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Reptile [31]
3 years ago
10

The battery standby duration (in hours) of a new model of cell phone is known to be normally distributed. Ten pieces of such new

model of cell phone supplied from the manufacturer are randomly chosen and the actual standby durations are recorded as below:
48.2 47.8 45.6 47.2 49.3
51.2 44.2 45.4 49.2 43.6

The manufacturer claimed that this new model of cell phone has the mean battery standby duration of longer than 46.5 hours. Test at 1% significance level if this claim is true.
Mathematics
1 answer:
emmainna [20.7K]3 years ago
8 0

x = number of hours

want to find probability (P) x >= 13

x is N(14,1) transform to N(0,1) using z = (x - mean) / standard deviation so can look up probability using standard normal probability table.

P(x >= 13) = P( z > (13 - 14)/1) = P(z > -1) = 1 - P(z < -1) = 1 - 0.1587 = 0.8413

To convert that to percentage, multiply 100, to get 84.13%

Please mark me brainliest

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Explain how you can find 4 x 754 using two different methods
Citrus2011 [14]

Solution: The value of 4\times 754  is 3,016.

Explanation:

The first method of multiplication is given below:

Multiply the ones digit by the multiplier and write the ones digit of the result in the bottom line and add the tens digit to the next resultant, and same happens with the next number multiplied. This method show in given figure.

The second method of multiplication is given below:

Write the digits as the addition of their place values and then multiply those place value by multiplier individually after that add the results.

4\times 754=4\times (700+50+4)\\4\times 754=4\times700+4\times50+4\times4\\4\times754=2800+200+16\\4\times754=3016

Therefore, the value of 4\times 754  is 3,016.

6 0
3 years ago
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What is the slope of the line that passes through the points shown in the table?
bonufazy [111]
The slope formula is y2-y1/x2-x1, this is considered rise over run. Simply subtract the y2 variable from the y1 variable, 1-(-1) =2 and then subtract the x2 variable from the x1 variable, 2-1 = 1. From there, ride (y) is on top and run(x) is on the bottom. Divide 2/1 which equal 2. The slope of this line is 2.
Hope this helped :)
4 0
3 years ago
What is the gcf of 36 &amp;90
NikAS [45]
The GCF of 36 and 90 is 18.
6 0
3 years ago
Please complete part A.+ B. And show all work. Thank you for your help.
Leno4ka [110]
-3,7) yes I am pretty sure that’s the answer
3 0
3 years ago
A local marketing company wants to estimate the proportion of consumers in the Oconee County area who would react favorably to a
Burka [1]

Answer:

C. n=423

Step-by-step explanation:

1) Notation and important concepts

Margin of error for a proportion is defined as "percentage points your results will differ from the real population value"

Confidence=90%=0.9

\alpha=1-0.9=0.1 represent the significance level defined as "a measure of the strength of the evidence that must be present in your sample before you will reject the null hypothesis and conclude that the effect is statistically significant".

\hat p=0.5 represent the sample proportion of consumers in the Oconee County area who would react favorably to a marketing campaig. For this case since we don't have enough info we use the value of 0.5 since is equiprobable the event analyzed.

z_{\alpha/2} represent a quantile of the normal standard distribution that accumulates {\alpha/2} on each tail of the distribution.

2) Formulas and solution for the problem

For this case the margin of error for a proportion is given by this formula

ME=z_{\alpha/2}\sqrt{\frac{\hat p(1-\hat p)}{n}}   (1)

For this case the confidence level is 90% or 0.9 so then the significance would be

\alpha=1-0.9=0.1 and \alpha/2=0.05

With \alpha/2=0.05, we can find the value for z_{\alpha/2} using the normal standard distribution table or excel.

The calculated value is z_{\alpha/2}=1.644854

Now from equation (1) we need to solve for n in order to answer the question.

\frac{ME}{z_{\alpha/2}}=\sqrt{\frac{\hat p(1-\hat p)}{n}}  

Squaring both sides:

(\frac{ME}{z_{\alpha/2}})^2=\frac{\hat p(1-\hat p)}{n}

And solving for n we got:

n=\frac{\hat p(1-\hat p)}{(\frac{ME}{z_{\alpha/2}})^2}

Now we can replpace the values

n=\frac{0.5(1-0.5)}{(\frac{0.04}{1.644854})^2}=422.739

And rounded up to the nearest integer we got:

n=423

3 0
3 years ago
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