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Talja [164]
3 years ago
9

You are comparing cell phones. The old model is 7/8 of an inch thick. The new model is 3/5 of an inch thick. How much thinner is

the new model than the old model?
Mathematics
1 answer:
Sloan [31]3 years ago
3 0

The new model is 11/40 of an inch thinner than the old model.

In order to do this, we need to subtract the new model from the old model.

7/8 - 3/5

In order to complete this, we will need common denominators.

35/40 - 24/40

Then simply complete the operation.

11/40

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Answer:

perimeter: 14

Area: 10

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AREA

LxB = 5x2 = 10

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How many 6-ounce cups can be filled from 4 gallons of juice? Hint 1 gallon = 128 ounces
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Sin(60-theta)sin(60+theta)​
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Step-by-step explanation:

\sin(60 -  \theta)  \sin(60 +  \theta)  \\  =  \{ \sin(60)  \cos( \theta)  -  \sin( \theta)   \cos(60)  \} \{ \sin(60)  \cos( \theta)  +  \cos(60)  \sin( \theta)  \} \\  =  \{ \frac{ \sqrt{3} }{2}  \cos( \theta)  -  \frac{1}{2}  \sin( \theta)  \} \{ \frac{ \sqrt{3} }{2}  \cos( \theta)  +  \frac{1}{2}  \sin( \theta)  \} \\  \\

from difference of two squares:

{ \boxed{(a - b)(a + b) = ( {a}^{2} -  {b}^{2} ) }}

therefore:

=  \{ {( \frac{ \sqrt{3} }{2}) }^{2}  { \cos }^{2}  \theta \} -  \{ {( \frac{ \sqrt{3} }{2} )}^{2}  { \sin }^{2}  \theta \} \\  \\  =  \frac{3}{4}  { \cos }^{2}  \theta -  \frac{3}{4}  { \sin}^{2}  \theta

factorise out ¾ :

=  \frac{3}{4} ( { \cos }^{2}  \theta  -  { \sin}^{2}   \theta) \\  \\  = { \boxed{ \frac{3}{4}  \cos(2 \theta) }}

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3 years ago
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Round 224.91 to the nearest whole number. Do not write extra zeros.
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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
3 years ago
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