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Assignment on Epidemiology and Biostatistics

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Added on  2020-04-07

Assignment on Epidemiology and Biostatistics

   Added on 2020-04-07

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Epidemiology and BiostatisticsNameStudent’s NumberProfessor’s Name6th October 2017
Assignment on Epidemiology and Biostatistics_1
Question One:Q1:Q2: in this part, we sought to test the hypothesis that thepopulation mean weight is the same regardless exercise levels,i.e., we compared the population mean weightbetween the twoexercise levels.i)Hypotheses: (1 mark)HO: HA: ii)Name the t test youused for the hypothesis(0.5 marks): iii)P value obtained from the t test you performed (0.5marks): iv)Conclusion of the t test: (2 marks)Table 1: DescriptivestatisticsExerciseGenderWeightMeanLowFemale65.048Male67.640HighFemale62.567Male60.747As can be seen in table 1,the mean weight forfemales at low exercise islower (M = 65.048, SD =3.839) as compared to thatof the male at the samelevel of exercise, low (M= 67.567, SD = 3.909).However, at high exerciselevels, the mean weightfor females (M = 62.567,SD = 4.673) is higher thanthat of the males (M =60.747, SD = 4.270).The mean weight is thesame for low and highexerciseThe mean weight isdifferent for low and highexerciseIndependent samples t-test0.000Since the p-value is less than 5% level of significance, we reject the nullhypothesis and conclude that the mean weight is significantly different forlow and high exercise levels.
Assignment on Epidemiology and Biostatistics_2
Q3: Now we assess the difference in the population mean weightbetween two exerciselevels using a multiple regression model, accounting for gender in the analyses as a potential effect modifier.i.Name the multiple regression model which is appropriate for this question. Why?ii.The mean plot for this question is given below:6062646668(mean)weightLowHighexerciseFemaleMale6062646668(mean)weightMaleFemalegenderLowHighBased on the mean plots given, make a justification on whether the interaction between exerciseand gender should be included and assessed in your model. (1 mark)iii.Fitthe model you recommended for weighton exerciseand gender. (2 marks)Attach relevant Stata output (eg., ANOVA table) hereThe most appropriate regression model is the multiple linear regression; this isbecause the dependent variable is a continuous variable that can easily beestimated using the mentioned model.Yes the interaction between exercise and gender should be included since from the mean plots we can see the lines are non-parallel suggesting that there is interaction between gender and exercise and as such it would be prudent to include the interaction of the two variables
Assignment on Epidemiology and Biostatistics_3
ANOVAaModelSum of SquaresdfMean SquareFSig.1Regression295.015398.3384.896.004bResidual1285.3486420.084Total1580.36367a. Dependent Variable: weightb. Predictors: (Constant), exercise_gender, gender, exerciseiv.Based on the ANOVA table in Question iii, test the hypothesis that there is no interaction in the population between the exerciseand gender, including your interpretations and conclusions (1 mark).v.Comment on ‘whether a further model, which removes the insignificant variable, is necessary’ by selecting an answer below: a)Yes, the insignificant variable (gender) should be removed from the model and hence I can have a further simpler model. Briefly justify your answer.Attach Stata output (eg., parameter estimation table) hereCoefficientsaModelUnstandardized CoefficientsStandardizedCoefficientstSig.BStd. ErrorBeta1(Constant)61.2141.08756.319.000Exercise 5.8341.537.6053.795.000Gender 2.2911.537.2381.490.141Exercise*gender-5.1032.174-.458-2.347.022a. Dependent Variable: weightBased on the output in iii above, it is evident that there is evidence of interaction in the population between exerciseand gender (p-value = 0.022; a value less than α = 0.05). The p-value for thevariable gender (which issimply the dummyvariable male after thecoding i.e. male=1 andfemale = 0) is 0.141; avalue greater than 5%level of significance.Hence the null hypothesisof beta equal to zero isnot rejected implying thatthe variable is notdifferent from zero (i.e. itis insignificant in themodel).
Assignment on Epidemiology and Biostatistics_4

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