A statistics instructor wonders whether significant differences exist in her students' final exam scores in her three different sections. She randomly selects the scores from 10 students in each section. A portion of the data is shown in the accompanying table. Assume exam scores are normally distributed. (You may find it useful to reference the F table.) Click here for the Excel Data File Section 1 Section 2 Section 3 50 56 82 89 70 76 66 88 65 a. Construct an ANOVA table. (Round intermediate calculations to at least 4 decimal places. Round "SS", "MS", "p-value" to 4 decimal places and "F" to 3 decimal places.) ANOVA Source of Variation df MS F p-value Between Groups Within Groups Total 0.0000 b. Do these data provide enough evidence at the 5% significance level to indicate that there are some differences in average exam scores among these three sections? O Yes , since the p-value is less than significance level. O Yes , since the p-value is not less than significance level. No , since the p-value is less than significance level. O No , since the p-value is not less than significance level.
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!
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