Predicting Product Quality from Operating Conditions Based on Multinomial Logistic Regression
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Multinomial distribution
Stepwise regression
This paper firstly introduces variable grouping method and its application in constructing positive semi-definite multinomial;then infers the degree calculation formula of n variable Sn-2 multinomial and n variable Sn-3;lastly puts forward several excellent items of multinomial inequality
Multinomial distribution
Degree (music)
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Purpose The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate. Design/methodology/approach Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring. Findings The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently. Originality/value The PGLM with log link has not been used to monitor multinomial profiles in Phase I.
Multinomial distribution
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The statistic model, deterministic model and mixing model are usually proposed to qualitatively and quantitatively analyze the influence on environmental factors of observed value in the monitoring of dam safety. Moreover, the stepwise regression is considered to be able to achieve the optimum solution of these models. But in fact, the stepwise regression analysis may not be able to gain the solution value in some specific cases. This article presents shortcomings of stepwise regression analysis by overall comparison.
Stepwise regression
Statistic
Value (mathematics)
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2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (2021)
This paper analyzes the influencing factors of highway passenger and freight traffic, determines its influencing factors, and collects relevant statistical data from 117 different regions. Based on the principle of multiple linear regression method, first all variables are incorporated into the multiple regression equation for simulation. Second, integrate, demonstrate the applicability of the model, and then use the stepwise multiple regression method for model fitting. Based on this idea, the multiple linear regression model is constructed and forecasted for the highway passenger and freight volume. The results show that the stepwise multiple regression is effective. While the number of variables is greatly reduced and the calculation process is simplified, the model's fit is still good, and the problem of collinear between multiple variables is solved, and the regression coefficient of the variable is not consistent with the actual problem, and the result is predicted, It is also consistent with the actual situation and the applicability of the verification method, which can provide application references for road passenger and freight volume forecasting in other related areas.
Stepwise regression
Variables
Regression diagnostic
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Accurate prediction of urban water consumption is of great significance to the management and improvement of water supply system. In this thesis, through correlation analysis on 16 factors affecting total water consumption in Nanjing from 2005 to 2019, the factors among them that have more obvious influence are selected, and then they are fitted by stepwise linear regression model and nonparametric regression model respectively. By comparing the fitting results, it is found that nonparametric regression model has better prediction effect than stepwise regression model. The analysis results show that the prediction of urban water consumption is very important for the rational allocation of water resources.
Stepwise regression
Consumption
Water consumption
Predictive modelling
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Exploratory-factor analysis revealed that only one factor was found by principle component analysis.Four short forms of tests were created,the number of subtests increased by 3,4,5,6corresponding to factor loading of subtests.The same procedure was conducted using stepwise regression.It was found that correlation between four short forms based on factor analysis,and the full form was significant.It was also found that difference between all four short forms,and the full form was significant.Correlation between the four short forms based on stepwise regression,and the full form was significant.It was found that difference between the short forms which contained three and four subtests,and the full form was not significant.When60 was used as the pass line,total hit ratio of short forms based on factor analysis was comparatively higher than those based on stepwise regression.However,positive hit ratio was comparatively lower than those based on stepwise regression.When 70 was used as the pass line,total hit ratio of short forms based on factor analysis was comparatively higher than those based on stepwise regression.It can be concluded that features of short form including correlation with full form,coverage and hit ratio based on stepwise regression are better than that based on factor analysis.
Stepwise regression
Exploratory factor analysis
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The aim of this work is to examine multinomial logistic models when the response variable can assume three levels, generalizing a previous work of logistic models with binary response variables. We also describe some related models: The null, complete, and saturated models. For each model, we present and prove some theorems concerning to the estimation of the corresponding parameters with details that we could not find in the current literature.
Multinomial distribution
Multinomial probit
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The aim of this work is to examine multinomial logistic models when the response variable can assume three levels, generalizing a previous work of logistic models with binary response variables. We also describe some related models: The null, complete, and saturated models. For each model, we present and prove some theorems concerning to the estimation of the corresponding parameters with details that we could not find in the current literature.
Multinomial distribution
Variables
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An improved multiple stepwise regression analysis for the quantitative structureactivity relationship was developed, so as to simplify the computation and save the computer time. The method is different from the usual stepwise regression analysis, and the all-equation approach in multiple regression analysis.
Stepwise regression
Regression diagnostic
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Objective Measurement of VFA of female college students,and on this based on the prediction equation,and the establishment of predictive equation on this basis.Methods: 62 students 18 ~ 22-year-old female morphological index and VFA measurements.Results Taking the morphological index as the independent variable,and female university students VFA as dependent variable,put them in stepwise regression analysis.Conclusion Points out two regression mode are y^=-130.624+2.316×Waist circumference,y^=-53.867+2.482×Waist circumference-0.539×Height.
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