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Once again, welcome to a new problem.
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This time, we're dealing with regression.
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We're dealing with regression.
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And for the most part, when it comes to regression, we have two options.
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We have the one or two options.
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We have a relationship between x and y variable.
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X is the independent and y is the dependent.
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So the independent, this is the independent.
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Explanatory variable, and this is the response variable.
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The regression equation is y hat equals to beta not plus beta 1 x1 plus beta 2 x2.
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And r is the correlation coefficient, the correlation coefficient that runs from negative one up until one and passes through zero.
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R squared is the coefficient of determination.
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Coefficient of determination, you know, that's your typical r squared that you're looking for.
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We're given a new problem and in this particular problem, we're looking at the relationship between the yield which we're going to consider as our y.
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And chemical processes to reactant concentration, that's your initial x1, and operating temperature, that's x2.
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So we want to see the relationship.
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We want to determine the regression model and discuss the significance.
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So we'll say the regression model involved two independent variables, involves two independent variables.
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Therefore, it is a multiple linear regression model.
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So this is going to be a multiple linear regression model...