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labwork [276]
3 years ago
11

Shannon went to an auto repair shop and paid $339.50, which included parts that cost $112 and 3.5 hours of labor. Joni went to a

n auto repair shop and paid $455, which included parts that cost $310 and 2.5 hours of labor. Which correctly compares the cost of the labor?
Mathematics
1 answer:
Illusion [34]3 years ago
8 0

Answer:

Option A, Shannon paid $ 7 per hour to labor than Joni

Step-by-step explanation:

Total amount paid by Shannon = $339.50

Total cost of 3.5 hours of labor paid by Shannon =($339.50- $112)

Total cost of 3.5 hours of labor paid by Shannon = $227.5/3.5

Total cost of 1 hours of labor paid by Shannon  $65 per hour

Total amount paid by Joni = $455

Total cost of 2.5 hours of labor paid by Joni =  $455- $$310

Total cost of 1 hours of labor paid by Joni = $145/2.5

Total cost of 1 hours of labor paid by Joni = $ 58

Hence, Shannon paid $ 7 per hour to labor than Joni

Option A is correct

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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.

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3 years ago
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vlabodo [156]

Answer:

The equation of the line is:  y = x + 4

Step-by-step explanation:

When we are given two points passing through a line, we can find the equation of the line by using two - point form.

Two - point form:    $ \frac{\textbf{y - y}_\textbf{1}}{\textbf{y}_{\textbf{2}} \textbf{-} \textbf{y}_{\textbf{1}}} = \frac{{\textbf{x - x}_\textbf{1}}}{\textbf{x}_{\textbf{2}} \textbf{-} \textbf{x}_{\textbf{1}}    }$

where $ (x_1, y_1) \hspace{3mm} \& \hspace{3mm} (x_2, y_2) $ are the points passing through the line.

Here, let us take two points (can be any two):

$(x _1, y_1) = (1, 5) $ and

$ (x_2, y_2) = (5, 9) $

Therefore, we have:

$ \frac{y - 5}{9 - 5} = \frac{x - 1}{5 - 1} $

$ \iff \frac{y - 5}{4} = \frac{x - 1}{4} $

$ \iff y - 5 = x - 1 $

$ \implies y = x - 1 + 5 $

$ \implies y = \textbf{x + 4} $ which is the required answer.

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