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problem 1, two parts Data 2 3 ANOVA Regression Residual Total 4 5 Intercept 6 7 X 8 9 10 161 203 235 176 201 188 228 211 191 178 To the right, in the gray colls, is the regression output for the data above (y is the dependent variable) This output is generated by the regression routine in Excel, and is similar to what would be produced by Mintab For HW7, you will learn the math behind how these numbers are generated and recreate this output The Week 8 notes, especialy 8-3, will give you the equations for each coll Your answers will go below, though you will need additional calls for calculations Each call should have a function or equation not just a number The result should match the output to the right, so you can easily check your work Y 100 200 241 163 197 Part a. Fill in the yellow cells below. These must all be functions or equations. Do not just type in numbers. Regression Statistics Muple R R Square Standard Error Observations 193 200 $$ 189 160 201 Coefficients Standard Error MS 1 Stat F P-value Significance F Lower 95% Part b. What can you conclude from this regression output? Is it a good fit? How do you know? Upper 95% SUMMARY OUTPUT(From Excel) Regression Statis Mutiple R RSquar Standard Error Observations ANOVA Regression Residual Total Intercept 08100 0.0657 15.1005 10 1 8 9 Coeficients 22.36 0.86 SS 3631.9 18242 6456.1 Standard Error 42.05 0.22 MS 3631.0 2211 1 Stat 0.02 3.90 F 15.9 Pvale 0.62 0 Significance F 0 Lower 95% -76.68 0.36 Upper 95% 121.4 1.36

problem 1, two parts Data 2 3 ANOVA Regression Residual Total 4 5 Intercept 6 7 X 8 9 10 161 203 235 176 201 188 228 211 191 178 To the right, in the gray colls, is the regression output for the data above (y is the dependent variable) This output is generated by the regression routine in Excel, and is similar to what would be produced by Mintab For HW7, you will learn the math behind how these numbers are generated and recreate this output The Week 8 notes, especialy 8-3, will give you the equations for each coll Your answers will go below, though you will need additional calls for calculations Each call should have a function or equation not just a number The result should match the output to the right, so you can easily check your work Y 100 200 241 163 197 Part a. Fill in the yellow cells below. These must all be functions or equations. Do not just type in numbers. Regression Statistics Muple R R Square Standard Error Observations 193 200 $$ 189 160 201 Coefficients Standard Error MS 1 Stat F P-value Significance F Lower 95% Part b. What can you conclude from this regression output? Is it a good fit? How do you know? Upper 95% SUMMARY OUTPUT(From Excel) Regression Statis Mutiple R RSquar Standard Error Observations ANOVA Regression Residual Total Intercept 08100 0.0657 15.1005 10 1 8 9 Coeficients 22.36 0.86 SS 3631.9 18242 6456.1 Standard Error 42.05 0.22 MS 3631.0 2211 1 Stat 0.02 3.90 F 15.9 Pvale 0.62 0 Significance F 0 Lower 95% -76.68 0.36 Upper 95% 121.4 1.36

Linear Algebra: A Modern Introduction
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 31EQ
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problem 1, two parts Data 6 problems (see sheet tabs) ANOVA i 1 2 3 4 Regression Residual Tatal 5 6 7 8 Intercept 10 X X 161 203 235 176 201 188 228 To the right, in the gray cells, is the regression output for the data above (y is the dependent variable). This output is generated by the regression routine in Excel, and is similar to what would be produced by Minitab. For HWT, you will learn the math behind how these numbers are generated and recreate this output 211 191 178 The Week 8 notes, especially 8-3, will give you the equations for each call Your answers will go below, though you will need additional calls for calculations. df Each call should have a function or equation not just a number. The result should match the output to the right, so you can easily check your work Part a. Fill in the yellow cells below. These must all be functions or equations. Do not just type in numbers. Regression Statistics Multiple R R Square Standard Error Observations Y 159 206 241 163 197 193 209 189 169 201 SS Coefficients Standard Error MS t Stat F P-value Significance F Lower 95% Part b. What can you conclude from this regression output? Is it a good fit? How do you know? Upper 95% SUMMARY OUTPUT (from Excel) Regression Statistics Multiple R R Square Standard Error Observations ANOVA Regression Residual Total Intercept X 0.8159 0.6657 15.1005 10 dr 1 8 9 Coefficients 22.36 0.86 SS 3631.9 1824.2 5456.1 Standard Error 42.95 0.22 MS 3631.9 228 1 Stat 0.52 3.99 F 15.9 P-value 0.62 0 Significance F 0 Lower 95% -76.68 0.36 Upper 95% 121.4 1.36
Transcribed Image Text:problem 1, two parts Data 6 problems (see sheet tabs) ANOVA i 1 2 3 4 Regression Residual Tatal 5 6 7 8 Intercept 10 X X 161 203 235 176 201 188 228 To the right, in the gray cells, is the regression output for the data above (y is the dependent variable). This output is generated by the regression routine in Excel, and is similar to what would be produced by Minitab. For HWT, you will learn the math behind how these numbers are generated and recreate this output 211 191 178 The Week 8 notes, especially 8-3, will give you the equations for each call Your answers will go below, though you will need additional calls for calculations. df Each call should have a function or equation not just a number. The result should match the output to the right, so you can easily check your work Part a. Fill in the yellow cells below. These must all be functions or equations. Do not just type in numbers. Regression Statistics Multiple R R Square Standard Error Observations Y 159 206 241 163 197 193 209 189 169 201 SS Coefficients Standard Error MS t Stat F P-value Significance F Lower 95% Part b. What can you conclude from this regression output? Is it a good fit? How do you know? Upper 95% SUMMARY OUTPUT (from Excel) Regression Statistics Multiple R R Square Standard Error Observations ANOVA Regression Residual Total Intercept X 0.8159 0.6657 15.1005 10 dr 1 8 9 Coefficients 22.36 0.86 SS 3631.9 1824.2 5456.1 Standard Error 42.95 0.22 MS 3631.9 228 1 Stat 0.52 3.99 F 15.9 P-value 0.62 0 Significance F 0 Lower 95% -76.68 0.36 Upper 95% 121.4 1.36
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