The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 44 inches. Is the result close to the actual weight of 293 pounds? Use a significance level of 0.05. 52 | 321 221 265 335 307 265 Click the icon to view the critical values of the Pearson correlation coefficient r. | 50 41 45 45 45 C Chest size (inches) Weight (pounds) What is the regression equation? y3+x (Round to one decimal place needed.)
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
Given that the systolic blood pressure in the right arm is 90 mm Hg, the best systolic blood pressure in the left arm is how many mm Hg?
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