939 Words Feb 12th, 2016 4 Pages
1) Hypothesis:
To statistically evaluate questions, we need to put forward a hypothesis, which is an unproven testable proposition or supposition that tentatively explains certain facts or phenomena. It is a statement of assumptions we have about the nature of populations or relationships. So, Hypothesis is an educated guess based on the material that one has read.
Hypothesis testing involves the careful construction of two statements: the null hypothesis and the alternative hypothesis. To statistically evaluate the truthfulness of a question, one has to develop a null hypothesis (or statistical hypothesis), which will be used to test the proposition or supposition. The null hypothesis reflects that there will be no observed effect for
The P-value represents the probability of getting the observed results (or more extreme results) if the null hypothesis is true.
A small P-value indicates that the results in the sample would be unlikely to arise if the null hypothesis were true; this implies that the null hypothesis is not true, and there is evidence to reject it. If we reject the null hypothesis, we describe the result as statistically significant.
For Example, in my previous courses, when we did a study on thirty denture patients with periodontitis to determine the relationship between clinical probing of pocket depth and the connective tissue attachment. teeth were probed clinically by one investigator to determine attachment levels. These measurements were repeated by the second investigator using a dividing caliper and a millimeter scale with a Vernier. The null hypothesis that the difference between the clinical measurement and bench measurement is zero was satisfied.
2) Linear Regression Analysis:
Simple regression and Multiple regression can be used to examine the causality between dependent and independent variables, that is, whether a change in variable a results in a change to variable b. Knowing there is a causal relationship will be extremely useful, especially when we are making recommendations, as there is strong support for undertaking some action.
A simple linear regression examines how one variable (the independent variable) causes change in another (the

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