A clothing manufacturer wants to estimate the amount of scrap cloth generated each day by its fabric cutting machines. Eight potential independent variables have been identified. These include the following. x, = amount of cloth run through cutting machines (in square feet) x, = machine cutting speed (in feet per minute) xg = age of machine (in years) The manufacturer selects 6 of the candidate independent variables to use in a multiple regression model for estimating y, the amount of scrap cloth (in square feet). Using data collected from 24 different cutting machines operating on different days, the model y= B,+B,x, +B,x,+... +B,x, is fit to the data. Fill in the blanks in the analysis of variance (ANOVA) table associated with this model. Do all calculations to at least three decimal places.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
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