class4a_regression_activites_engr195(ii)s10_hddsol

# class4a_regression_activites_engr195(ii)s10_hddsol - a= 0.5...

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ENGR 195(II) - Spring 2010 Class 4A - Activity 1 - Regression by Eye and Two Point Method i F(x) 1 1 0.5 0.5 2 3 2.5 1.5 3 5 2 2.5 4 7 4 3.5 5 9 4.5 4.5 Means 5 2.7 2.5 Regression By Eye and Two Point Method: 1. Use Insert > Shapes to draw a line on the graph as your best-fit line 2. Select two points on the line you drew point 1 : point 2: x1 1 x2 9 y1 0.5 y2 4.5 3. Use the two points to compute the slope (a) and intercept (b) a= 0.5 b= 0 x i y i 0 0 1 2 3 4 5 y

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ENGR 195(II) - Spring 2010 Class 4A - Activity 2 - Goodness of Fit of Regression by Eye and Two Point Method i 1 1 0.5 0.5 0 4 2 3 2.5 1.5 1 0 3 5 2 2.5 0.25 0.25 4 7 4 3.5 0.25 2.25 5 9 3.5 4.5 1 1 Sums: 25 12.5 2.5 7.5 Mean: 2.5 Goodness of Fit of Regression by Eye and Two Point Method: 1. Recall your slope (a) and intercept (b) from Activity 1
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Unformatted text preview: a= 0.5 b= 3. Consider how you would compute the errors for each data point that feed into the SSE and SST calculations. (Hint: You may need to add columns to the table above to perfom intermediate calculations) 4. Using the values in the table above compute the Sum of Squares of Errors. SSE 2.5 5. Using the values in the table above compute the Sum of the Squares of the Deviations SST 7.5 0.67 x i y i f(x i ) [y i-f(x i )] 2 [y i-y bar ] 2 2. Use your slope and intercept to compute predicted values of y for each x in the table above (called f(x i )) 6. Use SSE and SST to determine r 2 r 2...
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## This note was uploaded on 08/25/2011 for the course ENGR 195 taught by Professor Staff during the Fall '08 term at Purdue University-West Lafayette.

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class4a_regression_activites_engr195(ii)s10_hddsol - a= 0.5...

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