Bivariate data obtained for the paired variables x and y are shown below, in the
ID: 3376569 • Letter: B
Question
Bivariate data obtained for the paired variables x and y are shown below, in the table labelled "Sample data." These data are plotted in the scatter plot in Figure 1, which also displays the least-squares regression line for the data. The equation for this line is
Can anyone please help me solving these problems from 1-4
y 124.49 0.88x In the "Calculations table are calculations involving the observed y values, the mean y of these values, and the values y predicted from the regression equation Sample data Calculations 55.6 78.0 61.0 65.3 65.6 69.8 69.5 60.8 74.0 60.4 124.0996 2.4336 8.6436 36.7236 41.7316 213.6320 5.9438 30.3601 9.2294 6.4009 1.0609 75.7248 15.6025 0.0096 12.4609 56.1001 52.9952 159.8979 sums 50 55 60 6570 7580 Figure 1 Answer the following: 1. The least-squares regression line given above is said to be a line which "best fits" the sample data. The term "best fits" is used because the line has an equation that minimizes the error sum of squares which for these data is 52.9952 - 2. For the data point (74.0, 60.4), the value of the residual is 1.03. (Round your answer to at least 2 decimal places.) 3. The variation in the sample y values that is not explained by the estimated linear relationship between x and y is given by the total sum of squares data is 52.9952. v, which for these 4. The proportion of the total variation in the sample y values that can be explained by the estimated linear relationship between x and y s (Round your answer to at least 2 decimal places.)Explanation / Answer
1. The term "best-fit" is used because the line has an equation that minimizes the error sum of square, which for this data is 52.9952.
2. For the data point (74.0,60.4), the value of residual is 1.03.
3.the variation in the sample y values that is not explained by the estimated linear relationship between x and y is given by error sum of square, which for this data is 52.9952.
4. ssr/sst = 159.8979/213.6320 =0.748
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