a. Compute SST, SSR, and SSE. SSE 2442 SST 2272 SSR 170 A sales manager collected the following data on y = annual sales and x = years of experience. The estimated regression equation for these data is ŷ = 80 + 4x. Click on the datafile logo to reference the data. DATA file Salesperson 1 Years of Experience Annual Sales ($1000s) 1 80 2 3 97 3 4 92 4 4 102 5 6 103 6 8 111 7 10 119 8 10 123 9 11 117 10 13 136 a. Compute SST, SSR, and SSE.
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- The data show systolic and diastolic blood pressure of certain people. Find the regression equation, letting the systolic reading be the independent (x) variable. If one of these people has a systolic blood pressure of 139 mm Hg, what is the best predicted diastolic blood pressure? Systolic 130 131 122 131 119 138 122 123 Diastolic 93 71 67 98 59 100 89 92 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y= (Round to two decimal places as needed.) What is the best predicted diastolic blood pressure? y=enter your response here (Round to one decimal place as needed.)The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 65 55 70 86 74 82 65 75 77 73 Grades on Final 78 79 72 92 83 75 78 94 74 69The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $6 million. Is the result close to the actual number of viewers, 2.9 million? Use a significance level of 0.05
- Please answer parts d, e and f.Type the regression equation for “Area” and “Biomass” in context into your document. Interpret the slope of the regression line (in context of this data set) for “Area” and “Biomass”. is it meaningful to interpret the y-intercept for “Area” and “Biomass”? Why or why not?Refer to the data set:(-1, 2), (1, 3), (1, 5), (2, 7), (3, 8), (4, 11)Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 10
- For each of the following data sets: plot the data, determine the regression equation, and add the graph of the regression to the graph.Please answer question parts a. & b. in the image. Part b. is slightly cut off this is what it says: b. Based on the estimated regression model, what percentage of the total variation in average monthly sales can be explained by store size? ______ % (Type an integer or decimal rounded to one decimal place as needed.) In the second image is the data needed to answer the questions. Thanks!The reason why estimators have a sampling distribution is that: oooo Economics is not a precise science In real life you typically get to sample many times Individuals respond different to incentives The values of the explanatory variable and the error term differ across samples.
- The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…You run a regression of Iowa crop yield per acre on average growing season temperature, average growing season rainfall, and average growing season total precipitation. You get the following results: Data Table Variable Coefficient Std. Error t value p-value (Intercept) 103.4183 42.891 2.411 0.0161 Temp 1.9446 0.0636 30.577 0.000001 Rain 43.988 21.1693 2.078 0.038 Precipitation −46.5995 21.1768 −2.2 0.028 What problem(s) do you think this regression has? Group of answer choices a) Omitted variable of how much fertilizer was used. This will impact the Rain coefficient. b) Multi-collinearity. Rain and precipitation are very highly correlated, their estimates are unreliable. c) Huge outliers are driving the result from years in drought. d) No issues, regression is probably fine.Which of the following data sets or plots could have a regression line with a negative slope? Select all that apply: the number of miles a ship has traveled as a function of the number of years since it was launched the number of miles a ship has traveled each year as a function of the number of years since it was launched the number of cats living in an abandoned lot as a function of the number of years since the building was torn down the number of cats born each year in an abandoned lot as a function of the number of years since the building was torn down