Select Yes or No to state whether each data set is likely to be normally distributed.
Please help!
the number of coupons used at a supermarket. --> I believe YES.
the weights of the pumpkins that are delivered to a supermarket. --> I believe NO.
the number of raisins in each 8-oz box of raisins at a supermarket.---> I believe YES.
the amount of time customers spend waiting in the checkout line at a supermarket. --> I believe YES.
I agree.
The first one is NO and the second one is YES
To determine whether each dataset is likely to be normally distributed, we will consider some general guidelines.
1. The number of coupons used at a supermarket:
It is difficult to determine without further information whether the number of coupons used follows a normal distribution. However, it is possible for this dataset to be approximately normally distributed if we assume that the number of coupons used by customers follows a random pattern.
2. The weights of the pumpkins that are delivered to a supermarket:
It is unlikely that the weights of pumpkins would be normally distributed since there is likely to be a wide range of weights, with some pumpkins being much heavier or lighter than average. Therefore, the dataset is likely not normally distributed.
3. The number of raisins in each 8-oz box of raisins at a supermarket:
The number of raisins in each box can be considered a discrete variable. Assuming the packaging process is consistent, it is possible for this dataset to follow a normal distribution, with most boxes containing a similar number of raisins and outliers being less common.
4. The amount of time customers spend waiting in the checkout line at a supermarket:
It is plausible that the amount of time customers spend waiting in the checkout line could follow a normal distribution. Assuming a large number of customers, with various factors influencing wait times, the central limit theorem suggests that the distribution of wait times could be approximately normal.
In summary:
1. Number of coupons used: Unclear, but possibly normal.
2. Weights of pumpkins: Unlikely to be normal.
3. Number of raisins in each box: Possibly normal.
4. Waiting time in checkout line: Possibly normal.
To determine whether each data set is likely to be normally distributed, you can make use of a few methods, such as visual inspection, hypothesis testing, or calculating skewness and kurtosis.
1. The number of coupons used at a supermarket: YES.
To determine if this data set is normally distributed, you can plot a histogram or a probability plot of the coupon usage. If the distribution appears to be symmetric, bell-shaped, and centered around the mean, it is likely to be normally distributed.
2. The weights of the pumpkins delivered to a supermarket: NO.
To determine if this data set is normally distributed, you can again plot a histogram or a probability plot. However, if the distribution is skewed to one side, with a longer tail on either end, it is not likely to be normally distributed.
3. The number of raisins in each 8-oz box of raisins at a supermarket: YES.
Similar to the previous data set, plotting a histogram or a probability plot can help determine if this data is normally distributed. If it appears to be symmetric and bell-shaped around the mean, it is likely to be normally distributed.
4. The amount of time customers spend waiting in the checkout line at a supermarket: YES.
Once again, you can plot a histogram or a probability plot for this data set. If it appears to be symmetric and bell-shaped around the mean, it is likely to be normally distributed.
Keep in mind that these judgments are based on visual inspection and are not definitive. For a more precise analysis, you can use statistical tests like the Shapiro-Wilk test or calculate skewness and kurtosis to obtain more quantitative measures of normality.