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Exercise 12-2. Have you considered why we can use sample statistics for any variable? How can we use inferential statistics with a ratio-level variable such as income, an ordinal variable measuring education, or a dichotomous variable measuring gender? The answer is that each variable is normally distributed in its sampling distribution. For this exercise you will explore the sampling distribution for a variable that is binomial: a coin ip. It is binomial because it has only two categories, heads and tails. While you cannot ever observe a sampling distribution because it is created with an innite number of sampleswhich we can of course never completeyou can begin to see how a sampling distribution might look. For this exercise you will need a coin. Below you will record the results of ten series of coin ips, with ten ips in each series. You will ip the coin ten times and record the total number of heads you observe in the space marked Series 1. You will then proceed to ip the coin ten more times and record your observations for Series 2, and so on. Once you have recorded all your observations, use a histogram (review ch_apter 11) to visually display the results from the ten series (the number of heads observed in each series) in the space to the right. How does tossing coins demonstrate the concept of the sampling distribution? Example: Seven heads (I ipped my coin ten times and counted seven heads, so I wrote seven in the blank.) 0 Series 1: heads 0 Series 2: heads 0 Series 3: heads 0 Series 4: heads 0 Series 5: heads 0 Series 6: heads 0 Series 7: heads 0 Series 8: heads 0 Series 9: heads \f