Regression analysis is a statistical method that seeks to establish an equation that allows the unknown value of one variable to be estimated from the known value of one or more other variables.
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Q. 2One way to measure the strength of the relationship between the response variable y and the predictor variable x is to calculate the coefficient of determination; that is, the proportion of the total variation in y that is explained by the linear regression of y on x.
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Q. 3The residuals are observations of the error variable e. Consequently, the minimized sum of squared deviations is called the sum of squares for error, denoted SSE.
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Q. 4A regression analysis between sales (in 1000) and advertising (in 100) resulted in the following least squares line: y = 77 +8x. This implies that if advertising is 600, then the predicted amount of sales (in dollars) is 125,000.
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Q. 5If the coefficient of determination is 0.982, then the slope of the regression line must be positive.
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Q. 6Given that the sum of squares for error (SSE) is 52 and the sum of squares for regression (SSR) is 148, then the coefficient of determination is 0.74.
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Q. 7If a least squares regression line has a y-intercept of 6.84 and a slope of 2.16, then when x = 1 the actual value of y must be 9.
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