please answer the wrong ones. box answer (d) Calculate the residual corresponding to the first observation. (Round your answer to two decimal places.)-0.28Calculate the residual corresponding to the second observation. (Round your answer to two decimal places.)7.16X(e) Calculate a point estimate of o. (Round your answer to two decimal places.)116.47xInterpret the above point estimate of a.O The unit weights of concrete specimens deviate from the least squares line by approximately the square of the point estimate of sigma onaverage.O Porosity measurements deviate from the least squares line by approximately the square of the point estimate of sigma on average.Porosity measurements deviate from the least squares line by approximately the point estimate of sigma on average.O The unit weights of concrete specimens deviate from the least squares line by approximately the point estimate of sigma on average.O Porosity measurements deviate from sample mean porosity measurement by approximately the square of the point estimate of sigma onaverage.(f) What proportion of observed variation in porosity can be attributed to the approximate linear relationship between unit weight and porosity? (Round your answer to two decimal places.)0.97Need Help? Watch It No-fines concrete, made from a uniformly graded coarse aggregate and a cement-water paste, is beneficial in areas prone to excessive rainfall because of its excellent drainage properties. The article "Pavement Thickness Design for No-Fines ConcreteParking Lots,"+ employed a least squares analysis in studying how y porosity (%) is related to x unit weight (pcf) in concrete specimens. Consider the following representative data.125120115y110Relevant summary quantities are Σx, = 1640.2 ΣΥ, – 299.3, Σx2 – 179,865.54, Σxy, = 32,259,22, ΣΥ(a) Obtain the equation of the estimated regression line. (Round all numerical values to four decimal places.)y 119.3385 -0.9089x105y 28.9 27.8 26.9 25.1 22.9 21.4 20.8 19.7Create a scatterplot of the data and graph the estimated line.100O99.2 101.2 102.6 103.1 105.3 107.2 108.5 110.7X 112.0 112.2 113.8. 114.0 115.2 115.3 119.9y16.9 18.8 15.9 16.9USE SALT1012.8 13.5 11.015202530Xy125120115110105100101520Does it appear that the model relationship will explain a great deal of the observed variation in y?Yes25(c) Predict porosity when unit weight is 134? (Round your answer to two decimal places.)ŷ--2.45(b) Interpret the slope of the least squares line. (Round your answer to four decimal places.)The slope tells us that a one-pcf increase in the unit weight of a concrete specimen is associated with a 0.9989-6409.33.(d) Calculate the residual corresponding to the first observation. (Round your answer to two decimal places.)-0.28Why is it not a good idea to predict porosity when unit weight is 134?When we predict porosity for a unit weight of 134 (which is outside the scope of the data) the result is [negativey30252030 O, the linear model explains a great deal of the variation in y since the accompanying fitted line plot does1510100105•which cannot110 115120125✓✓happen in reality.y3025XO201510100Xpercentage point decrease in the specimen's predicted porosity.105 110show a very strong, linear association between unit weight and porosity.115120125

Question

please answer the wrong ones. box answer (d) Calculate the residual corresponding to the first observation. (Round your answer to two decimal places.)-0.28Calculate the residual corresponding to the second observation. (Round your answer to two decimal places.)7.16X(e) Calculate a point estimate of o. (Round your answer to two decimal places.)116.47xInterpret the above point estimate of a.O The unit weights of concrete specimens deviate from the least squares line by approximately the square of the point estimate of sigma onaverage.O Porosity measurements deviate from the least squares line by approximately the square of the point estimate of sigma on average.Porosity measurements deviate from the least squares line by approximately the point estimate of sigma on average.O The unit weights of concrete specimens deviate from the least squares line by approximately the point estimate of sigma on average.O Porosity measurements deviate from sample mean porosity measurement by approximately the square of the point estimate of sigma onaverage.(f) What proportion of observed variation in porosity can be attributed to the approximate linear relationship between unit weight and porosity? (Round your answer to two decimal places.)0.97Need Help? Watch It No-fines concrete, made from a uniformly graded coarse aggregate and a cement-water paste, is beneficial in areas prone to excessive rainfall because of its excellent drainage properties. The article &#34;Pavement Thickness Design for No-Fines ConcreteParking Lots,&#34;+ employed a least squares analysis in studying how y porosity (%) is related to x unit weight (pcf) in concrete specimens. Consider the following representative data.125120115y110Relevant summary quantities are Σx, = 1640.2 ΣΥ, – 299.3, Σx2 – 179,865.54, Σxy, = 32,259,22, ΣΥ(a) Obtain the equation of the estimated regression line. (Round all numerical values to four decimal places.)y 119.3385 -0.9089x105y 28.9 27.8 26.9 25.1 22.9 21.4 20.8 19.7Create a scatterplot of the data and graph the estimated line.100O99.2 101.2 102.6 103.1 105.3 107.2 108.5 110.7X 112.0 112.2 113.8. 114.0 115.2 115.3 119.9y16.9 18.8 15.9 16.9USE SALT1012.8 13.5 11.015202530Xy125120115110105100101520Does it appear that the model relationship will explain a great deal of the observed variation in y?Yes25(c) Predict porosity when unit weight is 134? (Round your answer to two decimal places.)ŷ--2.45(b) Interpret the slope of the least squares line. (Round your answer to four decimal places.)The slope tells us that a one-pcf increase in the unit weight of a concrete specimen is associated with a 0.9989-6409.33.(d) Calculate the residual corresponding to the first observation. (Round your answer to two decimal places.)-0.28Why is it not a good idea to predict porosity when unit weight is 134?When we predict porosity for a unit weight of 134 (which is outside the scope of the data) the result is [negativey30252030 O, the linear model explains a great deal of the variation in y since the accompanying fitted line plot does1510100105•which cannot110 115120125✓✓happen in reality.y3025XO201510100Xpercentage point decrease in the specimen's predicted porosity.105 110show a very strong, linear association between unit weight and porosity.115120125

Accepted Answer

Given data:

x
y

99.2
28.9

101.2
27.8

102.6
26.9

103.1
25.1

105.3
22.9

107.2
21.4…

Calculate the residual corresponding to the second observation. (Round your answer to two decimal places.) 7.16 x (e) Calculate a point estimate of o. (Round your answer to two decimal places.) 116.47 x Interpret the above point estimate of a.