Answer:
132cm²
Step-by-step explanation:
You can find the area of the entire shape by calculating the area of seperate smaller shapes.
You can split them up into 2 rectangles, one with a base of 7cm and height of 4cm, and another one with a base of 7cm and height of 8cm. You also have 2 triangles, with a base of 19cm - 7cm / 2 = 6cm since it is the entire bottom line subtracted by the top; they will have a height of 8cm each.
To calculate the rectangles areas, simply do length x width, which are:
4cm x 7cm = 28cm²
8cm x 7cm = 56cm²
Next, since the triangles are similar in measurements, instead of doing the normal calculation to find the area of a triangle, you can do base times height again, which is:
6cm x 8cm = 48cm²
Add all the answers together to get the final area, which is 132cm².
Step-by-step explanation:
6 customers per hour
(given mean)
Pdf is given by:
Pdf:
for
a)
&\text { c) }\\
&P(8<X<15)\\
&\begin{array}{l}
=P(X<15)-P(X<8) \\
=(1-\exp (-15 / \beta))-(1-\exp (-8 / \beta)) \\
=\exp (-8 / \beta)-\exp (-15 / \beta) \\
=e^{-0.8}-e^{-1.5} \\
=0.449329-0.22313 \approx 0.226199 \approx 0.2262 \\
\quad P(8<X<15)=0.2262
\end{array}
\end{aligned}
\begin{aligned}
&\text { d) }\\
&\begin{array}{l}
P(14<X<22) \\
=P(X<22)-P(X<14) \\
=(1-\exp (-22 / \beta))-(1-\exp (-14 / \beta)) \\
=\exp (-14 / \beta)-\exp (-22 / \beta) \\
=e^{-1.4}-e^{-2.2} \\
=0.246597-0.110803 \approx 0.135794 \approx 0.1358 \\
P(X<14 \cup X>22)=1-P(14<X<22)=1-0.1358=0.8642 \\
P(X<14 \cup X>22)=0.8642
\end{array}
\end{aligned}
Answer:
Yes she is correct.
Step-by-step explanation:
I think she is correct.
Find the rate:
51 ÷ 3 = £17
1 kg of beef = £17
2 kg of beef = £17 × 2 = £34
2 kg beef = £34
Answer:
Kindly check explanation
Step-by-step explanation:
Given the data:
Age(x)
7
8
5
8
8
7
7
7
9
8
5
8
6
5
8
Height (Y)
47.3
48.8
41.3
50.4
51
47.1
46.9
48
51.2
51.2
40.3
48.9
45.2
41.9
49.6
The estimated regression equation:
ŷ = 2.73953X + 27.91395
Where ;
X = independent variable
ŷ = predicted or dependent variable
27.91395 = intercept
C.) To obtain the variation in sample values of height estimated by the model, we obtain the Coefficient of correlation:
Using the online pearson correlation Coefficient calculator :
The correlation Coefficient is 0.9696.
which means that the regression model estimated in part (b) explains approximately (0.9696 * 100) = 96.96% = 97% of the variation in the height in the sample.