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3 years ago

15 points!!!!!!

2 answers:

7 0

$4\frac{3}{16}+5\frac{1}{16}=(4+5)+(\frac{3}{16}+\frac{1}{16})=9+\frac{3+1}{16}=9+\frac{4}{16}=9+\frac{4:4}{16:4}\\\\=9+\frac{1}{4}=\boxed{9\frac{1}{4}=9\frac{1\cdot25}{4\cdot25}=9\frac{25}{100}=9.25}\leftarrow total\ ribbon\\\\Answer:\boxed{9.25\cdot\$1.60=\$14.80}$

zzz [600]3 years ago

4 0

4 3/16=(4·16+3)/16=67/16=4,1875 (pink ribbon)
5 1/16=(5·16+1)/16=81/16=5,0625 (green ribbon)
4,1875+5,0625=9,25 (total)
9,25·1,60=$14,8

Answer: $14,8

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3 years ago

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Answer:

Step-by-step explanation:

Step(i):-

Given first random sample size n₁ = 500

Given Roper survey reported that 65 out of 500 women ages 18-29 said that they had the most say when purchasing a computer.

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$p^{-} _{1} = \frac{65}{500} = 0.13$

Given second sample size n₂ = 700

Given a sample of 700 men (unrelated to the women) ages 18-29 found that 133 men said that they had the most say when purchasing a computer.

second sample proportion

$p^{-} _{2} = \frac{133}{700} = 0.19$

Level of significance = α = 0.05

critical value = 1.96

Step(ii):-

Null hypothesis : H₀: There is no significance difference between these proportions

Alternative Hypothesis :H₁: There is significance difference between these proportions

Test statistic

$Z = \frac{p_{1} ^{-}-p^{-} _{2} }{\sqrt{PQ(\frac{1}{n_{1} } +\frac{1}{n_{2} } )} }$

where

$P = \frac{n_{1} p^{-} _{1}+n_{2} p^{-} _{2} }{n_{1}+ n_{2} } = \frac{500 X 0.13+700 X0.19 }{500 + 700 } = 0.165$

Q = 1 - P = 1 - 0.165 = 0.835

$Z = \frac{0.13-0.19 }{\sqrt{0.165 X0.835(\frac{1}{500 } +\frac{1}{700 } )} }$

Z = -2.76

|Z| = |-2.76| = 2.76 > 1.96 at 0.05 level of significance

Null hypothesis is rejected at 0.05 level of significance

Alternative hypothesis is accepted at 0.05 level of significance

Conclusion:-

There is there is a difference between these proportions at α = 0.05

3 0

3 years ago