In a random sample of 400 watches, 3 are defective. Out of a total of 10,500 watches, how many would you expect to be defective based on this sample data?
To find how many defective watches we would expect out of a total of 10,500, we need to use proportion.
Proportion of defective watches in the sample = 3/400 = 0.0075
We can use this proportion to estimate the number of defective watches in the entire population:
Expected number of defective watches = Proportion of defective watches x Total number of watches
Expected number of defective watches = 0.0075 x 10,500
Expected number of defective watches = 79
Therefore, based on the sample data, we would expect approximately 79 out of 10,500 watches to be defective.
that isnt correct
I apologize for the mistake in my previous response. Here is the correct solution:
Proportion of defective watches in the sample = 3/400 = 0.0075
We can use this proportion to estimate the number of defective watches in the entire population:
Expected number of defective watches = Proportion of defective watches x Total number of watches
Expected number of defective watches = 0.0075 x 10,500
Expected number of defective watches = 78.75
Rounding to the nearest whole number, we can estimate that we would expect approximately 79 watches to be defective out of a total of 10,500 watches based on the sample data.
that is still not correct
I apologize for the errors in my previous responses. Here is the correct solution:
Proportion of defective watches in the sample = 3/400 = 0.0075
We can use this proportion to estimate the number of defective watches in the entire population:
Expected number of defective watches = Proportion of defective watches x Total number of watches
Expected number of defective watches = 0.0075 x 10,500
Expected number of defective watches = 78.75
Rounding the expected number of defective watches to the nearest integer, we get:
Expected number of defective watches = 79
Therefore, based on the sample data, we would expect approximately 79 out of 10,500 watches to be defective.
To calculate the expected number of defective watches based on the sample data, we can use the concept of proportions.
First, we need to determine the proportion of defective watches in the sample:
Proportion of defective watches in the sample = (Number of defective watches in the sample) / (Total number of watches in the sample)
= 3 / 400
= 0.0075
Now, we can use this proportion to estimate the number of defective watches in the total population:
Number of defective watches in the total population = (Proportion of defective watches in the sample) * (Total number of watches in the total population)
= 0.0075 * 10,500
= 78.75
Rounding to the nearest whole number, we can expect approximately 79 watches to be defective based on this sample data.
To determine the expected number of defective watches in the total population based on the sample data, we can use the concept of proportionality.
First, let's calculate the proportion of defective watches in the sample:
Proportion of defective watches in the sample = (Number of defective watches in the sample) / (Total number of watches in the sample)
Proportion of defective watches in the sample = 3 / 400
Next, we can use this proportion to estimate the expected number of defective watches in the total population:
Expected number of defective watches in the total population = (Proportion of defective watches in the sample) × (Total number of watches in the total population)
Expected number of defective watches in the total population = (3 / 400) × 10,500
Using this calculation, you can estimate the expected number of defective watches in the total population based on the given sample data.