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BIO 500 W6 Discussion Question One
BIO 500 W6 Discussion Question One
As a health care professional in public health, compare when you would use hypothesis testing and when you would use regression.
At this point, you’ll already have a hypothesis ready to go. Now, it’s time to test your theory. Remember, a hypothesis is a statement regarding what you believe might happen. These are the steps you’ll want to take to see if your suppositions stand up:
- State your null hypothesis. The null hypothesis is a commonly accepted fact. It’s the default, or what we’d believe if the experiment was never conducted. It’s the least exciting result, showing no significant difference between two or more groups. Researchers work to nullify or disprove null hypotheses.
- State an alternative hypothesis. You’ll want to prove an alternative hypothesis. This is the opposite of the null hypothesis, demonstrating or supporting a statistically significant result. By rejecting the null hypothesis, you accept the alternative hypothesis.
- Determine a significance level. This is the determiner, also known as the alpha (α). It defines the probability that the null hypothesis will be rejected. A typical significance level is set at 0.05 (or 5%). You may also see 0.1 or 0.01, depending on the area of study.
If you set the alpha at 0.05, then there is a 5% chance you’ll find support for the alternative hypothesis (thus rejecting the null hypothesis) when, in truth, the null hypothesis is actually true and you were wrong to reject it.
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In other words, the significance level is a statistical way of demonstrating how confident you are in your conclusion. If you set a high alpha (0.25), then you’ll have a better shot at supporting your alternative hypothesis, since you don’t need to find as big a difference between your test groups. However, you’ll also have a bigger chance at being wrong about your conclusion.
- Calculate the p-value. The p-value, or calculated probability, indicates the probability of achieving the results of the null hypothesis. While the alpha is the significance level you’re trying to achieve, the p-level is what your actual data is showing when you calculate it. A low p-value offers stronger support for your alternative hypothesis.
- Draw a conclusion. If your p-value meets your significance level requirements, then your alternative hypothesis may be valid and you may reject the null hypothesis. In other words, if your p-value is less than your significance level (e.g., if your calculated p-value is 0.02 and your significance level is 0.05), then you can reject the null hypothesis and accept your alternative hypothesis.
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.
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