please help i have to get a bunch of work done today! If T = 84 in, U = 88 in, V = 66 in, W = 140 in, X = 112 in, Y = 19 in, and
Z = 110 in, what is the perimeter of the object?
1 answer:
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Answer:
Population mean = 7 ± 2.306 ×
Step-by-step explanation:
Given - A university researcher wants to estimate the mean number
of novels that seniors read during their time in college. An exit
survey was conducted with a random sample of 9 seniors. The
sample mean was 7 novels with standard deviation 2.29 novels.
To find - Assuming that all conditions for conducting inference have
been met, which of the following is a 94.645% confidence
interval for the population mean number of novels read by
all seniors?
Proof -
Given that,
Mean ,x⁻ = 7
Standard deviation, s = 2.29
Size, n = 9
Now,
Degrees of freedom = df
= n - 1
= 9 - 1
= 8
⇒Degrees of freedom = 8
Now,
At 94.645% confidence level
α = 1 - 94.645%
=1 - 0.94645
=0.05355 ≈ 0.05
⇒α = 0.5
Now,
= 0.025
Then,
= 2.306
∴ we get
Population mean = x⁻ ± ×
= 7 ± 2.306 ×
⇒Population mean = 7 ± 2.306 ×
The quadratic model and the linear model of the table of values are the same
<h3>The linear model of the data</h3>To do this, we make use of a graphing calculator.
From the graphing calculator, we have the following summary:
- Sum of X = 0
- Sum of Y = 113
- Mean X = 0
- Mean Y = 22.6
- Sum of squares (SSX) = 10
- Sum of products (SP) = 28
The regression equation is
y = mx + b
Where
m = SP/SSX = 28/10 = 2.8
b = MY - bMX = 22.6 - (2.8*0) = 22.6
So, we have:
y = 2.8x + 22.6
See attachment for graph (1)
<h3>The quadratic model of the data</h3>To do this, we make use of a graphing calculator.
From the graphing calculator, we have the following summary:
- a = 0
- b = 2.8
- c = 22.6
0 X^2 +2.8 X +22.6
The regression equation is
y = ax^2 + bx + c
So, we have:
y = 0x^2 + 2.8x + 22.6
See attachment for graph (1)
<h3>Why both graphs look the same</h3>The graphs look the same because when the quadratic model is simplified, it gives the linear model.
This in other words mean that:
The quadratic model and the linear model of the table of values are the same
Read more about regression at:
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