The Chi-squared test evaluates if two variables are related. The other statistical tests mentioned do not perform this function.

Crossover trials are characterized by the switching of study participants throughout the treatment groups, keeping a random order of switching. This randomization is important to determine any carry-over effect of different treatment modalities. The benefit of this study is the evaluation of the efficacy of various short-term treatment options for the relief of chronic conditions. The washout period is small in this type of study. Crossover studies are more efficient than the parallel studies, but they should be used according to the treatment options and outcomes.

Power calculations are a important step in study design and preparation, but do not directly help with the interpretation of the results. They are used to determine how many subjects are needed in order to avoid errors in accepting or rejecting the null hypothesis. These calculations are not usually complex and for the best results, the power of the test should be set to above 80%.

The main aim of randomisation in a clinical trial is to remove the bias and avoid any potential confounding variables. While in double blind studies both the investigators and the patients are not aware of which group they belong in, being blind is not essential in carrying out a randomized study, nor is it essential that the randomisation be done away from the study centre. A placebo also does not facilitate randomisation, which can be done in single centre and multi-centre trials.

A cohort study is a particular form of longitudinal study that samples a cohort (a group of people who share a defining characteristic, typically those who experienced a common event in a selected period, such as birth or graduation), performing a cross-section at intervals through time.
The relative risk (RR) or risk ratio is the ratio of the probability of an outcome in an exposed group to the probability of an outcome in an unexposed group. Relative risk is used in the statistical analysis of the data of experimental, cohort and cross-sectional studies, to estimate the strength of the association between treatments or risk factors, and outcomes.

Case control studies are used to determine the relationship between exposure to a risk factor and the resultant outcomes. It finds patients in the general population who have a certain condition and retrospectively searches for past exposure to possible risk factors for the disease. Controls are people who do not have the disease found in the general population. This type of study is useful for rare diseases, and is less time consuming to conduct. While cohort studies can be used to study rare diseases they measure the relative risk of developing the disease over time based on exposure. Ecological or cross-sectional studies attempt take a snapshot of a whole population, and thus are inappropriate for rare diseases as larger sample numbers are needed. A randomised control trial is better suited to determine the effect of an intervention.

Relative risk = (Incidence in exposed group)/incidence in unexposed group). So in this case RR = (50/100)/(50/200) = 2.

An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional and cohort study designs as well (with some modifications and/or assumptions). Wherea = Number of exposed casesb = Number of exposed non-casesc = Number of unexposed casesd = Number of unexposed non-casesOR=(a/c) / (b/d) = ad/bc

The equation, lung function = 3.2 + (1.7 x height) is linear, but this does not necessarily mean that the relationship itself is linear. The equation is able to predict that taller individuals are more likely to have greater lung functions. No information is given on whether the coefficients and the R value are statistically significant.

If the 95% confidence interval contains zero (more precisely, the parameter value specified in the null hypothesis), then the effect will not be significant at the 0.05 level.If the 95% CI for the DIFFERENCE between the 2 groups contains the value 0, this means that the p-value will be greater than 0.05. Conversely, if the 95% CI does not contain the value 0, then the p-value will be strictly less than 0.05. The same applies when comparing groups using a ratio, such as an odds ratio or risk ratio. When using a RATIO instead of a DIFFERENCE, the situation of no difference between the 2 groups will be indicated by a value of 1 instead of 0. If the ratio equals to 1, the 2 groups are equal. Hence, if the 95% CI of the ratio contains the value 1, the p-value will be greater than 0.05. Alternatively, if the 95% CI does not contain the value 1, the p-value is strictly less than 0.05.