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elixir [45]
2 years ago
14

Can you help me with these questions pls

Mathematics
1 answer:
Korvikt [17]2 years ago
4 0
So for the angles you would take 87 and 17 and subtract it and that’ll be your answer
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Freeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee pointsssssssssssssssssssssssssssssssssssssssssssssss
GaryK [48]

Answer:

thanks?

Step-by-step explanation:

6 0
2 years ago
Ashley took out a $1500 loan and promises to pay it back with 5% interest after 18 months. How much interest would she have to p
babymother [125]

Answer:

75.00

Step-by-step explanation:

1500*0.05

because 5% is her interest you convert 5% into a decimal = 0.05

then you multiply 1500 by 0.05 and you get 75.00

5 0
3 years ago
on monday mrs jones spent 32 dollars on 4 books. on tuesday she spent $16 on more books.each book cost the same amount. how many
Gala2k [10]
She bought 6 books in all because if you divide the whole number of all the books in all which is $32 divide that by 4 so..

32 
÷4=8

so thats just the 4 books then she buys 16 more dollars worth that which you then multiply 4 times 8 which give you 16 so you then add the 4 books to the other 2 books spent on the next day

ANSWER: 6 BOOKS IN ALL 
6 0
3 years ago
How did the Germanic tribes govern thier kingdoms
Verdich [7]

The Germanic tribes were governed by unwritten costumes.

3 0
3 years ago
A linear regression model is fitted to the data x y 37.0 65.0 36.4 67.2 35.8 70.3 34.3 71.9 33.7 73.8 32.1 75.7 31.5 77.9 with x
andrey2020 [161]

Answer:

b0= 144.59

b= -2.12

Se²= 1.02

99%CI E(Y/X=35): [68.78; 71.99]

Step-by-step explanation:

Hello!

I've arranged the given data:

X: 37.0, 36.4, 35.8, 34.3, 33.7, 32.1, 31.5

Y: 65.0, 67.2, 70.3, 71.9, 73.8, 75.7, 77.9

The equation of the linear regression model is:

Yi= β₀ + βXi + εi

Where

Yi is the dependent variable

Xi is the independent variable

εi represents the errors or residues

β₀ is the intercept of the line

β is the slope

The conditions to make a linear regression analysis are:

For each given value of X, there is a population of Y~N(μy;σy²)

Each value of Y is independent of the others.

The population variances of each population of Y are equal.

From these conditions the following characteristic is deduced:

εi~N(0;σ²)

The parameters of the regression are:

β₀, β, and σ²

If the conditions are met then you can estimate the regression line:

Yi= bo * bXi + ei.

And the point estimation of the parameters can be calculated using the formulas:

β₀ ⇒ b0= (∑y/n)-b(∑x/n)

β ⇒ b= [∑xy- ((∑x)(∑y))/n]/(∑x²-((∑x)²/n))

σ²⇒ Se²= 1/(n-2)*[∑y²-(∑y)²/n - b²(∑x²-(∑x)²/n)]

n= 7

∑y= 501.80

∑y²= 36097.88

∑x= 240.80

∑x²= 8310.44

∑xy= 17204.87

b0= 144.59

b= -2.12

Se²= 1.02

The estimated regression line is:

Yi= 144.59 -2.12Xi

You need to calculate a 99%CI E(Y/X=35), the formula is:

(b0 + bX0) ± t_{n-2;1-\alpha /2}*\sqrt{S_e^2(\frac{1}{n}+\frac{(X_0-X[bar])^2}{sumX^2-(\frac{(sumX)^2}{n} )} )}

(144.59 + (-2.12*35)) ± 4.032*\sqrt{1.02(\frac{1}{7}+\frac{(35-34.4)^2}{8310.44-(\frac{(240.80)^2}{7} )} )}

[68.78; 71.99]

With a 99% confidence level youd expect that the interval [68.78; 71.99] contains the true value of the average of Y when X= 35.

I hope it helps!

6 0
3 years ago
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