Now let's look at the data from all the Buffalo. If we calculate the 60% confidence interval for every buffalo's count (i.e. data sample), how many Cls do we expect to contain the true mean? Store your answer in theoretical_hits. Then we need to check our theoretical answer with our actual data. Calculate the 60% confidence interval for each buffalo's count. Then commpute how many of those Cls contain the true mean. Assume that the true mean is 25,000. Store this value in sample_hits . Does this value match the theoretical value? It may be helpful to visualize the Confidence Intervals, along with the true mean. Create a plot for the Confidence intervals for the first 40 rows of the dataframe. Also plot true_mean=25000 as a horizontal dotted line. This plot will not be graded, but may help you get a better understanding of different confidence intervals for the same underlying population. We recommend looking into the ggplot package to help with this task.

We have to calculate let's now examine the Buffalo's collective data. How many Cls should we expect to include the true mean if we compute the 60% confidence interval for each buffalo count (i.e., data sample).

We know that,

Theoretical hits= confidence interval × total no. of buffaloes

= 0.6×(buffaloes)

Sample hits = count among total no. of buffaloes, from which how many of inside 25000, that will be sample hits.

So,

=0.6×25000

=15000

Therefore, 15000 Cls should we expect to include the true mean if we compute the 60% confidence interval for each buffalo count (i.e., data sample).

To learn more about interval visit: https://brainly.com/question/2274635

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