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Dmitriy789 [7]
3 years ago
10

The average cost when producing x items is found by dividing the cost function, C(x), by the number of items, x. When is the ave

rage cost less than 100, given the cost function is C(x) = 10x + 180?
Mathematics
1 answer:
kakasveta [241]3 years ago
8 0

Answer:

<em>The average cost is less than 100 when the number of items x is greater than 2</em>

Step-by-step explanation:

The cost equation is C(x) = 10x + 180

The average cost Cp = \frac{C(x)}{x}

We want to know when the cost Cp is less than 100.

So

Cp

Cp = \frac{C(x)}{x}

10 + \frac{180}{x}

For x> 0

\frac{180}{90}

x> 2

The average cost is less than 100 when x>2

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You are 25 dollars in debt. You borrow 12 dollars more. What is the total amount of your debt? Justify your reasoning.
juin [17]

Answer:

you would be 37 dollars in debt.

Step-by-step explanation:

If you already are 25 dollars in debt and borrow 12 more dollars you would add that 12 dollars to your already 25 dollar in debt which would mean you would be 37 dollars in debt.

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What is the quotient (7x^(2))/(2x+6)-:(3x-5)/(x+3)
Irina18 [472]

Answer:

5.525

Step-by-step explanation:

1. (7x^(2))/(2x + 6) -:(3x - 5) / (x + 3) Solve Bold

   (7x^(2)) / (2x + 6) -:(3x - 5) / (x + 3)

2. 49x / 8x - (-2x) / 3x Solve Bold

   49x/8x-(-2x)/3x

3. 6.125 - .6 Solve Bold

Answer: 5.525

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2 years ago
A research firm tests the miles-per-gallon characteristics of three brands of gasoline. Because of different gasoline performanc
Anni [7]

Answer:

A.) At α = 0.05, is there a significant difference in the mean miles-per-gallon characteristics of the three brands of gasoline.

B.)<u><em>The advantage of attempting to remove the block effect is</em></u>

The completely randomized designs does not prove that H0 is incorrect only that it cannot be rejected.

Step-by-step explanation:

<em><u /></em>

<em><u>Using Two-way ANOVA method</u></em>

Given problem

<em><u>Observation              I          II       III          Row total (xr)</u></em>

A                              18 21 20            59

B                            24 26 27             77

C                            30 29 34             93

D                            22 25 24            71

<u>E                            20 23 24           63                      </u>

Col total (xc)             114 124 129        367

∑x²=9233→(A)

∑x²c/r

=1/5(114²+124²+129²)

=1/5(12996+15376+16641)

=1/5(45013)

=9002.6→(B)

∑x²r/c

=1/3(59²+77²+93²+71²+67²)

=1/3(3481+5929+8649+5041+4489)

=1/3(27589)

=9196.3333→(C)

(∑x)²/n

=(367)²/15

=134689/15

=8979.2667→(D)

Sum of squares total

SST=∑x²-(∑x)²/n

=(A)-(D)

=9233-8979.2667

=253.7333

Sum of squares between rows

SSR=∑x²r/c-(∑x)²/n

=(C)-(D)

=9196.3333-8979.2667

=217.0667

Sum of squares between columns

SSC=∑x²c/r-(∑x)²/n

=(B)-(D)

=9002.6-8979.2667

=23.3333

Sum of squares Error (residual)

SSE=SST-SSR-SSC

=253.7333-217.0667-23.3333

=13.3333

<u>ANOVA table</u>

Source                 Sums         Degrees      Mean Squares

of Variation       of Squares   of freedom

<u>                               SS                 DF              MS       F p-value</u>

B/ w     SSR=217.0667              4 MSR=54.2667    32.56 0.0001

rows

B/w     SSC=23.3333         c-1=2 MSC=11.6667        7 0.01

columns

<u>Error (residual)SSE=13.3333 (r-1)(c-1)=8 MSE=1.6667                  </u>

<u>Total SST=253.7333 rc-1=14                                                        </u>

Conclusion:

<u> 1. F for between Rows</u>

The critical region for F(4,8) at 0.05 level of significance=3.8379

The calculated F for Rows=32.56>3.8379

Therefore H0 is rejected

<u>2. F for between Columns</u>

The critical region for F(2,8) at 0.05 level of significance=4.459

We see that the calculated F for Colums=7>4.459

therefore H0 is rejected,and concluded that there is significant differentiating between columns

<u><em>Part B:</em></u>

To analyze the data for completely  randomized designs click on anova two factor without replication  in the data analysis dialog box of the excel spreadsheet.

The following table is obtained

Source DF             Sum                  Mean           F Statistic

<u>                 (df1,df2)    of Square (SS) Square (MS)                    P-value</u>

Factor A       1 1496.5444 1496.5444 769.6514          0.001297

Rows

Factor B -     2 19.4444           9.7222               5                  0.1667

Columns

Interaction

AB               2    3.8889   1.9444        0.1013         0.9045

<u> Error     12   230.4            19.2                                           </u>

<u>Total 17 1750.2778 102.9575                                                         </u>

<u />

<u>Factor - A- Rows</u>

Since p-value < α, H0 is rejected.

<u>Factor - B- Columns</u>

Since p-value > α, H0 can not be rejected.

The averages of all groups assume to be equal.

<u>Interaction AB</u>

Since p-value > α, H0 can not be rejected.

<u><em>The advantage of attempting to remove the block effect is</em></u>

The completely randomized designs does not prove that H0 is incorrect only that it cannot be rejected.

3 0
2 years ago
This is the 6th time i've asked this question. i'm so desperate, please help me!
OverLord2011 [107]

Answer:

Look at explanation.

Step-by-step explanation:

For the first four, take the first term and add the common difference to it to get the second term. Then add the common difference to the second term to get the third term, and so on until the 5th term. If the common difference is negative, you subtract it instead of adding it.

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a sprinkler that sprays water in a circular area can be adjusted to spray up to 30 feet. turning the radius-reduction screw on t
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Answer:

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Step-by-step explanation:

r=30 so you need to multiply that by 25% (30*.25=7.5)

30-7.5=22.5

A= pi(r)^2

A=pi*(22.5)^2

A=506.25pi

A=1590.43 or 1590.4 (to the nearest tenth)

4 0
3 years ago
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