discriminant function
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The present paper deals with use of discriminant function analysis for developing wheat yield forecast model for Kanpur (India). Discriminant function analysis is a technique of obtaining linear/Quadratic function which discriminates the best among populations and as such, provides qualitative assessment of the probable yield. In this study, quantitative forecasts of yield have been obtained using multiple regression technique taking regressors as weather scores obtained through discriminant function analysis. Time series data of 30 years (1971-2000) have been divided into three categories: congenial, normal and adverse, based on yield distribution. Taking these three groups as three populations, discriminant function analysis has been carried out. Discriminant scores obtained from this have been used as regressors in the modelling. Various strategies of using weekly weather data have been proposed. The models have been used to forecast yield in the subsequent three years 2000-01 to 2002-03 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

This study aims to determine what factors influence learning achievement. This study aimed to create a discriminant model of learning achievement from the influencing factors. The object of this research is the students of MI Nurul Iman, South Tangerang City. The variables used are six variables. The dependent variable in this study is the average report card, while the independent variables are motivation (X1), Learning Methods (X2), Teacher Competence (X3), Parental Environment (X3), School Infrastructure (X5), Community Environment (X6), which used as many as 56 samples. Based on the results of the output of the discriminant model using the SPSS version 21 program, it shows that the factors that affect student learning outcomes are School Facilities (X5). The discriminant model formed to determine the discriminant function is from the factors that affect learning achievement. Apart from 1, these variables are not used. Then the following function is obtained: D = -3,980 + 0,369X5

The purpose of the study was to determine the peculiarities of motor skills development in boys aged 14-15. Materials and methods. The study participants were boys aged 14 (n=20) and 15 (n=20). The children and their parents were fully informed about all the features of the study and gave their consent to participate in the experiment. To solve the tasks set, the following research methods were used: study and analysis of scientific and methodological literature; pedagogical observation, timing of training tasks; pedagogical experiment, methods of mathematical statistics, discriminant analysis, nearest neighbor analysis. Results. The study made an assumption about a significant influence of the modes of alternating exercise repetitions and the rest interval on the effectiveness of motor skills development in boys aged 14 and 15. The standardized canonical discriminant function coefficients helped to determine age peculiarities and the peculiarities of influence of exercise modes on the effectiveness of motor skills development. They showed that the components of motor fitness are a priority in developing motor skills. The structure canonical discriminant function coefficients indicate the importance of movement control skills for mastering the entire exercise. Conclusions. Discriminant analysis revealed the peculiarities of motor skills development in boys aged 14 and 15, depending on age and exercise modes. With the first exercise mode, boys aged 15 master the first, second, and fourth series of training tasks more quickly. Boys aged 14 – the sixth series (exercise mode: 6 repetitions, rest interval of 60 s). With the second exercise mode, boys aged 14 master the first and fourth series of training tasks more quickly. Boys aged 15 – the second series (exercise mode: 12 repetitions, rest interval of 60 s). The coordinates of centroids for four groups indicate a significant difference in the influence of exercise repetition modes on the number of repetitions required for motor skills development in boys aged 14-15 during physical education classes. The results of group classification show that 87.5% of the original grouped cases were classified correctly.

In this paper, spatial data specified by auto-beta models is analysed by considering a supervised classification problem of classifying feature observation into one of two populations. Two classification rules based on conditional Bayes discriminant function (BDF) and linear discriminant function (LDF) are proposed. These classification rules are critically compared by the values of the actual error rates through the simulation study.

In the present paper, an application of discriminant function analysis of weather variables (minimum & maximum temperature, Rainfall, Rainy days, Relative humidity 7 hr & 14 hr, Sunshine hour and Wind velocity )for developing suitable statistical models to forecast pigeon-pea yield in Faizabad district of Eastern Uttar Pradesh has been demonstrated. Time series data on pigeon-pea yield for 22 years (1990-91 to 2011-12) have been divided into three groups, viz., congenial, normal, and adverse based on de-trended yield distribution. Considering these groups as three populations, discriminant function analysis using weekly data on eight weather variables in different forms has been carried out. The sets of discriminant scores obtained from such analysis have been used as regressor variables along with time trend variable and pigeon-pea yield as regressand in development of statistical models. In all nine models have been developed. The forecast yield of pigeon-pea have been obtained from these models for the year 2009-10, 2010-11 and 2011-12, which were not included in the development of the models. The model 4 and 9 have been found to be most appropriate on the basis of R2adj, percent deviation of forecast, percent root mean square error (%RMSE) and percent standard error (PSE) for the reliable forecast of pigeon-pea yield about two and half months before the crop harvest.

The performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models obtained at different weeks was compared using Adj R2, PRESS (Predicted error sum of square), number of misclassifications and forecasts were compared using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. Ordinal logistic regression based approach was found to be better than discriminant function analysis approach.  

Discriminant function analysis technique using Bayesian approach has been attempted for wheat forecasting in Kanpur district of Uttar Pradesh, India both qualitatively and quantitatively. Crop yield data and weekly weather data on temperature (maximum and minimum), relative humidity (maximum and minimum), rainfall for 16 weeks of the crop cultivation have been used in the study. These data have been utilized for model fitting and validation. Crop years were divided into two and three groups based on the de-trended yield. Crop yield forecast models have been developed using posterior probabilities calculated through Bayesian approach in stepwise discriminant function analysis along with year as regressors for different weeks. Suitable strategy has been used to solve the problem of number of variables more than number of data points. Performance of the models obtained at different weeks was compared using Adjusted R2, PRESS (Predicted error sum of square), number of misclassifications. Forecasts were evaluated using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. The result shows that the model based on three groups case perform better. The performance of the proposed Bayesian discriminant function analysis technique approach was better as compared to existing discriminant function analysis score based approach both qualitatively and quantitatively.

Abstract Purpose The study was designed to evaluate the value of left atrial (LA) sphericity (LASP) in the identification of patients with atrial fibrillation (AF) who had prior ischemic stroke. The secondary aim was to investigate the possibility of improving stroke risk assessment based on six geometrical variables of LA. Methods This prospective observational study involved 157 patients: 74 in the stroke group and 83 in the control. All patients had cardiac computed tomography (CT) performed to analyze LA volume and dimensions. LASP and the discriminant function of six geometrical measurements were calculated. Results Multivariate logistic regression analysis showed a significant association of stroke with and gender, diabetes, CHA2DS2-VASc score, LA anteroposterior diameter, and LA sphericity. Patients with prior stroke had lower LASP than those without (66.6 ± 10.3% vs. 70.5 ± 7%; p = 0.0062). The most accurate identification of patients with a history of ischemic stroke was achieved by using a function of six geometrical measurements, the sphericity and volume coefficient. The C-statistic was higher for the above discriminant function (0.7273) than for LASP (0.3974). The addition of the discriminant function to the CHA2DS2-VASc score increased the performance of the risk score alone. Conclusion LASP is associated with prior stroke in AF patients. The proposed new formula for identification of AF patients who are at risk of stroke, based on geometrical measurements of LA, is superior to the basic LASP in identification of AF patients with a history of stroke.