Repeat Questions #6-9 using the MJ-5 sample. How does this interval compare to the one you found in Question #7? Now, use Minitab to calculate a 95% confidence interval estimate for Michael Jordan’s career per-game tov. Report your interval in your lab write-up. Use the sampling distribution you found in Question #6 to calculate a 95% percentile bootstrap confidence interval. Does the sampling distribution appear to be approximately normal (ie: is it symmetric and bell-shaped)? Is this variable skewed right or skewed left?įor the MJ-200 sample, use StatKey to estimate the sampling distribution (via bootstrapping) of Michael Jordan’s average per-game tov. Using the MJ-200 sample, use Minitab to construct a histogram of the variable “tov”. We’ll explore this perhaps surprising result by looking at the variable “tov”, or the turnovers committed by Michael Jordan in each game. Generally speaking, given the sample size is large enough, the sampling distribution of most statistics will be approximately normal, even if the estimate comes from a variable with a skewed distribution. Looking at the CLT normal approximation for a single mean, explain why the relationship you saw in Question #3 exists? (Hint: Think about the margin of error in the confidence interval formula) Include this graph in your lab write-up, along with 1-2 sentences describing how sample size appears to be related to confidence interval length. Which of the intervals above do you think is most likely to contain the population parameter of interest (Michael Jordan’s average points-per-game for his entire career)? Briefly explain.Ĭalculate the length of each confidence interval in your table, then use Minitab (or Excel/another program) to plot interval length versus sample size. Record these intervals in a table like the one below: Sample Size Intuitively, estimation of a population parameter should be easier when the sample size is larger, and in this section we’ll try and understand the impact of sample size on confidence intervals in greater detail.įor each sample, use Minitab to calculate a 95% confidence interval estimate of Michael Jordan’s career average points per game. The first factor we’ll explore is sample size. trb - total rebounds (offensive + defensive).result - game result: win (W) or loss (L) and score difference.age - Jordan’s age reported as years-days.MJ-200 sample link - A random sample of \(n = 200\) games.MJ-75 sample link - A random sample of \(n = 75\) games.MJ-25 sample link - A random sample of \(n = 25\) games.
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#How to do one mean confidence intervals on minitab 18 professional
For this part of the lab, we’ll use four different random samples of basketball games played by Michael Jordan, a professional basketball player who is widely considered to be the greatest of all time.