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Week 11.docx

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Department
Political Science
Course
POL242Y1
Professor
Anthony Sealey
Semester
Summer

Description
Week 11: POL 242 Review-Testing Hypotheses Testing Hypotheses Alternative Approach  Take a point estimate and then taking the point estimate into a standardized value. Keep your probabilbility value and take a point estimate into a standized value (for alternative value)  Compare prob. Values rather than T scores. (it makes more sense)  1.96 makes more sense than 0.04. Example: Suppose that you want to test the hypothesis that the proportion of the public who favors some public policy is not equal to 50%. You draw a sample of 1200 Canadians and find that 53.2% are in favor. Please test this hypothesis using the alternative approach. Solution: Compare TS and CV. Our P value is less than our alpha value. We reject our null hypothesis. Our substantive conclusion… Bi-variate Regression  We use our line of best fit to estimate the type of relationship.  Generally, we use the line that minimized the sume of the square of the residuals  What is a residual? Minimize the distance from the points to the line of best fit. Regression and Strength  We try to estimate the size of slope coefficient. Essentially, the slop coefficient, how much change is made to DV when change is made to IV.  Our estimated slope is very small, T value is small and P value is really large. Regression and inference  Can we generalize any possible relationship that we can find between IV and DV?  Can we infer that there is a true relationship between the population?  When T scores are small, (we are comparing T score and to standized value of 1.96 and - 1.96) . The T score is 8 which is far beyond the standized value of 1.96.  Therefore we reject hypothesized value.  Compare P value to alpha.  All we have to do is TO ALWAYS COMPARE P VALUE TO OUR ALPHA VALUE. This is much easier to determine whether to reject or accept our null hypothesis. The number -8.202 is much bigger than -0.5. Regression and goodness of fiti (besides looking at strength of relationship, we often have to look at the overall goodness of fit)  Estimated line of best fit.  How accurate is our explanation of the dependent
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