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Paraphin [41]
3 years ago
13

In which number is the value of the 7 ten times the value of 7 in the number 1,273?

Mathematics
2 answers:
notsponge [240]3 years ago
8 0

Answer:

1723

Step-by-step explanation:

frosja888 [35]3 years ago
4 0

Answer:

1723

Step-by-step explanation:

10 x 70=700

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A sales analyst listed the probabilities of profits and losses in dollars for a certain company. Find the mean for the probabili
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Answer: The mean for the probability distribution will be 69.25.

Explanation:

Since we given that

At\ x_1= -500 ,P(x) = 0.076\\

At\ x_2= -250,P(x)= 0.191\\

At\ x_3=0, P(x)= 0.265\\

At\ x_4=250 , P(x)= 0.316\\

At\ x_5=500 , P(x)= 0.152\\

As we know the formula for expectation of x i.e. known as mean for the probability distribution,

E(x)=x\sum_{1}^{5} P(x)

So, we  apply this formula in our case,

-500\times 0.076+(-250)\times 0.191+0\times 0.265+250\times 0.316+500\times 0.512=69.25

\mu = 69.25

Hence, the mean for the probability distribution will be 69.25.



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What can you conclude about the angles formed by two parallel lines cut by a transversal?
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°Alternate interior angles are equal
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°Vertically opposite angles are equal
°Angles on the same side of tranversal are supplementary

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I think the answer would be 75%, meaning that the orange juice would have a greater percentage of water.
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The management of a relatively new social networking website named BooglePlus is conducting a pilot study comparing use of its o
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Answer:

0.0623 ± ( 2.056 )( 0.0224 ) can be used to compute a 95% confidence interval for the slope of the population regression line of y on x

Step-by-step explanation:

Given the data in the question;

sample size n = 28

slope of the least squares regression line of y on x or sample estimate = 0.0623

standard error = 0.0224

95% confidence interval

level of significance ∝ = 1 - 95% = 1 - 0.95 = 0.05

degree of freedom df = n - 2 = 28 - 2 = 26

∴ the equation will be;

⇒ sample estimate ± ( t-test) ( standard error )

⇒ sample estimate ± ( t_{\alpha /2, df) ( standard error )

⇒ sample estimate ± ( t_{0.05 /2, 26) ( standard error )

⇒ sample estimate ± ( t_{0.025, 26) ( standard error )

{ from t table; ( t_{0.025, 26) = 2.055529 = 2.056

so we substitute

⇒ 0.0623 ± ( 2.056 )( 0.0224 )

Therefore, 0.0623 ± ( 2.056 )( 0.0224 ) can be used to compute a 95% confidence interval for the slope of the population regression line of y on x

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