I need help in the attached question parts a and b. Please give final answers and round as needed in each part A student was performed on a type of bearing to find the relationship of amount of wear y to x₁ = oil viscosity and x2 = load. The accompanying data were obtained. Complete parts (a) and (b) below.Click the icon to view the bearing data.(a) Estimate the unknown parameters of the multiple linear regression equation Hyx₁x₂ =ẞ0 +ẞ1×1 + B2×2.(Round the constant to the nearest integer as needed. Round the x₁-coefficient to two decimal places as needed. Round the x-coefficient to three decimal places as needed.)(b) Predict wear when oil viscosity is 25 and load is 1000.(Round to three decimal places as needed.) Bearing Datayx1X21931.485217222.2105511532.8136423215.88099042.8120512739.9 1110

Answer for Bearing Wear AnalysisData:We'll use the provided bearing data (y - wear, x1 - oil…

Answered: A student was performed on a type of…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 94E

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bThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.
A study of the amount of rainfall and the quantity of air pollution removed produced the accompanying data. Complete parts (a) and (b) below. Click the icon to view the daily rainfall and air pollution data. (a) Find the equation of the regression line to predict the particulate removed from the amount of daily rainfall. The equation is y = 154.76 + (6.72 )x. (Round to two decimal places as needed.)
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct (a) x = 140 cm scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (c) x = 164 cm E Click the icon to view the table of lengths and girths. (b) x = 172 cm (d) x = 158 cm The equation of the regression line is y = x+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Plot length on the horizontal axis and girth on the vertical axis. Choose the correct graph below. OA. OB. Oc. OD. 140 140 140 100 100 180 (a) Predict the girth for a length of 140 cm, if it is meaningful. Select the correct choice below and, if necessary, fill in the answer box within your choice. O A. ý= cm (Round to two…
A realty group would like to develop a regression model to help it set weekly rental rates (y) for beach properties during the summer season. The independent variables for this model are the number of bedrooms a property has (x1), its age in years (x2), and the number of blocks away from the ocean the property is (x3). Use the data provided to complete parts a through e below. Click the icon to view the data for randomly selected rental properties. a) Construct a regression model using all three independent variables. OX1 + (OX2 + (O X3 y = (Round the constant term to the nearest integer. Round the coefficients of x1, X2, and x3 to one decimal place as needed.)
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x=140 cm (b) x=172 cm (c) x=164 cm (d) x=158 cm Length, x Girth, y138 106168 130153 116144 105159 126158 119124 103137 103154 121148 111147 106145 110 The equation of the regression line is y=____x + ___
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x=140 cm (b) x=172 cm (c) x=164 cm (d) x=158 cm Length, x Girth, y137 107168 130153 117146 106160 124160 120123 103137 104155 119148 111146 107145 110 (a) The equation of the regression line is y= x+ . (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Plot length on the horizontal axis and girth on the vertical axis. (b) Predict the girth for a length of 140 cm, if it is…
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x= 140 cm E Click the icon to view the table of lengths and girths. (b) x= 172 cm (c) x= 164 cm (d) x= 158 cm - X The equation of the regression line is y =x+ (Round to two decimal places as needed.) Lengths and Girth Construct a scatter plot of the data and draw the regression line. Plot length on the horizontal axis and girth on the vertical axis. Choose the correct graph below. O A. Length, x Girth, y O B. Oc. 137 106 168 130 153 117 144 105 158 125 159 118 180 120 125 102 137 104 (a) Predict the girth for a length of 140 cm, if it is meaningful. Select the correct choice below and, if…
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a)x= 140 cm (b)x= 172 cm (c) x = 164 cm (d) x = 158 cm Click the icon to view the table of lengths and girths. The equation of the regression line is y=x+7 (Round to two decimal places as needed.). 4
Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9-0.00749x, where x represents weight. Complete parts (a) through (d). Click the icon to view the car data. The site to your captio V.UVITU. D. The slope is -0.00749 and the y-intercept is 58.9. c. What is the predictor variable? ... OA. The predictor variable is highway fuel consumption, which is represented by x. OB. The predictor variable is highway fuel consumption, which is represented by y. C. The predictor variable is weight, which is represented by x. OD. The predictor variable is weight, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2994 lb? The best predicted value of highway fuel consumption of a car that weighs 2994 lb is (Round to one decimal place as needed.) mi/gal.
ANSWER Parts D, E & F
A doctor wanted to determine whether there is a relation between a male’s age and his HDL (so-called good cholesterol). He randomly selected 9 of his patients and determined their HDL cholesterol. The data is reported in the table below: a) Compute the regression equation for HDL as a function of age. b)Interpret the meaning of the regression parameters.
A study of the amount of rainfall and the quantity of air pollution removed produced the accompanying data. Complete parts (a) and (b) below. Click the icon to view the daily rainfall and air pollution data. (a) Find the equation of the regression line to predict the particulate removed from the amourt of daily rainfall. The equation is y = + ( )x. (Round to two decimal places as needed.) Daily Rainfall and Air Pollution Data Daily Rainfall, Particulate Removed, y x (0.01 cm) (μg/m³) 4.3 127 4.5 120 5.9 116 5.6 118 6.1 113 5.2 117 3.7 131 2.1 142 7.4 108 Print Done ☑

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