A producer of educational TV shows for gifted children wants to promote the claim that gifted children'sscores on an analytical test increase the more the children watch their shows.In RStudio, run the following code to install and/or library the package "openintro".1. install.packages("openintro") # don't do this again if you already did this!2. library(openintro)3. giftedDelete the install line of code if you are in an RMD file so that it doesn't install every time you knit. Thelast line of code will access the dataset of that name.The dataset named 'gifted' gives information about test scores of gifted children on a standard analyticaltest and the number of hours of educational TV these children watch. use the data set to make a model topredict score on analytical skills test ("score") from hours of educational TV watched per week ("edutv").a. Make a scatterplot of "score" (y-axis) vs "edutv" (x-axis). Which plot is the scatterplot?Graph A Graph B Graph C Graph DCA165-160-155-150-C 3.025-22.0-1.5-10-1501.01561.5edutv160edutv1653.0D165-160-O Ho: ₁3.067HA: 3.067156-150-165-160-155-150-b. Does the relationship appear roughly linear?O No, all the points do not fall on a straight line.OYes, there is no obvious curvature in the graphO Yes, all the points fall on a straight line.O No, there are gaps in the graph along the x-axis.○ Ho: B₁ = 0HA:B10c. Do the data provide strong evidence that high school GPA and first-year college GPA are associated?State the null and alternative hypotheses, report the p-value, and state your conclusion.i. The hypotheses are:Ho: B₁0HA:B10Ho: a = 0HA: 0:edutvedutvHo: 1 = 0HA: >0ii. The p-value for the test is (round to three decimal places--if that value is 0 then enter 0.):Ho: B₁- 3.067HA:B 3.067iii. The result of this hypothesis test is:O About 13.71% of the variation in hours of educational TV watched per week is explainedby the least-squares line.O The total amount of variation in the model.The total unexplained variation in the model.O hours of educational TV watched per week is predictive of score on analytical skills test.O About 13.71% of the variation in score on analytical skills test is explained by the least-squares line.e. What is the equation of the regression line?O hoursty - 165.144 score - 3.067O hoursry -3.067 score 165.144O score - 165.144 hoursty - 3.067O score = -3.067 hoursTV + 165.144O hours of educational TV watched per week is not predictive of score on analytical skillstest.d. Interpret R²O The amount of variation in hours of educational TV watched per week that is explained by theleast squares line.O the amount of variation in score on analytical skills test that is explained by the least squaresline.f. Interpret the slope in the context of the model. For each Select an answer V increase inSelect an answerV. Select an answerVSelect an answerSelect an answer V.g. Interpret the y-intercept in the context of the model or explain why it should not be interpreted.O When the score on analytical skills test of a gifted child is 0 the expected value of hours ofeducational TV watched per week is -3.067.O When the score on analytical skills test of a gifted child is 0 the expected value of hours ofeducational TV watched per week is 165.144.O It does not make sense to interpret the y-intercept for this model because no gifted child inthe data had a hours of educational TV watched per week anywhere close to 0.O When the hours of educational TV watched per week of a gifted child is 0 the expected valueof score on analytical skills test is 165.144.

Note: “Since you have posted multiple questions, we will provide the solution only to the specified…

A producer of educational TV shows for gifted children wants to promote the claim that gifted children's scores on an analytical test increase the more the children watch their shows. In RStudio, run the following code to install and/or library the package "openintro". 1. install.packages("openintro") # don't do this again if you already did this! 2. library(openintro) 3. gifted Delete the install line of code if you are in an RMD file so that it doesn't install every time you knit. The last line of code will access the dataset of that name. The dataset named 'gifted' gives information about test scores of gifted children on a standard analytical test and the number of hours of educational TV these children watch. use the data set to make a model to predict score on analytical skills test (score") from hours of educational TV watched per week ("edutv"). a. Make a scatterplot of "score" (y-axis) vs "edutv" (x-axis). Which plot is the scatterplot? Graph A Graph B Graph C Graph D O O A T C 160- 150- 15- 10- edutv 160- b. Does the relationship appear roughly linear? O No, all the points do not fall on a straight line. Yes, there is no obvious curvature in the graph Yes, all the points fall on a straight line. O No, there are gaps in the graph along the x-axis. i. The hypotheses are: He: B₁0 HA:B10 ⒸH₂:1-3.067 165- HA: -3.067 c. Do the data provide strong evidence that high school GPA and first-year college GPA are associated? State the null and alternative hypotheses, report the p-value, and state your conclusion. ⒸH₂:₁ = 0 HA:B10 ⒸH₁:1=0 H₁:₁0 iii. The result of this hypothesis test is: edutv ii. The p-value for the test is (round to three decimal places--if that value is 0 then enter 0.): H₂:10 HA:21 > 0 Ho: 31-3.067 HA:B₁-3.067 About 13.71% of the variation in hours of educational TV watched per week is explained by the least-squares line. O hours of educational TV watched per week is predictive of score on analytical skills test. About 13.71% of the variation in score on analytical skills test is explained by the least- squares line. O hours of educational TV watched per week is not predictive of score on analytical skills test. e. What is the equation of the regression line? O hoursry - 165.144 score - 3.067 d. Interpret R². O The amount of variation in hours of educational TV watched per week that is explained by the least squares line. O hoursty = -3.067 score + 165.144 O score - 165.144 hoursry-3.067 Oscore -3.067 hoursry 165.144 - + O the amount of variation in score on analytical skills test that is explained by the least squares line. O The total amount of variation in the model. O The total unexplained variation in the model. f. Interpret the slope in the context of the model. For each Select an answer increase in Select an answer V. Select an answer Select an answer Select an answer g. Interpret the y-intercept in the context of the model or explain why it should not be interpreted. O When the score on analytical skills test of a gifted child is 0 the expected value of hours of educational TV watched per week is -3.067. When the score on analytical skills test of a gifted child is 0 the expected value of hours of educational TV watched per week is 165.144. O It does not make sense to interpret the y-intercept for this model because no gifted child in the data had a hours of educational TV watched per week anywhere close to 0. When the hours of educational TV watched per week of a gifted child is 0 the expected value of score on analytical skills test is 165.144.