Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 220 for “"linear regression model"”.
-
Outliers in a Linear Regression Model
Over the last several decades the linear regression model has become one of the most widely used tools of the social sciences and the physical sciences. Given the data, the least squares method gives information for statistical inferences. However, the researcher frequently feels that the …
-
Linear regression model for continuous non-invasive blood pressure estimation for an ICU setting
… a novel, continuous non-invasive BP estimation model with age, gender and heart rate as covariates along with PTT to estimate ICU patient invasive BP data is presented in this pilot-study. The proposed novel model is tested for four common ICU clinical conditions. The results indicate compliance …
-
Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
Certain statistical systems for modelling are influenced by human perception. Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with. Thus, fuzzy structure system is considered. The objectives of this study were to: …
-
Development of a Bayesian linear regression model for the detection of a weak radiological source from gamma spectra measurements
… research has been conducted on using a Bayesian model to develop a decision parameter for weak source detection. The use of a Bayesian model has been shown in laboratory settings to outperform the traditional frequentist method. However, the model tested was designed for gross counts only. In the …
-
Fitting a Linear Regression Model and Forecasting in R in the Presence of Heteroskedascity with Particular Reference to Advanced Regression Technique Dataset on kaggle.com.
… select variables to include to use in building a model using the information in one of the data sets (training data set) and then test the effectiveness of the model on another set (test data set). Then, we explore the relationships between these variables and decide whether it is appropriate to …
-
Development of Multiple Linear Regression Model and Rule Based Decision Support System to Improve Supply Chain Management of Road Construction Projects in Disaster Regions
… The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / …
-
Data-Driven Analysis of Time of Day Pricing for Residential Consumers
… to the pricing scheme can inform more accurate models of consumption to maintain the integrity of the grid while lowering consumers' utility bills and optimizing renewable use. In this thesis, I analyze the data from a time-of-day pricing trial in London to see whether the treatment was …
-
Simultaneous confidence bands in linear regression analysis
… on the plausible range of an<br/>unknown regression model. For a simple linear regression model, the most frequently<br/>quoted bands in the statistical literature include the two-segment band, the three-segment<br/>band and the hyperbolic band, and for a multiple linear regression model, …
-
Monte Carlo Examination of Static and Dynamic Student t Regression Models
… issues related to Static and Dynamic Student t Regression Models. The Static Student t Regression Model is derived and transformed to an operational form. The operational form is then examined in a series of Monte Carlo experiments. The model is judged based on its usefulness for estimation and …
-
Multiple Linear Model Selection: A Data Study on the Ecological Controls on Methylmercury Production
… goal was to use the Backward Elimination model selection to develop the best fitted linear regression model to test the impact of seven environment variables on methylmercury concentration. Before analysis, I cleaned the whole data set and removed all missing values and transformed some …
-
An Intraseason Forecasting System for Commercial Marine Fisheries
… as the average timing or the average performance model. The method is not easily related to standard statistical models, but does show some similarity to both a single parameter linear regression model and the ratio estimator of sampling theory. A comparison of these models, a two parameter linear …
-
Optimum experimental designs for models with a skewed error distribution: with an application to stochastic frontier models
… optimum experimental designs for a statistical model possessing a skewed error distribution are considered, with particular interest in investigating possible parameter dependence of the optimum designs. The skewness in the distribution of the error arises from its assumed structure. The error …
-
Specification tests for autoregressive conditional heteroskedastic models with applications to exchange rates of Asian countries
… information matrix (IM) test is applied to the linear regression model with autoregressive conditional heteroskedastic (ARCH) errors. ARCH models are used widely in analyzing economic and financial time series data. However, in practice, the models are not often thoroughly tested. We derived …
-
An Investigation of Sensitivity of an F Test in Locating Change Points in Linear Regression
… of the F-test for detecting change points in linear regression, using a two-phase linear regression model. it offers an effective method to detect "undocumented" change points using a form of an F-test. Using simulated data, we explore its sensitivity and accuracy with respect t different …
-
INFERENCE AFTER VARIABLE SELECTION
This thesis presents inference for the multiple linear regression model Y = beta_1 x_1 + ... + beta_p x_p + e after model or variable selection, including prediction intervals for a future value of the response variable Y_f, and testing hypotheses with the bootstrap. If n is the sample size, most …
-
Forecast of Virginia coal production
This thesis provides a model for forecasting coal production rates in southwest Virginia. A multiple linear regression model is developed for the forecasting process. The model includes six independent variables: Virginia coal price times Virginia coal mining productivity (x₁), Virginia mining …
-
Examination of capital structure impact on profitability and share returns: evidence from pharmaceutical and biotech industry on FTSE-all shares index.
… was conducted on 30 companies using a multiple linear regression model with the aid of Excel. Based on a 0.05% significant level, I found that the relationship between share returns and capital structure is statistically insignificant. Also, the relationship between profitability and capital …
-
Academic Performance among Homeless Students: Exploring Relationships of Socio-Economic and Demographic Variables
… grade level and racial differences. A multiple linear regression model is used to test the hypotheses while controlling confounding variables. Statistically significant relationships are reported between race and academic performance, and grade level and academic performance. Practical and …
-
Detection of Outliers and Influential Observations in Regression Models
<p>Observations arising from a linear regression model, lead one to believe that a particular observation or a set of observations are aberrant from the rest of the data. These may arise in several ways: for example, from incorrect or faulty measurements or by gross errors in either response or …
Page 1 of 11