Questions: A Food Marketing Institute found that 32% of households spend more than 125 a week on groceries. Assume the population proportion is 0.32 and a simple random sample of 368 households is selected from the population. What is the probability that the sample proportion of households spending more than 125 a week is less than 0.34?

A Food Marketing Institute found that 32% of households spend more than 125 a week on groceries. Assume the population proportion is 0.32 and a simple random sample of 368 households is selected from the population. What is the probability that the sample proportion of households spending more than 125 a week is less than 0.34?
Transcript text: A Food Marketing Institute found that $32 \%$ of households spend more than $\$ 125$ a week on groceries. Assume the population proportion is 0.32 and a simple random sample of 368 households is selected from the population. What is the probability that the sample proportion of households spending more than $\$ 125$ a week is less than 0.34 ?
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Solution

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Solution Steps

Step 1: Calculate the Standard Error

The standard error (SE) of the sample proportion is calculated using the formula:

SE=p(1p)n SE = \sqrt{\frac{p(1 - p)}{n}}

where p=0.32 p = 0.32 (the population proportion) and n=368 n = 368 (the sample size). Substituting the values, we find:

SE=0.32×(10.32)3680.0243 SE = \sqrt{\frac{0.32 \times (1 - 0.32)}{368}} \approx 0.0243

Step 2: Determine the Z-scores

Next, we calculate the Z-scores for the sample proportion. The Z-score for the upper bound (0.34) is calculated as follows:

Zend=p^pSE=0.340.320.02430.8255 Z_{end} = \frac{\hat{p} - p}{SE} = \frac{0.34 - 0.32}{0.0243} \approx 0.8255

For the lower bound, since we are considering the probability of the sample proportion being less than 0.34, the Z-score is:

Zstart= Z_{start} = -\infty

Step 3: Calculate the Probability

Using the Z-scores, we can find the probability that the sample proportion is less than 0.34. The probability is given by:

P=Φ(Zend)Φ(Zstart)=Φ(0.8255)Φ() P = \Phi(Z_{end}) - \Phi(Z_{start}) = \Phi(0.8255) - \Phi(-\infty)

Since Φ()=0 \Phi(-\infty) = 0 and Φ(0.8255) \Phi(0.8255) approaches 1, we have:

P1.0 P \approx 1.0

Final Answer

The probability that the sample proportion of households spending more than \$125 a week is less than 0.34 is approximately

P=1.0 \boxed{P = 1.0}

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