# Question Please check my answer The box plot below shows the results of a study comparing the mean Pulmonary function of different smoking groups. The study specifically wanted to determine if passive smoking was associated with lung damage. The study measured pulmonary function as the forced mid-expiratory flow (FEF) taken from a spirometer. The researchers in the study compared the groups using One-way ANOVA. 6.00- 130 104 207 297 5.00- H 4.00- Forced mid-expiratory flow (FEF 3.00 2.00- 100- 119 o 258 o 0.00 Nonsmokers Passive Smokers Non-inhaling smokers Light smokers Moderate smokers Heavy Smokers Why did the researchers use One-Way ANOVA? O One-way ANOVA was used because the researchers needed to compare the variance between the six smoking groups. O One-way ANOVA was used because the researchers needed to compare the medians between the six smoking groups. One-way ANOVA was used because the researchers needed to compare the means between the six smoking groups. O One-way ANOVA was used because the researchers needed to compare each smoking group to all other smoking groups separately. For the study comparing the mean pulmonary function between different smoking groups, which of the following is the correct Null hypothesis for the One-way ANOVA? O The sample mean pulmonary function is the same across all smoking groups. The population mean pulmonary function of the passive smoking group is different from the non-smoking group At least one of the groups has a population mean pulmonary function that is not equal to at least one of the other smoking groups. O The population mean pulmonary function is equal across all smoking groups. The following table shows the Normality tests for the pulmonary function across groups. Tests of Normality Group Kolmogorov-Smirnova Shapiro-Wilk Statistic df Statistic df Sig. Nonsmokers .048 50 Sig. 200 200" .992 50 Forced mid-expiratory flow (FEF) .978 Passive Smokers .079 50 .977 50 .420 Non-inhaling smokers .210 50 .000 .921 50 003 Light smokers .108 50 2009 .983 50 .687 Moderate smokers .110 50 .178 981 50 -577 Heaw Smokers .075 50 200" 984 50 .710 a. Lilliefors Significance Correction *. This is a lower bound of the true significance. In which group is the normality assumption violated? In which group is the normality assumption violated? O Passive smokers O Light smokers O Nonsmokers O Moderate smokers O Heavy smokers O Non-inhaling smokers The following table shows the results of the Levenes test of equal variance for the One-way ANOVA. Test of Homogeneity of Variances Maximum Finger-wrist Tapping Score Levene Statistic df1 df2 Sig. 3.118 2 98 .049 Can we assume equal variance between groups? Why? O Yes, as the p-value of the Levene's test > 0.05 Yes, as the p-value of the Levene's test < 0.05 O No, as the p-value of the Levene's test < 0.05 Test of Homogeneity of Variances Maximum Finger-wrist Tapping Score Levene Statistic df1 df2 Sig. 3.118 2 98 .049 Can we assume equal variance between groups? Why? O Yes, as the p-value of the Levene's test > 0.05 Yes, as the p-value of the Levene's test < 0.05 O No, as the p-value of the Levene's test < 0.05 O No, as the p-value of the Levene's test > 0.05 Assuming equal variance, use the following table of pairwise comparisons to answer the following question. 95% Confidence interval Std. Error Sig 064 Lower Bound Upper Bound - 29 14.95 3.129 3.840 004 3.41 22.12 3.129 064 - 14.95 29 Multiple Comparisons Dependent Variable Manimum Finger-wirts Topping Score Lead Concentration A) Lead Concentration Mean Difference - J) Bonferroni <40ught Om. 40-50g/100ml 7.330 60+ug/100mL. 12.705 40-50ug/100ml 40g/100mL -7.330 50 ug/100ml 5.435 50-ug/100ml <400g/100ml -12.765 40-50g/100mL -5.435 Dunnet T3 <400g/100mL 40-50ug/100ml 7330 50 ug/100ml 12785 40-50ug/100ml <40g/100ml -7.330 50 ug/100ml 5.435 30ug/100mL <4Dug/100mL. -12.765 40-500g/100ml -5,435 The mean difference is significant at the 0.05 level 3.644 14.31 417 004 -3.44 -22.12 3.840 -3.41 3644 417 -14.31 3.178 069 - 42 15.00 3.116 000 5.07 20.46 3.178 069 -15.08 .42 3.272 274 -2.61 -20.45 13.47 -5.07 3.116 000 3272 274 -13.47 2.81 Is there a statistically significant difference between <40 ug/100ml vs. 50+ug/100ml? Yes O No

PUR1KP The Asker · Probability and Statistics