Differentiation of parameters a, b, c and d of the pro- posed **regression** Eq. (3) of gluten thermal expansion is shown in Fig. 6. To facilitate interpretation of the results, some general information about differences in the behaviour of wet gluten of two examined cultivars during heating at 110°C should be taken into consideration. At this tempe- rature, the volume increase of strong gluten (cv. Torka) was very slow and was of linear character. That was the reason why it was impossible to calculate the values of parameters a, b and c (characteristic for hyperbolic expansion) for the lowest temperature. The **regression** **equation** in this case contained only one parameter, d, which indicated linearity of gluten expansion. For all other experimental combina- tions, the course of gluten expansion was compliant with the proposed model.

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In this paper, we focus on the impressions that people gain from reading articles in Japanese newspapers, and we propose a method for extracting and quantifying these impressions in real numbers. The target impressions are limited to those represented by three bipo- lar scales, “Happy – Sad,” “Glad – Angry,” and “Peaceful – Strained,” and the strength of each impression is computed as a real num- ber between 1 and 7. First, we implement a method for computing impression values of articles using an impression lexicon. This lexicon represents a correlation between the words appearing in articles and the influence of these words on the readers’ impressions, and is created from a newspaper database us- ing a word co-occurrence based method. We considered that some gaps would occur be- tween values computed by such an unsuper- vised method and those judged by the readers, and we conducted experiments with 900 sub- jects to identify what gaps actually occurred. Consequently, we propose a new approach that uses **regression** equations to correct im- pression values computed by the method. Our investigation shows that accuracy is improved by a range of 23.2% to 42.7% by using regres- sion equations.

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agreement analysis also suggest that the 20-m multistage shuttle run test can be applied for use with the studied population. The results suggest that the application of the present form of 20- meter multistage shuttle run test be justified in the studied population. For better prediction of VO 2max a new **equation** has been developed based on present data.

Other possible limitations of our study also should be mentioned. First, in addition to the factors related to the retrospective nature of the present study, the discrepancy between the Mayo Clinic–derived formula and the **regression** **equation** used by the Beijing Eye Study may reflect ethnicity- related differences between the two populations, differences in lumbar puncture technique, or unknown medical or environmental factors. Second, blood pressure in our sample was at times measured several days from the determination of the lumbar CSFP. Third, blood pressure–lowering medication might have had a different effect on blood pressure and CSFP. Fourth, regardless of the accuracy of noninvasive, calculated CSFP estimates, a major issue remains: none of these approaches, even if accurate, measure the CSFP of the perioptic subarachnoid space, which is the critical measure- ment contributing to the translaminar pressure differential. Anatomic and physiologic studies have suggested that the orbital subarachnoid space is compartmentalized, and that clinically measured CSFP may not in fact translate to the perioptic subarachnoid CSFP. 24 This would imply that the

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obtained interms of H and CSP. It can be predicted the compact structure, wrapping fibers and higher centrifugal force because of higher rotor speeds are the responsible for obtaining better CSP and hairiness values. For each quality parameters in relationship with variation of rotor speeds both coefficient of determination and single linear **regression** analysis were also established. At last using all the parameters a multiple **regression** **equation** was established.

CIRCULATING RED CELL VOLUME PLOTTED AGAINST BODY DENSITY OF FORTY-TWO MEN AND TWENTY WOMEN Shown are the regression equation and correlation coefficient, "r." See text for regression equ[r]

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ABSTRACT: Surface roughness is one of the main characteristics of a work piece in the quality control process. Several parameters such as cutting tool material and geometry, cutting parameters, work piece material properties, machine tool and coolant type affect the surface quality. An important task of process planners is the proper selection of three main cutting parameters: cutting speed, feed rate, and depth of cut in order to have not only low surface roughness, but also to perform the process within a reasonable amount of time. In this paper, using full factorial experiment design, the multiple **regression** **equation** for the surface roughness in the climb milling process of DIN 1.4021 martensitic stainless steel has been obtained and then used as one of the objective functions in the Multi-objective Improved Self- Adaptive Particle Swarm Optimization (MISAPSO) algorithm. This algorithm has been used to obtain cutting parameters to achieve low surface roughness simultaneously with a high material removal rate. The relatively new algorithm MISAPSO developed with some changes in the common particle swarm optimization (PSO) technique, has been used in multi-objective optimization of machining processes and was shown to be able to help the process planners in selecting cutting parameters.

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The **regression** **equation** of India’s export to ASEAN(X) on total exports of India to the world (Y) is X = -1.715+0.118 Y and the value of **regression** coefficient of India’s export to ASEAN on total exports of India to the world is significant as the p-value 0.00 is less than level of significance 0.01and null hypothesis is rejected. Hence, results show that the dependence of India’s export to ASEAN on total exports of India to the world is significant.

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In order to the combined effects of the independent variables on the responses, L9 orthogonal array experiments with no repetition were carried out. The observed responses were fitted to a first order polynomial **equation**. From this the **regression** **equation** was derived for the composites and the taguchi technique was applied to evaluate the optimum composite and condition. The investigational results and calculated values were obtained based on the plan of experiment and then the results were analyzed with the help of commercial software MINITAB 16 as specially utilized for the Design of Experiment and statistical analysis of experiment applications.

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To develop qualitative software products in a given schedule it is necessary to estimate the software effort. The effort estimation is helpful to the project managers to determine effort required to complete qualitative software products. In this paper we proposed a novel linear **regression** model based on software size metric. We applied proposed **regression** model on 60 real time BPO projects. The proposed **regression** model considers a linear relationship between size and effort. To validate the accuracy of **regression** **equation**, we calculated R value. We used MMER and PRED(x) to calculate error rate and made comparisons with standard models. The proposed model has shown much closed and better results against some standard estimation models.

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Linearity was established over a concentration range of 2-10 µg/ml by plotting a graph of concentration versus respective peak areas. **Regression** analysis of the data thus obtained showed a good **regression** coefficient of 0.999 with a **regression** **equation** Y=64728x+731.6. The standard deviation and percentage relative standard deviation values were calculated at each concentration which was found to be within limits i.e., less than 2. The data obtained was shown in Table 1 (Figure 4).

Linear Regression Model The first step in specifying the linear regression equation model is to determine whether the product market competition, ownership, firm age, human resources man[r]

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A simple and cost effective spectrophotometric method is described for the determination of febuxostat in pure form and in pharmaceutical formulations. The drug was highly soluble in methanol so it was selected as the solvent system for the drug. This ensured adequate drug solubility and maximum assay sensitivity. The linearity range for febuxostat at its wavelength of detection of 315 nm was obtained as 0.2–15 µg/ml.The linear **regression** **equation** obtained by least square **regression** method, were Y =0.0802.X + 0.0036, where Y is the absorbance and X is the concentration (in µg/ml) of pure drug solution. The absorbance was found to increase linearly with increasing concentration of febuxostat, which is corroborated by the calculated correlation coefficient value of 0.9999. The limit of detection and limit of quantification was found to be 0.1743 µg/ ml & 0.5281 µg /ml respectively. The validity of the described procedure was assessed. Statistical analysis of the result shows high accuracy and good precision. The proposed method was successfully applied to the determination of febuxostat in pharmaceutical formulations without any interference from common excipients.

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Selection, on the other hand, allows for the construction of an optimal **regression** **equation** along with investigation into specific predictor variables. The aim of selection is to reduce the set of predictor variables to those that are necessary and account for nearly as much of the variance as is accounted for by the total set. In essence, selection helps to determine the level of importance of each predictor variable. It also assists in assessing the effects once the other predictor variables are statistically eliminated. The circumstances of the study, along with the nature of the research questions guide the selection of predictor variables.

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The following model represents the regression equation representing the relationship between the financial sustainability of non- governmental organizations as a linea[r]

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(11) The second step is the generation of synthetic volumes. A synthetic volume could be estimated from a synthetic peak with the **regression** **equation** between them, but this would lead to a perfect linear relationship that does not simulate its real variability. Therefore, as the residuals of the regres- sion **equation** are normally distributed in the log-log space of variables (Fig. 4), a normal randomisation was performed for each synthetic peak flow, with a mean equal to the re- sult of the **regression** **equation** (Eq. 5 or 8) and standard deviation equal to the residual variance of the **regression** (σ reg ) (Eq. 9 or 10).

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The cost driver analysis in Chapter 2 resulted in several cost drivers, of which the country and the location are hard to take into account in a **regression** **equation**, because they are not continuous or ordinal. Normalizing the data based on wage differences alone did not provide a better fit, but to account for the country and location factor, a conceptual **regression** **equation** is set up, based on three elements that are all driven by throughput: labour costs, building area costs and throughput costs. Labour costs: The amount of labour needed is a combination of the automation level and the throughput. The subsequent costs of the amount of labour then is mainly dependent on the country the warehouse is in. In order to take this into account, the **regression** slope is corrected per automation level:

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From the results of regression analysis, the authors set up a linear regression equation that evaluates the impact of independent factors on the dependent variable “Employee satisfaction[r]

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The **regression**-based imputation is another method of handling missing data where the missing cases are replaced with predicted values derived from **regression** **equation** based on observed values of the variables in the data set that are complete. Also referred to as conditional mean imputation, it is probably one of the best among the simple ad-hoc methods because cases with missing values are preserved and sample size is maintained. It is more informative since all existing information are utilized. The shortcomings of this method are; values beyond the logical range of the data may be imputed thereby distorting inferences, choosing the right **regression** model to fit the given data is challenging, it is not suitable for application on multivariate data having more than one variable with missing values, and large sample size is required to produce valid estimates.

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In the preparation of the deflection - rotation **regression** **equation** modeling two-dimensional structures, in order to obtain accurate results, the data must be grouped according to the shape of the elasticity line. Others, the deflection **equation** - quadratic rotation from two-dimensional structure modeling gives more accurate results than the linear deflection - rotation **equation**. Finally, the use of rotational data with four data rotation (4 tiltmeter) from two- dimensional structural modeling gives more accurate results than rotational data with two data rotation (2 tiltmeter). Author strongly recommends to verify this result againts three dimension structure analysis.

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