Please provide this question correctly in 1 hour I will give you Thumbs up please provide correct solution Problem 2. (generated samples of (y,x) and fit simple linear regression models: (i) y on x,and (ii) x on y. Below are the (partial) outputs. Fill in the blanks (1) and (2). Make sure to show yourcalculations/reasoning.> summary (1m (y^x))Call:1m (formula = y ~ x)Residuals:Min1Q Median3QMax4.6601-4.3423 -1.3463 0.0226 1.4724Coefficients:Estimate Std. Error t value Pr (>|t|)(Intercept)0.16070.21170.7590.45X3.18140.2273 13.996<2e-16 ***Multiple R-squared: 0.6666F-statistic: 195.9 on 1 and 98 DF, p-value: <2.2e-16> summary (1m (x*y))Call:1m (formula = x y)Residuals:1Q Median3QMaxMin-1.17438 -0.37399 -0.03598 0.36148 1.52009Coefficients:Estimate Std. Error t value Pr (>|t|)(Intercept) -0.042540.05431 -0.7830.435y(1)0.01497<2e-16Multiple R-squared: (2)***

As we see that, p-value is same for coefficient y and x. So t-value will also be the same for both…

I generated samples of (y,x) and fit simple linear regression models: (i) y on x, are the (partial) outputs. Fill in the blanks (1) and (2). Make sure to show your Problem 2. ( and (ii) x on y. Below calculations/reasoning. > summary (1m (y x)) Call: 1m (formula = y ~ x) Residuals: Min Median 3Q 1Q -4.3423 -1.3463 0.0226 1.4724 Max 4.6601 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) 0.1607 0.2117 0.45 0.759 0.2273 13.996 X 3.1814 <2e-16 *** Multiple R-squared: 0.6666 F-statistic: 195.9 on 1 and 98 DF, p-value: < 2.2e-16 > summary (1m (x*y)) Call: ~ 1m (formula = x y) Residuals: 1Q Median 3Q Max Min -1.17438 -0.37399 -0.03598 0.36148 1.52009 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) -0.04254 0.05431 -0.783 0.435 y (1) 0.01497 <2e-16 *** Multiple R-squared: (2)

I generated samples of (y,x) and fit simple linear regression models: (i) y on x, are the (partial) outputs. Fill in the blanks (1) and (2). Make sure to show your Problem 2. ( and (ii) x on y. Below calculations/reasoning. > summary (1m (y x)) Call: 1m (formula = y ~ x) Residuals: Min Median 3Q 1Q -4.3423 -1.3463 0.0226 1.4724 Max 4.6601 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) 0.1607 0.2117 0.45 0.759 0.2273 13.996 X 3.1814 <2e-16 *** Multiple R-squared: 0.6666 F-statistic: 195.9 on 1 and 98 DF, p-value: < 2.2e-16 > summary (1m (x*y)) Call: ~ 1m (formula = x y) Residuals: 1Q Median 3Q Max Min -1.17438 -0.37399 -0.03598 0.36148 1.52009 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) -0.04254 0.05431 -0.783 0.435 y (1) 0.01497 <2e-16 *** Multiple R-squared: (2)

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
In Step 2: Construct an estimated simple linear regression model how did you come up with the column X*X ?
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