1. Mohsen Askarishahi Reference: 1)Aviva Petrie. Medical Statistics at a Glance. Blackwell (2005) 2) Sheldon M. Ross. Introductory Statistics. Elsevier Inc. (2010) 3)Wayne W. Daniel.Biostatistics,A Foundation for Analysis in the Health. John Wiley & Sons.Inc.(1995)

5. Numerical data

11. 1000100 Outcome 700 300 no cancer 30 Not Exposed (non smoke) 70 Exposed (smoke) cancer Exposure OR = ad/bc = 5.44 RR = I e /I n = 4.41 Comparing Odds Ratio and Relative Risk

12. Stating your results OR = 5.44 Those with the disease are 5.44 times as likely to have had the exposure compared to those without the disease RR = 4.41 Those with the exposure are 4.41 times as likely to develop the disease compared to those without the exposure

13. We have a sample from a single group of individuals and one numerical or ordinal variable of interest. We are interested in whether the average of this variable takes a particular value. For example, we may have a sample of patients with a specific medical condition. We have been monitoring triglyceride levels in the blood of healthy individuals and know that they have a mean of 1.74mmol/L. We wish to know whether the average level in our patients is the same as this value.

14. The one-sample t-test 1- Define the null and alternative hypotheses under study 2- Collect relevant data from a sample of individuals 3- Calculate the value of the test statistic specific to H0 4- Compare the value of the test statistic to values from a known probability distribution 5 -Interpret the P-value and results

15. Interpret the P-value and results

17. Numerical data: two related groups We have two samples that are related to each other and one numerical or ordinal variable of interest. The variable may be measured on each individual in two circumstances. For example, each patient has two measurements on the variable, one while taking active treatment and one while taking placebo. The individuals in each sample may be different, but are linked to each other in some way. For example, patients in one group may be individually matched to patients in the other group in a case–control Study.

18. Numerical data: two related groups The paired t-test

21. We have samples from two independent (unrelated) groups of individuals and one numerical or ordinal variable of interest. We are interested in whether the mean or distribution of the variable is the same in the two groups. For example, we may wish to compare the weights in two groups of children, each child being randomly allocated to receive either a dietary supplement or placebo.

24. We have samples from a number of independent groups. We have a single numerical or ordinal variable and are interested in whether the average value of the variable varies in the different groups. For example, whether the average platelet count varies in groups of women with different ethnic backgrounds.

28. خسته نباشید.