Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.3 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 8.3 7.8 8.6 9.2 9.3 9.6 Weight (kg) 199 207 238 237 263 284 9 Click the icon to view the critical values of the Pearson correlation coefficient r. - X Critical Values of the Pearson Correlation Coefficient r %3D The regression equation is y =.x. (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r a-0.05 0.950 0.878 0.811 0.754 NOTE: To test Ho p=0 against H, p0, reject He Jf the absolute value of r is greater than the critical value in the table. In - 0.01 The best predicted weight for an overhead width of 2.3 cm is kg. (Round to one decimal place as needed.) 0.990 0.959 14 15 p.959 0.917 0.875 0.834 Can the prediction be correct? What is wrong with predicting the weight in this case? 17 18 19 10 11 12 13 14 15 16 17 18 19 20 25 30 35 40 0.707 0.666 0.632 l0,602 O A. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. 0.798 0.765 0.735 0.708 0.684 0.661 0 641 0.623 0.606 l0.590 l0 575 0 561 0.505 0.463 0.430 0.402 0.378 0.361 0.330 0.305 0.286 0.269 0 256 a- 0.01 OB. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. OC. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OD. The prediction can be correct. There is nothing wrong with predicting the weight in this case. 0.576 0.553 0.532 0.514 0.497 0.482 0.468 0.456 0.444 0.396 10.361 0.335 0.312 0.294 45 50 60 70 80 90 100 In 0.279 0 254 0.236 0.220 0.207 0.196 -0.05 Print Done
Family of Curves
A family of curves is a group of curves that are each described by a parametrization in which one or more variables are parameters. In general, the parameters have more complexity on the assembly of the curve than an ordinary linear transformation. These families appear commonly in the solution of differential equations. When a constant of integration is added, it is normally modified algebraically until it no longer replicates a plain linear transformation. The order of a differential equation depends on how many uncertain variables appear in the corresponding curve. The order of the differential equation acquired is two if two unknown variables exist in an equation belonging to this family.
XZ Plane
In order to understand XZ plane, it's helpful to understand two-dimensional and three-dimensional spaces. To plot a point on a plane, two numbers are needed, and these two numbers in the plane can be represented as an ordered pair (a,b) where a and b are real numbers and a is the horizontal coordinate and b is the vertical coordinate. This type of plane is called two-dimensional and it contains two perpendicular axes, the horizontal axis, and the vertical axis.
Euclidean Geometry
Geometry is the branch of mathematics that deals with flat surfaces like lines, angles, points, two-dimensional figures, etc. In Euclidean geometry, one studies the geometrical shapes that rely on different theorems and axioms. This (pure mathematics) geometry was introduced by the Greek mathematician Euclid, and that is why it is called Euclidean geometry. Euclid explained this in his book named 'elements'. Euclid's method in Euclidean geometry involves handling a small group of innately captivate axioms and incorporating many of these other propositions. The elements written by Euclid are the fundamentals for the study of geometry from a modern mathematical perspective. Elements comprise Euclidean theories, postulates, axioms, construction, and mathematical proofs of propositions.
Lines and Angles
In a two-dimensional plane, a line is simply a figure that joins two points. Usually, lines are used for presenting objects that are straight in shape and have minimal depth or width.
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