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SR = 450
<span>S = 450/R </span>
<span>(S - 3)(R + 5) = 450 </span>
<span>(450/R - 3)(R + 5) = 450 </span>
<span>(450 - 3R)(R + 5) = 450R </span>
<span>450R + 2250 - 3R^2 - 15R = 450R </span>
<span>3R^2 + 15R - 2250 = 0 </span>
<span>R^2 + 5R - 750 = 0 </span>
<span>R^2 + 30R - 25R - 750 = 0 </span>
<span>R(R + 30) - 25(R + 30) = 0 </span>
<span>(R + 30)(R - 25) = 0 </span>
<span>R ∈ {-30,25}
</span>
<span>Only positive numbers make sense in this context, therefore R = 25.</span>
Basing the results on Probability is calculation. The correct option among all the options that are given in the question is the third option or option "c". Probability is basically calculating the chance of an event happening based on an experiment that is performed. I hope the answer comes to your help.
Answer:
B) 28.53 unit²
Step-by-step explanation:
The diagonal AD divides the quadrilateral in two triangles:
- Triangle ABD
- Triangle ACD
Area of Quadrilateral will be equal to the sum of Areas of both triangles.
i.e.
Area of ABCD = Area of ABD + Area of ACD
Area of Triangle ABD:
Area of a triangle is given as:
Base = AB = 2.89
Height = AD = 8.6
Using these values, we get:
Thus, Area of Triangle ABD is 12.43 square units
Area of Triangle ACD:
Base = AC = 4.3
Height = CD = 7.58
Using the values in formula of area, we get:
Thus, Area of Triangle ACD is 16.30 square units
Area of Quadrilateral ABCD:
The Area of the quadrilateral will be = 12.43 + 16.30 = 28.73 units²
None of the option gives the exact answer, however, option B gives the closest most answer. So I'll go with option B) 28.53 unit²
Answer:
Solution-
We know that,
Residual value = Given value - Predicted value
The table for residual values is shown below,
Plotting a graph, by taking the residual values on ordinate and values of given x on abscissa, a random pattern is obtained where the points are evenly distributed about x-axis.
We know that,
If the points in a residual plot are randomly dispersed around the horizontal or x-axis, a linear regression model is appropriate for the data. Otherwise, a non-linear model is more appropriate.
As, in this case the points are distributed randomly around x-axis, so the residual plot show that the line of regression is best fit for the data set.
Hope this helps!
Step-by-step explanation:
