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Kisachek [45]
3 years ago
5

How do I find the variable?

Mathematics
1 answer:
velikii [3]3 years ago
7 0

Answer:

x = 8

Step-by-step explanation:

Step 1:

9x = 72        Def. of an isosceles triangle

Step 2:

x = 72 ÷ 9        Divide

Answer:

x = 8

Hope This Helps :)

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What is the value of x to the nearest tenth? <br> A) 3.3<br> B) 9.5<br> C) 8.0<br> D) 4.7
Alisiya [41]

Answer:

D) 4.7

Step-by-step explanation:

this perpendicular connection with the circle center cuts this intersecting line in half, which is therefore on both sides of x 6.5 long.

I assume 16 is the diameter, so the radius is 8.

therefore, x is one side of the right-angled triangle of

radius

x

half of the intersecting line

Pythagoras

8² = x² + (6.5)²

64 = x² + 42.25

21.75 = x²

x = 4.7

6 0
3 years ago
If pamela has 20 of string does she have enough to make bracelets for 4 of her friends?
JulijaS [17]

How much string is needed to make one bracelet?

From the information given, she should have enough to make 4 bracelets for each of her friends if she uses 5 strings per bracelet.

5 0
3 years ago
#10 i The table shows the admission costs (in dollars) and the average number of daily visitors at an amusement park each the pa
lions [1.4K]

The line of best fit is a straight line that can be used to predict the

average daily attendance for a given admission cost.

Correct responses:

  • The equation of best fit is; \underline{ \hat Y = 1,042 - 4.9 \cdot X_i}
  • The correlation coefficient is; r ≈<u> -0.969</u>

<h3>Methods by which the line of best fit is found</h3>

The given data is presented in the following tabular format;

\begin{tabular}{|c|c|c|c|c|c|c|c|c|}Cost, (dollars), x&20&21&22&24&25&27&28&30\\Daily attendance, y&940&935&940&925&920&905&910&890\end{array}\right]

The equation of the line of best fit is given by the regression line

equation as follows;

  • \hat Y = \mathbf{b_0 + b_1 \cdot X_i}

Where;

\hat Y = Predicted value of the<em> i</em>th observation

b₀ = Estimated regression equation intercept

b₁ = The estimate of the slope regression equation

X_i = The <em>i</em>th observed value

b_1 = \mathbf{\dfrac{\sum (X - \overline X) \cdot (Y - \overline Y) }{\sum \left(X - \overline X \right)^2}}

\overline X = 24.625

\overline Y = 960.625

\mathbf{\sum(X - \overline X) \cdot (Y - \overline Y)} = -433.125

\mathbf{\sum(X - \overline X)^2} = 87.875

Therefore;

b_1 = \mathbf{\dfrac{-433.125}{87.875}} \approx -4.9289

Therefore;

  • The slope given to the nearest tenth is b₁ ≈ -4.9

b_0 = \mathbf{\dfrac{\left(\sum Y \right) \cdot \left(\sum X^2 \right) - \left(\sum X \right) \cdot \left(\sum X \cdot Y\right)} {n \cdot \left(\sum X^2\right) - \left(\sum X \right)^2}}

By using MS Excel, we have;

n = 8

∑X = 197

∑Y = 7365

∑X² = 4939

∑Y² = 6782675

∑X·Y = 180930

(∑X)² = 38809

Therefore;

b_0 = \dfrac{7365 \times 4939-197 \times 180930}{8 \times 4939 - 38809} \approx \mathbf{1041.9986}

  • The y-intercept given to the nearest tenth is b₀ ≈ 1,042

The equation of the line of best fit is therefore;

  • \underline{\hat Y = 1042 - 4.9 \cdot X_i}

The correlation coefficient is given by the formula;

\displaystyle r = \mathbf{\dfrac{\sum \left(X_i - \overline X) \cdot \left(Y - \overline Y \right)}{ \sqrt{\sum \left(X_i - \overline X \right)^2 \cdot \sum \left(Y_i - \overline Y \right)^2} }}

Where;

\sqrt{\sum \left(X - \overline X \right)^2 \times \sum \left(Y - \overline Y \right)^2}  = \mathbf{446.8121}

\sum \left(X_i - \overline X \right) \times \left(Y - \overline Y\right) = \mathbf{-433.125}

Which gives;

r = \dfrac{-433.125}{446.8121}  \approx \mathbf{-0.969367213}

The correlation coefficient given to the nearest thousandth is therefore;

  • <u>Correlation coefficient, r ≈ -0.969</u>

Learn more about regression analysis here:

brainly.com/question/14279500

7 0
3 years ago
1367324+3469345-75369x3429x0=??
Mnenie [13.5K]

Answer:

it's ans is 4761300

Step-by-step explanation:

by using BODMAS

75369×3429×0

=0

0+1367324+3469345

=4836669

4836669-75369

=4761300

plz mark me as brainliest plz

5 0
3 years ago
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