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Open the file ‘ Birthweight_reduced ’

Exercise 1:. Open the file ‘ Birthweight_reduced ’ Recode mnocig ‘Number of cigarettes smoked per day’ into smoker/non-smoker (tip: use ‘ Transform  Recode into different variable’ and create a new variable ‘ Smoker ’ with codes 0: non-smoker; 1: smoker)

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Open the file ‘ Birthweight_reduced ’

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  1. Exercise 1: Open the file ‘Birthweight_reduced’ Recode mnocig ‘Number of cigarettes smoked per day’ into smoker/non-smoker (tip: use ‘Transform  Recode into different variable’ and create a new variable ‘Smoker’ with codes 0: non-smoker; 1: smoker) Conduct an independent samples t-test to examine whether birthweight differed between women who smoked and women who did not smoke. Don’t forget to look at the assumptions and see if they are met What do you conclude?

  2. Exercise 2: Open the file ‘Commute times paired’ This file contains data on my journey to and from work. These data are paired by day as each to/from combination represents a particular day Conduct a paired t-test to examine whether it takes me longer to cycle home than it does to cycle to work. Don’t forget to check the assumption that the paired differences are approximately normally distributed (you will need to calculate the differences to do this: (tip: use ‘Transform  Compute variable’ and create a new variable of the differences) What do you conclude?

  3. Exercise 3: Open the file ‘Titanic’ Conduct a Chi-squared test to examine whether there is a relationship between gender and survival. Remember to check the assumption that no more than 20% of the expected frequencies are < 5 and none are < 1 What do you conclude?

  4. Exercise 1:

  5. Exercise 1: Assumption 1: Data in the groups are approximately normally distributed Graphs  Legacy Dialogs  Histogram Add ‘Smoker’ to the Rows Box Don’t need to be perfect, just approximately symmetrical

  6. Exercise 1: Assumption 2: Variances are the same Two options: Look at the standard deviations in the Group Statistics table. These look similar enough (don’t expect them to be exactly the same) Look at Levene’s test in the main output table: should be not significant, i.e. p > 0.05, then can assume the variances do not differ from each other

  7. Exercise 1: Output

  8. Exercise 1: Reporting the results An independent samples t-test was carried out to examine whether the birthweight of baies differed between mothers who smoked and those who did not. There is evidence (p = 0.043) to suggest that the birthweight of babies whose mothers smoke is different from the birthweight of babies with non-smoking mothers What is the difference? On average women who smoke give birth to babies who are 0.38kg lighter than women who don’t smoke (95% CI: 0.01kg to 0.74kg)

  9. Exercise 2: Calculating the difference

  10. Exercise 2: Assumption 1: Differences are approximately normally distributed Graphs  Legacy Dialogs  Histogram Doesn’t need to be perfect, just approximately symmetrical

  11. Exercise 2: Output

  12. Exercise 2: Reporting the results A paired t-test was conducted to examine whether the time to travel to work differed from the time to travel home. There is strong evidence to suggest that the journey times to and from work differ (p = 0.009) What is the difference? On average it takes 3.2 minutes longer to cycle home than it takes to cylce to work (95% CI: 0.85 to 5.55 minutes) (Note: the journey to work is mainly downhill, the journey home is mainly uphill!)

  13. Exercise 3: Output

  14. Exercise 3: Reporting the results A chi-squared test was conducted to examine whether there was a relationship between gender and survival for passengers on board the Titanic. There is very strong evidence (p < 0.001) to suggest that gender and survival were linked What is the difference? 81% of male passengers died (682/843) compared to only 27% of female passengers (127/466)

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