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pochemuha
2 years ago
15

Greatest common factor of the following monomials 40a^3b and 24a^3b^5

Mathematics
1 answer:
timofeeve [1]2 years ago
8 0

the gcf is product of gcf each factor,

gcf(40a³b,24a³b⁵)

= gcf(40,24)•gcf(a³,a³)•gcf(b,b⁵)

= 8a³b

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Because Bernard has some health issues, he must pay 15% more for life insurance. About how much more annually will a $115,000 10
olga2289 [7]

From the given table, the annual premium rate as a percentage of value insured a person at age 35 has to pay is 0.14%.

  • The amount more annually a $115,000 10-year term insurance at age 35 cost Bernard than someone of the same age without health issues is option d. <u>$24</u>

Reasons:

The data in the table are presented as follows;

\begin{tabular}{|c|c|c|}Age&Annual Insurance Premiums (per \$1,000 of face value)&\\&10-Year Term &\\&Male&Female\\35&1.40&1.36\\40&1.64&1.59\\45&2.07&2.01\end{array}\right]

From the above table, we have that the amount a 35 year old without health issues will pay per $1,000 is $1.40

Therefore, the amount to be paid for $115,000 is 115 × $1.4 = $161

The amount Bernard pays = 15% more = 1.15 × $161 = $185.15

Therefore;

The amount more Bernard has to pay = $185.15 - $161 = $24.15 ≈ <u>$24</u>

Learn more about insurance premiums here:

brainly.com/question/3053945

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Please help me please
oksano4ka [1.4K]

Answer:

See below.

Step-by-step explanation:

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105 - 75

30

2. 3x + 4x - 5x

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2x

3. 3(x-2) + 4(x-2) - 5(x-2)

3x - 6 + 4x - 8 - 5x + 10

2x - 4

4. (I'm not typing that out)

7.5x + 19.5 + 10x + 26 - 12.5x - 32.5

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8 0
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
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dusya [7]
You have to foil out the problem
(2x+8)(2x+8)
4x^2+16x+16x+64
4x^2+32x+64
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