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Stick the landing-Eite female gymnasts compete on 4 apparatus Floor Vaut, Uneven Bars, and Balance Bea Simone is investigating the relationship between gymnasts' scores on the different apparatus. She collects a random sample of 75 gymnasts who competed in international competitions between the years 2006 and 2019. For this problem we will look at the scores for the two apparatus, vaut and balance bea Simone constructs near regression model using Score on Vault as the explanatory variable and Score on Balance Beam as the response variable. A scatterplot of Simone's data is shown Simone uses statistical software to fit a near model to the data. A summary of that model it is given below Coefficients Estimate Snd Error 1 value | PY( > [1]). Ontercept P Soome on Vau 5.812 2.405 2.231 05735 01414 3.162 0.028 0.0022 Residual standarder: 0.005 on 23 degrees of freedom MR-01205, Adjusted Rsquared: 0.1084 1. Use the computer output to write the estimated near regression equation for predicting Score on Balance Beam from Score on X these and intercept of the linear regression mode 2. An increase of 1 point in Score on Vault is associated with 7 3.A gymnast who received a score of 0 points in Score on Vault would have an expected score of in Score on Balance Beam in Score on Balance Beam Elite Womens Gymnastics 135 140 145 150 155 Score on Vaut

Stick the landing-Eite female gymnasts compete on 4 apparatus Floor Vaut, Uneven Bars, and Balance Bea Simone is investigating the relationship between gymnasts' scores on the different apparatus. She collects a random sample of 75 gymnasts who competed in international competitions between the years 2006 and 2019. For this problem we will look at the scores for the two apparatus, vaut and balance bea Simone constructs near regression model using Score on Vault as the explanatory variable and Score on Balance Beam as the response variable. A scatterplot of Simone's data is shown Simone uses statistical software to fit a near model to the data. A summary of that model it is given below Coefficients Estimate Snd Error 1 value | PY( > [1]). Ontercept P Soome on Vau 5.812 2.405 2.231 05735 01414 3.162 0.028 0.0022 Residual standarder: 0.005 on 23 degrees of freedom MR-01205, Adjusted Rsquared: 0.1084 1. Use the computer output to write the estimated near regression equation for predicting Score on Balance Beam from Score on X these and intercept of the linear regression mode 2. An increase of 1 point in Score on Vault is associated with 7 3.A gymnast who received a score of 0 points in Score on Vault would have an expected score of in Score on Balance Beam in Score on Balance Beam Elite Womens Gymnastics 135 140 145 150 155 Score on Vaut

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Stick the landing - Elite female gymnasts compete on 4 apparatus: Floor, Vault, Uneven Bars, and Balance Beam. Simone is investigating the relationship between gymnasts' scores on the different apparatus. She collects a random sample of 75 gymnasts who competed in international competitions between the years 2006 and 2019. For this problem we will look at the scores for the two apparatus, vault and balance beam. Simone constructs a linear regression model using Score Vault as the explanatory variable and Score on Balance Beam as the response variable. A scatterplot of Simone's data is shown. Simone uses statistical software to fit a linear model to the data. A summary of that model fit is given below: Coefficients Estimate Std Error t value Pr(t) (Intercept) 2.605 2.231 5.812 0.0288 Score on Vault 0.5735 0.1814 3.162 0.00228 Residual standard error: 0.905 on 73 degrees of freedom Multiple R-squared: 0.1205, Adjusted R-squared: 0.1084 1. Use the computer output to write the estimated linear regression equation y= Interpret the slope and intercept of the linear regression model: 2. An increase of 1 point in Score on Vault is associated with a(n) ? 3. A gymnast who received a score of 0 points in Score on Vault would have an expected score of predicting Score on Balance Beam from Score on Vault. Test statistic: Vof 4. Which of the following is the correlation coefficient for the linear relationship between Score on Balance Beam and Score on Vault? OA. 0.3471 OB.-0.1205 OC. -0.3471 OD. 0.1205 5. What are the null and alternative hypotheses to test if there is a linear relationship between Score on Balance Beam and Score on Vault? OA. Ho ₁0 vs. H: ₁0 OB. Ho B₁ b₁ vs. H₁ B₁ #b₁ OC. Ho b₁ = 0 vs. H₁ b₁0 OD. Ho B₁ = 0 vs. H₁₁0 6. Based on the computer output, what is the test statistic for the test in part 5? 7. Based on the computer output, the results of the hypothesis test tell us that we have ? Estimated value= in Score on Balance Beam in Score on Balance Beam. Residual = V evidence that there? 8. In 2013, Victoria Moors recieved a score of 14.733 on the vault apparatus. Calculate the estimated value for this gymnast's score balance beam that is predicted by the linear model. 9. Victoria Moors's actual score on balance beam that year was 13.8. Use this information and your result from part 8 to calculate the residual for this gymnast. Score on Balance Beam 13 14 15 16 11 12 Elite Womens Gymnastics a linear relationship between Score on Balance Beam and Score on Vault. 13.5 14.0 14.5 15.0 15.5 Score on Vault
Transcribed Image Text:Stick the landing - Elite female gymnasts compete on 4 apparatus: Floor, Vault, Uneven Bars, and Balance Beam. Simone is investigating the relationship between gymnasts' scores on the different apparatus. She collects a random sample of 75 gymnasts who competed in international competitions between the years 2006 and 2019. For this problem we will look at the scores for the two apparatus, vault and balance beam. Simone constructs a linear regression model using Score Vault as the explanatory variable and Score on Balance Beam as the response variable. A scatterplot of Simone's data is shown. Simone uses statistical software to fit a linear model to the data. A summary of that model fit is given below: Coefficients Estimate Std Error t value Pr(t) (Intercept) 2.605 2.231 5.812 0.0288 Score on Vault 0.5735 0.1814 3.162 0.00228 Residual standard error: 0.905 on 73 degrees of freedom Multiple R-squared: 0.1205, Adjusted R-squared: 0.1084 1. Use the computer output to write the estimated linear regression equation y= Interpret the slope and intercept of the linear regression model: 2. An increase of 1 point in Score on Vault is associated with a(n) ? 3. A gymnast who received a score of 0 points in Score on Vault would have an expected score of predicting Score on Balance Beam from Score on Vault. Test statistic: Vof 4. Which of the following is the correlation coefficient for the linear relationship between Score on Balance Beam and Score on Vault? OA. 0.3471 OB.-0.1205 OC. -0.3471 OD. 0.1205 5. What are the null and alternative hypotheses to test if there is a linear relationship between Score on Balance Beam and Score on Vault? OA. Ho ₁0 vs. H: ₁0 OB. Ho B₁ b₁ vs. H₁ B₁ #b₁ OC. Ho b₁ = 0 vs. H₁ b₁0 OD. Ho B₁ = 0 vs. H₁₁0 6. Based on the computer output, what is the test statistic for the test in part 5? 7. Based on the computer output, the results of the hypothesis test tell us that we have ? Estimated value= in Score on Balance Beam in Score on Balance Beam. Residual = V evidence that there? 8. In 2013, Victoria Moors recieved a score of 14.733 on the vault apparatus. Calculate the estimated value for this gymnast's score balance beam that is predicted by the linear model. 9. Victoria Moors's actual score on balance beam that year was 13.8. Use this information and your result from part 8 to calculate the residual for this gymnast. Score on Balance Beam 13 14 15 16 11 12 Elite Womens Gymnastics a linear relationship between Score on Balance Beam and Score on Vault. 13.5 14.0 14.5 15.0 15.5 Score on Vault
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