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vekshin1
3 years ago
12

owynn wants to buy a computer that normally costs $689.99 but is on sale for 5% off. If he waits two weeks the same computer wil

l be on sale for 15% off . Owynn wants to calculate the cost of the computer for both sales
Mathematics
1 answer:
EastWind [94]3 years ago
7 0
Sorry I don’t have a calculator with me atm but if you just put this into a calculator you’ll have the answer
So I use decimal multipliers for discounts to do this just change your percentage into a decimal with 100% as 1.00 so if it were increasing by 5% it would be 1.05 since it’s a sale the decimal would be 0.95 you can now multiply this to the original price to find this new price
So 689.99 x 0.95
If you follow this again for the 15% sale then the decimal multiplier would be 0.85
So 689.99 x 0.85
Just put these into a calculator and you’ll get your answers
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Answer:

The test statistic t is t=2.9037.

The null hypothesis is rejected.

For a significance level of 0.05, there is enough evidence to support the alternative hypothesis.

Step-by-step explanation:

<em>The question is incomplete:</em>

<em>The sample 1, of size n1=25 has a mean of 1.15 and a standard deviation of 0.31. </em>

<em>The sample 2, of size n2=25 has a mean of 0.95 and a standard deviation of 0.15. </em>

This is a hypothesis test for the difference between populations means.

The null and alternative hypothesis are:

H_0: \mu_1-\mu_2=0\\\\H_a:\mu_1-\mu_2\neq 0

The significance level is α=0.05.

The difference between sample means is Md=0.2.

M_d=M_1-M_2=1.15-0.95=0.2

The estimated standard error of the difference between means is computed using the formula:

s_{M_d}=\sqrt{\dfrac{\sigma_1^2+\sigma_2^2}{n}}=\sqrt{\dfrac{0.31^2+0.15^2}{25}}\\\\\\s_{M_d}=\sqrt{\dfrac{0.119}{25}}=\sqrt{0.005}=0.069

Then, we can calculate the t-statistic as:

t=\dfrac{M_d-(\mu_1-\mu_2)}{s_{M_d}}=\dfrac{0.2-0}{0.069}=\dfrac{0.2}{0.069}=2.9037

The degrees of freedom for this test are:

df=n_1+n_2-1=25+25-2=48

This test is a two-tailed test, with 48 degrees of freedom and t=2.9037, so the P-value for this test is calculated as (using a t-table):

P-value=2\cdot P(t>2.9037)=0.0056

As the P-value (0.0056) is smaller than the significance level (0.05), the effect is significant.

The null hypothesis is rejected.

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