Use the computer output to write the estimated linear regression equation for predicting Standing Reach from Wingspan. ŷ = Which of the following is the correlation coefficient for the linear relationship between Standing Reach and Wingspan? OA. 0.7753 OB. -0.8805 )C. -0.7753 D. 0.8805 Each additional 1 inch of Wingspan is associated with a(n) increase v of inches in Standing Reach.
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- The amount of time a student devotes to class attendance and revision: and the grade obtained are assumed to be linearly related. In a small class of 15 students, the following results were obtained for this linear relationship. Y = 30 +2.50.Y (0.55) (2.96) R'= 0.85 The standard errors are in parenthesis. a,Test the hypothesis that the intereept and slope are individually equal to zero (0) at the S% level of signiticance. (b) Construct n 95% conlidence interval for the true slope. (c)Compute the elasticity of grades with respect to time input and interpret your results. (d) Interpret your R' and explain how it relates to the slope of the regression line. (e) From the results and your answers in (a) to (d), does time input into class attendance and revision really influence the final grade?Determine the best (according to sum-of-squares-measure) curve y = Axb, through the dataabove.Transformed equation ln(y) = ln(A) + b ln(x)orY = a + bX.Determine the best (according to sum-of-squares-measure) curve y = Aebx , through the data above. Transformed equation ln(y) = ln(A) + bx or Y = a + bx.
- Refer to Exercise 16. Assume that T0 = 73.1 ± 0.1°F, Ta = 37.5 ± 0.2°F, k = 0.032 min−1 with negligible uncertainty, and T = 50°F exactly. Estimate t, and find the relative uncertainty in the estimate.Estimate the standard errors of the intercept and slope se B0 = ? (3 decimal places) se B1 = ? (3 decimal places) Test the hypothesis Ho: B0 = 0 versus H1: B0 not equal to 0 using a = 0.01 to = ? (3 decimal places) Test the hhpothesis Ho: Bo = 2500 versus H1 : B0 > 2500 using a 0.025 to = (3 decimal places)Test the null hypothesis that the slope is zero versus the two-sided alternative in the following setting using the alpha=0.05 signifiance level. n=20, yhat=28.5+1.4x, and SEb1=0.65
- On the second sheet is data which shows the rate of growth of a particular patch of bamboo vs daily high temperature.(a) Construct a scatterplot, including the equation of the line of best fit and value of R2.(b) What would the predicted growth rate be for a day with a temperature of 84◦?(c) Is there evidence, at α = 0.01, to support a claim that there is a linear relationship between temperature and growth rate? Please state clearly the null hypothesis, the alternative hypothesis, and what decision you make.The following regression model describes the relation between the number of days of experience in a job involving the wiring of electronic components and the number of components which were rejected stack N u m b e r space o f space r e j e c t s with hat on top equals 249 minus 1.4 space D a y s space o f space e x p e r i e n c e Based on this model, estimate the number of components rejected for an employee with 97 days of experience in the job. Round your answer to one decimal place.A researcher believes that there is a linear association between the level of potassiumcontent (y) in milligrams and the amount of fiber (x) in grams in cereal. The regression line forthe data is computed to be: ŷ = 36+27x rate. It was also computed that r = .62 a. If a cereal has 4 grams of fiber, what is its predicted potassium content?
- A forecaster used the regression equation Qt = a + bt + c1D1 + c2D2 + c3D3 and quarterly sales data for 2004I–2021IV (t = 1, ..., 64) for an appliance manufacturer to obtain the results shown below. Q is quarterly sales, and D1, D2 andD3 are dummy variables for quarters I, II, and III. DEPENDENT VARIABLE: QT R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 64 0.8768 107.982 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 30.0 12.80 2.34 0.0224 T 1.5 0.70 2.14 0.0362 D1 10.0 3.00 3.33 0.0015 D2 25.0 7.20 3.47 0.0010 D3 40.0 15.80 2.53 0.0140 Using the estimation results given above, the predicted level of sales in 2022II is _______ units.Regression analysis was applied between sales data (y) and advertising data (x) and the following information was obtained. SSR = 800SST = 1250Sample Size (n) = 12 The standard error of the estimate isA forecaster used the regression equation Qt = a + bt + c1D1 + c2D2 + c3D3 and quarterly sales data for 2004I–2021IV (t = 1, ..., 64) for an appliance manufacturer to obtain the results shown below. Q is quarterly sales, and D1, D2 and D3 are dummy variables for quarters I, II, and III. DEPENDENT VARIABLE: QT R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 64 0.8768 107.982 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 30.0 12.80 2.34 0.0224 T 1.5 0.70 2.14 0.0362 D1 10.0 3.00 3.33 0.0015 D2 25.0 7.20 3.47 0.0010 D3 40.0 15.80 2.53 0.0140 What is the estimated intercept of the trend line in the second quarter?