sleep S17 jmp: Distribution of Sleep (hours) Distributions Sleep (hours) Summary
ID: 3202303 • Letter: S
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sleep S17 jmp: Distribution of Sleep (hours) Distributions Sleep (hours) Summary Statistics Fitted Normal v Quantiles 100.0% maximum Mean 7.1014493 Parameter Estimates 1.64 0.95 99.596 Std Dev 5044779 1.28- 0.91 Type Parameter Estimate Lower 95 Upper 95 Std Err Mean a 97.5% 10 0.18 79 Location u 7.1014493 6.7400342 7.4628643 0.84 9 Upper 95% Mean 7.4628643 90.096 E 75.0% quartile 8 Lower 9596 Mean 6.7400342 Dispersion a 1.5044779 1.2886271 1.8078684 0.67. 0.7 2log (Likelihood) 79055590915 2 50.09% median 69 v Goodness-of-Fit Test 25.0% quartile 6.25 0.0- 0.5 10.0% Shapiro-Wilk W Test 2.5% -0.67 0.3 W ProbExplanation / Answer
Whether you will accept or reject the null hypothesis that will depend on the alpha chosen. In general cases, most of the people choose alpha to be 0.0. If alpha is 0.05 then p-value is not less than alpha.Then we can not reject the null hypothesis.This means that one can expect that the sample is from a parent normal population. But if it is not the case then one may use normality transformation to make the data normal. One of such transformation is box-cox transformation.Another important aspect with this data is that as the boxplot suggests that there is outlier in the data. So, one must carry forward the analysis with and without the outlier in order to understand that whether the outlier affects the result of statistical test or not.
If example in such case the box-cox transformation is used is used is regression analysis because violation of normality assumption affect the result of linear regression whereas logistic regression is not affected by the assumption of normality.
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