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3 years ago

What is the second law of exponents

Mathematics

1 answer:

Vlad1618 [11]3 years ago

8 0

Answer:

When we divide powers of the same base, this means that we have to do is subtract the exponents. ... In other words, all we would need is to divide two powers that have the same exponent then the subtraction would lead to zero

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Speedy Swift is a package delivery service that serves the greater Atlanta, Georgia, metropolitan area. To maintain customer loy

Paladinen [302]

Answer:

See Explanation and attachments

Step-by-step explanation:

Given

See question for data

Solving (a):

The scale used is an ordinal scale.

Ordinals scale uses hierarchy arrangement and the data can be arranged as:

$EARLY ->> ON-TIME ->> LATE ->> LOST$

The variable of the delivery performance is qualitative because it is non-numerical

Solving (b): Frequency table

From the table, we have:

$On - Time = 57$ $Early = 19$ $Late = 9$ $Lost = 2$

So, the frequency distribution table is:

$\begin{array}{cc}{Performance}&{Frequency}&{On-Time}&{57}&{Early}&{19}&{Late}&{9} & {Loss} & {2} & {Total} & {87}\end{array}$

Solving (c): Frequency table.

To do this, we add another column (Relative Frequency) to the above table.

The relative frequency is calculated as:

$Relative\ Frequency = \frac{Frequency}{Total}$

So, we have:

$On - Time = \frac{57}{87} = 0.656$ $Early = \frac{19}{87} = 0.218$

$Late = \frac{9}{87} = 0.103$ $Lost = \frac{2}{87} = 0.023$

So, the frequency distribution table is:

$\begin{array}{ccc}{Performance}&{Frequency}&{Relative\ Frequency} & {On-Time}&{57}&{0.656}&{Early}&{19}&{0.218} & {Late}&{9} & {0.103} & {Loss} & {2} & {0.023} & {Total} & {87}&{1}\end{array}$

Solving (d & e): See attachment 1 for bar chart & attachment 2 for pie chart

Solving (f):

From the question, we understand that the object is to return 99% early or on time and never to lose a package.

The analysis is as follows:

Early and On-Time (ET) packages

$ET = \frac{Early + On-Time}{Total} * 100\%$

$ET = \frac{57 + 19}{87} * 100\%$

$ET = \frac{76}{87} * 100\%$

$ET = \frac{7600}{87} \%$

$ET = 87.4 \%$

Lost packages

$Lost = \frac{Lost}{Total} * 100\%$

$Lost = \frac{2}{87} * 100\%$

$Lost = \frac{200}{87} \%$

$Lost = 2.30\%$

<em>From the above analysis, we can see that 87.4% of the packages were delivered early enough and 2.30% were lost. </em>

<em>The fraction of packages delivered early can be improved and the fraction of lost packages can be reduced by exploring the chances of taking alternative routes when possible. </em>