In this dataset, experimenters sampled seven professional soccer players to see if there was a significant difference in

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In This Dataset Experimenters Sampled Seven Professional Soccer Players To See If There Was A Significant Difference In 1 (58.3 KiB) Viewed 19 times

In This Dataset Experimenters Sampled Seven Professional Soccer Players To See If There Was A Significant Difference In 2 (72.2 KiB) Viewed 19 times

In this dataset, experimenters sampled seven professional soccer players to see if there was a significant difference in energy intake and energy expenditure (measured in Megajoules per day). Consider energy expenditure to be the first sample and energy intake to be the second sample. For now, assume that the sample of differences is representative of the population of differences for energy intake and energy expenditure; also assume the population is normally distributed. Conduct an appropriate hypothesis test with a level of significance .05 to investigate the question of whether energy expenditure is significantly more than energy intake for soccer players. Test the relevant hypotheses using a=.05. Round each answer as directed. (Hint: this data is paired, so your first step must be to create a column of differences). What is the appropriate pair of hypotheses? 0 Ho: Hd = 0 Ha: Ad < 0 O Hp: Ad > 0 Ha: Md = 0 O Ho: Ad < 0 Ha: Md = 0 O H0: Hd = 0 Ha: Md > 0 What is the value of the test statistic? Round this answer to 1 decimal place. t = Calculate the P-value. Round this answer to 3 decimal places. P-value = What conclusion can you draw? Reject null hypothesis. We do not have reason to believe that energy expenditure is significantly more than energy intake. O Fail to reject null hypothesis. We have reason to believe that energy expenditure is significantly more than energy intake. O Fail to reject null hypothesis. We do not have reason to believe that energy expenditure is significantly more than energy intake. Reject null hypothesis. We have reason to believe that energy expenditure is significantly more than energy intake.

Note that one of the assumptions above was that the population of differences in energy expenditure and intake was normally distributed. Check the normality of your column of differences. You will need the following csv file of normal scores for comparison: Normal Scores for a Sample of 7 Create a scatterplot where y is the column of differences (sorted smallest to largest) and x is the normal scores, and find the value of the correlation coefficient r. (Hint: does your data need to be sorted at all before you make your scatterplot?) What is the value of r? Round to 2 decimal places. = Is it reasonable to assume your differences come from a population of differences that is normally distributed? (You may refer to this table for guidance: Table 6.2). No, it is not reasonable to think that the population of differences in energy expenditure and energy intake for soccer players is normal. Yes, it is reasonable to think that the population of differences in energy expenditure and energy intake for soccer players is normal.