @default.
• W256380213 cites W2797532987 @default.
• W256380213 cites W2798102897 @default.
• W256380213 cites W2800289289 @default.
• W256380213 cites W2800963611 @default.
• W256380213 cites W281649968 @default.
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• W256380213 cites W3123845958 @default.
• W256380213 cites W3149745985 @default.
• W256380213 cites W959948855 @default.
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• W256380213 hasPublicationYear "2012" @default.
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