Answer:
The answer to the equation is B.
Step-by-step explanation:
Answer:
1) By SAS theorem, ΔADE≅ΔCDF
2) By SSS theorem, ΔBDE≅ΔBDF
Step-by-step explanation:
Consider isosceles triangle ABC (see diagram).
1. In triangles ADE and CDF:
- AD≅DC (since BD is median, then it divides side AC in two congruent parts);
- AE≅CF (given);
- ∠A≅∠C (triangle ABC is isosceles, then angles adjacent to the base are congruent).
By SAS theorem, ΔADE≅ΔCDF.
2. In triangles BDE and BDF:
- side BD is common;
- DE≅DF (ΔADE≅ΔCDF, then congruent triangles have congruent corresponding sides);
- BE≅FB (triangle ABC is isosceles, AB≅BC, AE≅CF, then BE=AB-AE, FB=BC-CF).
Be SSS theorem, ΔBDE≅ΔBDF.
Remark
There is a very quick way to do this. Scan the numbers. You want 75 to be the mean, so make it so. Then figure out how many up or down you go from 75. I'll make a list of the givens.
72 - 3 72 is three down from 75 so call it - 3
75 0 That is your desired average so it adds nothing to the score.
74 -1 It is one down from 75, so call it -1
76 +1 It is one up from 75
76 +1 it is one up from 75
75 adds 0.
-3 - 1 + 1 + 1 = - 2
That comes - 2/7 which means that you need a score just under 75. So 75 will give you the right average of 75
Answer B <<<<<
Using Visual inspection, the model which fits the data in the distribution better is the power function.
The power and linear functions can of the data can both be modeled using technology,
<u>Using Technology</u> :
The power function in the form
which models the data is ![1.926x^{1.662}](https://tex.z-dn.net/?f=%201.926x%5E%7B1.662%7D%20)
The linear function in the form
which models the data is ![y = 7.429x - 8.333](https://tex.z-dn.net/?f=%20y%20%3D%207.429x%20-%208.333)
- Where A = intercept and B = slope
- From the model, correlation coefficient given by the power and linear models are 0.999 and 0.986 respectively.
- Hence, the power model is a better fit for the data than the linear model.
Therefore, Inspecting the models visually, the power function fits the data better as the points on the curve are closer to the regression line than on the linear model.
Learn more :brainly.com/question/18405415