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yaroslaw [1]
2 years ago
10

The owner of a chain of supermarkets notices that there is a positive correlation between the sales of beer and the sales of ice

cream over the course of the previous year. Seasons when sales of beer were above average, sales of ice cream also tended to be above average. Likewise, during seasons when sales of beer were below average, sales of ice cream also tended to be below average. A plausible explanation of these facts is that
A. the sales records must be in error. There should be no association between beer and ice cream sales.
B. drinking beer causes a desire for ice cream or eating ice cream causes a thirst for beer.
C. a scatterplot of monthly ice cream sales versus monthly beer sales would show that a straight line describes the pattern in the plot, but it would have to be a horizontal line.
D. the sale of beer and ice cream may both be affected by another variable such as the outside temperature.
E. None of the above.
Mathematics
1 answer:
Dmitry_Shevchenko [17]2 years ago
4 0

Answer:

D. the sale of beer and ice cream may both be affected by another variable such as the outside temperature.

Step-by-step explanation:

A. There is no evidence to indicate that the positive correlation observed is an error.

B. Even though there is a positive correlation between beer and ice cream sales, there is no evident cause and consequence relationship.

C. The relationship isn't necessarily linear.

D. It is plausible to assume that the sales of beer and ice cream are both affected by another variable like temperature. Both of the products are usually consumed cold and should see an increase in sales during warmer seasons and a decrease in sales during colder seasons.

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with 0.10 level of significance the P-VALUE that would be used in the hypothesis claim is 0.05%

Step-by-step explanation:

In hypothesis testing in statistics, we can say that the p-value is a probability of obtaining test results when we assume that the null hypothesis is correct.

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A Roper survey reported that 65 out of 500 women ages 18-29 said that they had the most say when purchasing a computer; a sample
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Answer:

Step-by-step explanation:

<u><em>Step(i):-</em></u>

<em>Given first random sample size n₁ = 500</em>

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

<em>First sample proportion </em>

<em>                              </em>p^{-} _{1} = \frac{65}{500} = 0.13

<em>Given second sample size n₂ = 700</em>

<em>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.</em>

<em>second sample proportion </em>

<em>                              </em>p^{-} _{2} = \frac{133}{700} = 0.19

<em>Level of significance = α = 0.05</em>

<em>critical value = 1.96</em>

<u><em>Step(ii)</em></u><em>:-</em>

<em>Null hypothesis : H₀: There  is no significance difference between these proportions</em>

<em>Alternative Hypothesis :H₁: There  is significance difference between these proportions</em>

<em>Test statistic </em>

<em></em>Z = \frac{p_{1} ^{-}-p^{-} _{2}  }{\sqrt{PQ(\frac{1}{n_{1} } +\frac{1}{n_{2} } )} }<em></em>

<em>where </em>

<em>         </em>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<em></em>

<em>        Q = 1 - P = 1 - 0.165 = 0.835</em>

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

<em>Z =  -2.76</em>

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

<em>Null hypothesis is rejected at 0.05 level of significance</em>

<em>Alternative hypothesis is accepted at 0.05 level of significance</em>

<u><em>Conclusion:</em></u><em>-</em>

<em>There is there is a difference between these proportions at α = 0.05</em>

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