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) ?ofin Score on Balance Beam.3. A gymnast who received a score of 0 points in Score on Vault would have an expected score ofin Score on Balance Beam.4. Which of the following is the correlation coefficient for the linear relationship between Score on Balance Beam and Score on Vault?O A. -0.4169Ов.-0.1738OC. 0.1738O D. 0.41695. What are the null and alternative hypotheses to test if there is a linear relationship between Score on Balance Beam and Score on Vault?O A. Ho : b1ОВ. Но : В 3 bi vs. Ha : Bi # biос. Но : ВO D. Ho : B1= 0 vs. HA : b1 # 0= 0 vs. HA : B1 # 0= 0 vs. HA : B, > 06. Based on the computer output, what is the test statistic for the test in part 5?Test statistic:7. Based on the computer output, the results of the hypothesis test tell us that we have?evidence that therev a linear relationship between Score on Balance Beamand Score on Vault.8. In 2018, Naomi Visser recieved a score of 13.466 on the vault apparatus. Calculate the estimated value for this gymnast's score on balance beam that is predicted by the linear model.Estimated value =9. Naomi Visser'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.Residual =10. Use information from the computer output to calculate a 99% confidence interval for the slope, B1, of the regression line predicting Score on Balance Beam from Score on Vault.IMPORTANT! You MUST use a t* value rounded to EXACTLY 3 decimal places in this calculation. Round your final answers to 4 decimal places. 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 theyears 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 on Vault as the explanatory variable and Score on Balance Beam as the response variable. A scatterplot of Simone's data is shown.Elite Womens Gymnastics13.514.014.515.015.5Score on VaultSimone uses statistical software to fit a linear model to the data. A summary of that model fit is given below:CoefficientsEstimateStd Errort valuePr( > [t])(Intercept)3.5112.6631.3190.191Score on Vault0.72830.18593.9180.000199Residual standard error: 0.908 on 73 degrees of freedomMultiple R-squared: 0.1738, Adjusted R-squared: 0.16241. Use the computer output to write the estimated linear regression equation for predicting Score on Balance Beam from Score on Vault.ŷ =+Score on Balance Beam11121314 1591

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density34 35745 34148 33160 32965 325 Step 5 of 6: Determine if the statement "All points predicted by the linear model fall on the same line" is true or false.
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The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 39 43 46 61 64 Bone Density 352 346 321 314 312 Table Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places
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