11. Match each experiment design to its description. Answer Description Detects but can't resolve curvature 1/8th fraction resolution IV design Subset of a 3 design with extra center cells Can include up to 11 two-level study variables Response surface design, almost never used. Resolves main effects and interactions up to the k-factor interaction Five-level three-variable experiment Saturated model with main effects and all two-factor interactions Two levels of each of k variables (a) 3k (b) 2k (c) CC (23) (d) 27-3 (e) PB (12) (f) 2 plus centers (g) BB (k) (h) 25-1
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- A parallel coordinates plot, stretches or condenses the vertical axes so they look to be the same height for cach variable. This ables the rcader to see patterns in the relationship between variables across subjects. The graph below plots seasonal data for baseball players playing two different positions: • first base (1B), the solid blue line • second base (2B) the dashed red line. Players are compared across the three dimensions: • home runs (number of home runs hit) • steals (bases stolen) • batting average Home Runs Steals Average Cyyc yc Q. Looking at the data for first base (1B) players, which of the following statements is accurate for the sample? A. They steal bases quite often. B. Of the three variables the players batting average is the most consistent. • C. Using this graph, if you know how many homes runs a player hit, then you can estimate the steals for that player. Q. Looking at the data for second base (2B) players, which of the following statements is accurate for the…I need help with part b please. linear regression equation: y_hat = 6.6500 + 1.7000x table of temperatures versus converted sugars: Temperature, x Converted Sugar, y 1 8.2 1.1 8.1 1.2 8.7 1.3 9.9 1.4 9.6 1.5 8.7 1.6 8.2 1.7 10.6 1.8 9.3 1.9 9.2 2 10.73. Fit a least squares quadratic curve to the following data and estimate Y at X = 2.4 fig. 03 2 3. 4. గోలిగౌజి రర్ Y 1.7 1.8 2.3 2.3 A. The required least squares quadratic curve(parabola) and the estimated value of Y at X=2.4? * 大 O Y= 2- 0.5X + 0.2X^2; Y = 1.952 Y= 2-0.6X+ 0.2X^2; Y = 1.712 O Y= 2.1 - 0.5X + 0.2X^2;Y = 2.052 Y= 2- 0.5X + 0.4X^2; Y = 3.104
- Question 4-G- Trapezoids and x pd.mheducation.com/dle-assess-delivery-ui/v1.44.2/assessment/page/4?toker me County SD ps.classlink.com G- Trapezoias and Kites Hill Education Question 4 of 10 V Question 4 CD is the midsegment of trapezoid ABGF. If AB = 7 and FG = 11, find CD. Round your answer to nearest tenth, if necessary. Next Question Check Answer 2021 McGraw-Hill Education. All Rights Reserved. Privacy and Cookies | Terms of Use | MininHow can we make predictions using a fitted model in R?The effect size is the difference in the parameter of interest that represents a clinically meaningful difference. True False
- What does this plot show about the model being analysed?The data in the accompanying table include the appraised value, land area of the property in acres, and age, in years, for a small sample of 30 single-family homes in a small city. Perform a multiple regression analysis to predict appraised value based on land area of the property X1 and age, in years, X2 and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Determine the VIF for each independent variable in the model.Recently there has been increased use of stainless steel claddings in industrial settings. Claddings are used to finish the exterior walls of a building and help weatherproof the structure. To ensure the quality of claddings, it is essential to know how welding parameters impact the cladding process. The authors of “Mathematical Modeling of Weld Bead Geometry, Quality, and Productivity for Stainless Steel Claddings Deposited by FCAW” (J. Mater. Engr. Perform., 2012: 1862–1872) in vestigated how y 5 deposition rate was influenced by x1 = feed rate (Wf , in m/min) and x2 = welding speed (S, in cm/min). The following 22 observations correspond to the experiment condition where applied voltage was less than 30v: y: 2.718 3.881 2.773 3.924 2.740 3.870 x1 : 17.0 10.0 7.0 10.0 7.0 10.0 x 2 : 30 30 50 50 30 30 y: 2.847 3.901 2.204 4.454 3.324 3.319 x1 : 7.0 10.0 5.5 11.5 8.5 8.5 x2 : 50 50 40 40 40 20 The whole data and Question parts are attached
- Arm circumferences (cm) and heights (cm) are measured from randomly selected adult females. The 146pairs of measurements yield x=34.69cm, y=163.25cm, r=0.075, P-value=0.368, and y=157+0.1826x. Find the best predicted value of y (height) given an adult female with an arm circumference of 40.0cm. Let the predictor variable x be arm circumference and the response variable y be height. Use a 0.05 significance level. The best predicted value is ____cm.Answer #2 and #3 only with the same format as the examples given and please include the curve/rejection region drawing. Thanks.Arm circumferences (cm) and heights (cm) are measured from randomly selected adult females. The 139 pairs of measurements yield x = 31.99 cm, y = 163.33 cm, r= 0.032, P-value = 0.708, and y = 158 + 0.1703x. Find the best predicted value of y (height) given an adult female with an arm circumference of 35.0 cm. Let the predictor variable x be arm circumference and the response variable y be height. Use a 0.05 significance level. %3D ..... The best predicted value is cm. (Round to two decimal places as needed.)