The evaluation model of fabric transient cooling sensation based on multiple stepwise regression analysis is a statistical approach used to assess the cooling sensation provided by different types of fabrics.
In this model, multiple stepwise regression analysis is used to identify the key factors that contribute to the fabric's cooling sensation. The analysis helps determine the relationship between these factors and the perceived cooling effect.
Here are the general steps you may follow to develop the model:
1. Data Collection: Collect a dataset that includes fabric properties (such as fiber composition, fabric thickness, weight, porosity, etc.) and subjective ratings of cooling sensation provided by human subjects. The subjective ratings can be obtained through sensory evaluations or questionnaires.
2. Variable Selection: Use stepwise regression analysis to select the most relevant fabric properties as independent variables. The stepwise regression method automatically identifies combinations of variables that best predict the cooling sensation, based on their statistical significance.
3. Model Development: Develop a regression model using the selected independent variables as predictors and the subjective cooling ratings as the dependent variable. The goal is to create a model that accurately predicts the cooling sensation based on fabric properties.
4. Model Evaluation: Validate the model using a separate dataset or through cross-validation techniques. Assess its performance by measuring the goodness-of-fit, such as R-squared value or Mean Squared Error (MSE). This will provide an indication of how well the model predicts the cooling sensation.
5. Interpretation: Analyze the coefficients of the regression model to understand the influence of each fabric property on the cooling sensation. This analysis helps identify which fabric properties contribute the most to the perceived cooling effect.
It's important to note that the specific details of the model, variables used, and analysis techniques can vary depending on the specific study and research objectives. Additionally, this type of evaluation model can be further refined and expanded to incorporate other variables or advanced statistical methods, as needed.
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