42/396 using compatible numbers
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Answer:
The answer to your question is 13 u²
Step-by-step explanation:
We know that the small triangle is surrounded by right triangles so we can use the Pythagorean theorem to find the lengths of the small triangle
AD² = 3² + 2²
Simplify
AD² = 9 + 4
AD² = 13
AD =
Find the area of the square
Area = side x side
Area = AD x AD
Area =
Area = 13 u²
We know that
</span>
find the residuals value
point 1) <span>(2, 94.5)
</span>yˆ=−6x+105.7
for x=2
Observed value=94.5
Predicted value=-6*2+105.7-----> 93.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[94.5-93.7]----> 0.8-----> 1
point (2,1)
point 2) <span>(3, 89)
</span>yˆ=−6x+105.7
for x=3
Observed value=89
Predicted value=-6*3+105.7-----> 87.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[89-87.7]----> 1.3-----> 1
point (3,1)
point 3) <span>(4,80)
</span>yˆ=−6x+105.7
for x=4
Observed value=80
Predicted value=-6*4+105.7-----> 81.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[80-81.7]----> -1.7-----> -2
point (4,-2)
point 4) <span>(6, 71)
</span>yˆ=−6x+105.7
for x=6
Observed value=71
Predicted value=-6*6+105.7-----> 69.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[71-69.7]----> 1.3 -----> 1
point (6,1)
point 5) <span>(7, 63)
</span>yˆ=−6x+105.7
for x=7
Observed value=63
Predicted value=-6*7+105.7-----> 63.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[63-63.7]----> 0.7 -----> 1
point (7,1)
using a graph tool
<span>graph the residual plot for the data set.
the points are
</span>(2,1) (3,1) (4,-2) (6,1) (7,1)
see the attached figure
The residual value is the observed value minus the predicted value.
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE]
where
Predicted value.--> the predicted value given the current regression equation
<span>Observed value. --> The observed value for the dependent variable</span>
find the residuals value
point 1) <span>(2, 94.5)
</span>yˆ=−6x+105.7
for x=2
Observed value=94.5
Predicted value=-6*2+105.7-----> 93.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[94.5-93.7]----> 0.8-----> 1
point (2,1)
point 2) <span>(3, 89)
</span>yˆ=−6x+105.7
for x=3
Observed value=89
Predicted value=-6*3+105.7-----> 87.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[89-87.7]----> 1.3-----> 1
point (3,1)
point 3) <span>(4,80)
</span>yˆ=−6x+105.7
for x=4
Observed value=80
Predicted value=-6*4+105.7-----> 81.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[80-81.7]----> -1.7-----> -2
point (4,-2)
point 4) <span>(6, 71)
</span>yˆ=−6x+105.7
for x=6
Observed value=71
Predicted value=-6*6+105.7-----> 69.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[71-69.7]----> 1.3 -----> 1
point (6,1)
point 5) <span>(7, 63)
</span>yˆ=−6x+105.7
for x=7
Observed value=63
Predicted value=-6*7+105.7-----> 63.7
RESIDUAL VALUE=[OBSERVED VALUE-PREDICTED VALUE
so
RESIDUAL VALUE=[63-63.7]----> 0.7 -----> 1
point (7,1)
using a graph tool
<span>graph the residual plot for the data set.
the points are
</span>(2,1) (3,1) (4,-2) (6,1) (7,1)
see the attached figure