Linear Regression Sample Clauses
The Linear Regression clause establishes the use of a statistical method to model the relationship between a dependent variable and one or more independent variables. In practice, this clause may specify how data will be collected, analyzed, and interpreted using linear regression techniques, such as determining trends or making predictions based on historical data. Its core function is to provide a clear, standardized approach for analyzing data relationships, thereby ensuring objective and reproducible results in decision-making or reporting processes.
Linear Regression. A linear regression model was built to assess the continuous outcome, depression severity, using the stepwise method starting with the significant (α=0.05) covariates only. The final linear regression model is: Y = β0 + β1X1 + β2X2 Where: Y=Depression severity index X1 = Sex, where 0 is male and 1 is female X2 = Years of education, where 0 is >12 years, 1 is <12 years The estimate of the model is Y = 4.621 + (0.536)X + (-4.480)X + ε While no connectivity covariates were significantly related to depression severity, it was interesting to see that sex and education were significant at the α=0.05 level. Using the results, we could predict an average depression severity score of 4.62 among men with less than 12 years of schooling. Compared to men with the same level of schooling , we would expect female respondents to have a 0.54 increase in their depression severity score. Among respondents of the same sex, those who have more than twelve years of schooling will have a predicted 0.48 decrease in their severity of depression score. Female 0.536 (0.291) Education (<12 years) -0.480 (0.285) This study examined connectivity to home covariates that are most strongly associated with self-reported depression (respondent self-reported feeling sad, blue, or depressed for two weeks or more in a row during the last year) among recent legal permanent resident status recipients in the U.S. using data from the 2003 New Immigrant Survey. Two dependent variables were examined: a dichotomous depression or no depression outcome, and a depression severity index, with seven different depressive behaviors included. Using logistic regression, I modeled relationships between depression and a series of connectivity to home variables including: family unification, financial remittances/transfers, plans to visit home country in the next year, and possession of one or more assets/liabilities in the home country. Linear regression was used to analyze the relationship between the depression severity outcome and the same connectivity to home covariates. I controlled for demographic characteristics, such as age, gender, education, and region of origin in both models. The results showed that there were specific behaviors within the connectivity to home realm that were significantly related to depression. The connectivity covariates that were significantly related to depression were whether or not the immigrant had travel plans to their home country in the next year, if the spouse lived with...
Linear Regression. Preprocessing the data for linear regression involved first concatenating the columns (each column corresponding to a sample) from two of the P. knowlesi clones together to create an X dataset. Next, the columns of the remaining P. knowlesi clone had to be merged together into one, using some representative value for the expression level of that gene across all three samples to create a Y dataset. Different measurements were tested, including minimum, maximum, mean, and median. This process was repeated for three different combinations (columns of two P. knowlesi clones combined to make X dataset in order to predict merged columns of remaining P. knowlesi clone which is the Y dataset): A and B to predict C, A and C to predict B, and B and C to predict A. For this study, the LinearRegression class from Scikit-learn was used. Simple linear regression attempts to fit a linear model using the dependent input values (X dataset) to independent output values (Y dataset) (Pedregosa et al 2011). In this manner, we will be able to learn a linear function in the form y = f(x) that can be used to predict output values from new input values. This linear function is found by drawing a line that minimizes the distance between the given data points and the line, so that the residual sum of squares between the given Y output values and the approximated Y output values given by the line is as small as possible (▇▇▇▇▇▇▇▇▇ et al 2011). Thus, the coefficients of this best fit line describe the amount of variation each feature/column of the X dataset has on the Y dataset, which can be observed by the magnitude of its coefficient in the resulting linear model after fitting.
Linear Regression. 3.6.1 Crude Linear Regression Not Natural Flooring Material (log TTC) = α +β1FLOOR -0.16 -0.60, 0.28 0.4842 Own Animals (log TTC) = α +β1OWNAN 0.19 -0.04. 0.42 0.1046 Storage Container Narrow Mouth (log TTC) = α +β1MOUTH -0.74 -1.37, -0.12 0.0204* Storage Container Covered (log TTC) = α +β1COVERED -0.01 -0.28, 0.25 0.9203 Handwashing Materials Available (log TTC) = α +β1HW_MAT 0.21 -0.08, 0.50 0.1475 Handwashing Frequency (log TTC) = α +β1HW_FRQ 0.07 -0.03, 0.17 0.1913 Improved Toilet Facility (log TTC) = α +β1TOILET_T -0.29 -0.56, -0.01 0.0401* Toilet Facility Shared (log TTC) = α +β1TOILET_S -0.10 -0.38, 0.18 0.4742 Drinking Water Served by Pour (log TTC) = α +β1WTR_SEV -0.10 -0.48, 0.28 0.6193 Improved Water Source (log TTC) = α +β1SAM_SC -1.07 -1.37, -0.78 <.0001* Time Since Collection (log TTC) = α +β1CO_HRS 0.001 -0.002, 0.004 0.5987 Fetching Distance >=500 meters (log TTC) = α +β1FETCH_D -0.26 -0.51, -0.02 0.0312* Water Fetching Time >=30 mins (log TTC) = α +β1FETCH_T -0.34 -0.58, -0.10 0.0053* Water Purchased (log TTC) = α +β1BUY_CO -0.35 -0.63, -0.07 0.0138* Water Treated (log TTC) = α +β1SAM_TR 0.19 -0.35, 0.73 0.4846 SES Quartiles (log TTC) = α +β1SES_Q 0.09 0.02, 0.16 0.0146* Rainy Season (log TTC) = α +β1SEASON 0.61 0.38, 0.84 <.0001* Province (log TTC) = α +β1PROVINCE 0.08 -0.01, 0.17 0.0955 Crude linear regression models were run for each of the predictors as well as for the potential effect modifiers, this information is displayed in Table 8. The variables that had a statistically significant relationship with water quality were storage containers with narrow mouths (p-value=0.0204), improved household toilet facility (p- value=0.0407), improved water source (p-value<0.0001), water fetching distance more or equal to 500 meters (p-value = 0.0312), water fetching time more or equal to 30 minutes(p-value=0.0053), purchasing water (p-value=0.0138), SES quartile status (p- value= 0.0146), and rainy season (p-value <0.0001). The factors that contributed to lower levels of TTC concentration are non-natural flooring materials, narrow mouthed storage containers, covered storage containers, improved toilet facilities, shared toilet facilities, serving water by pouring it, improved water source, fetching distance more or equal to 500 meters, fetching time more or equal to 30 minutes, and purchased water. The factors contributing to higher levels of TTC concentration are owning animals, having handwashing materials available, handwashing frequency, hou...
Linear Regression. In this section brief description of the linear regression method is presented. Based on this method we will estimate the steel demand in Norway for year 2010. We would like to highlight that Linear Trend Equation and Least Square Method are alternative names for this method. Least Square Method is a powerful technique used to make forecasts when the data represent a linear trend. It determines which line best fits the historical data by minimizing sum of squared deviations around the line. According to ▇▇▇▇▇▇▇ (1989), the relationship between x and y is given by equation: _