Class Notes (807,343)
POL242Y1 (17)
Lecture

Week 10 Lecture

4 Pages
126 Views

School
University of Toronto St. George
Department
Political Science
Course
POL242Y1
Professor
Anthony Sealey
Semester
Summer

Description
Week 10 Taking up the Quiz#4  We need a point estimate plus or minus our margin of error. 5.33 + or minus 0.25. (5.33 + 0.25)  Alternative hypothesis is related to region of rejection. Assignment #4-  Say/relate confidence to what you were analyzing  Explanation Section 2: Calculate confidence interval: you can say and relate it to what you were analyzing. E.g. Mean of the post communist data, therefore we can say with 95% of confidence that population parameter is the mean of the score of citizens of communist states is somewhere between…. And …. Testing Hypotheses: An alternative Approach Unstandardized Value  Probability value into standardized value called a T score. Stick it into Chi- function etc…Fifth step, you take your unstandardized test statistic into a standardized value.  X bar, subtract off the hypothesized value.  Universal standardized score (mean of standardized score): Mew= 0. o If we have a standardized score of 0.5, how much area is there?  If you get prob. Value and get it there, therefore, the form of the hypothesized test is: we do not reject null hypothesis.  B/c we know our alpha score is 0.5 We are looking for probability score of less than 0.5.  Left tailed test is -1.96. 2 is close to 0.5. If you calculate probability, the guess is 4.8 which is slightly behind 5%  Rather than using the tin, norminv and chiinv function in R to transform prob. Alues from level of significance into critical values, we transform from standardized to probabibility values. Example: Suppose that you want to test the hypothesis that the proportion of the public who favors some public policy is less than 50%. You draw a sample of 1200 canadians and find that 53.2% are in favor. Please test this hypothesis.  No need to find the critical numbers, b/c the alpha is already a prob. Score not standardized score AN introduction to Regression Analysis  Bi variate Linear Regression  Regression analysis s introduced to analyze relationships between linear variables.  Far more powerful tool used to analyze ANY type of variable.  Standard linear bivariate regression model (two variables)- in a relationshipthen presumably, there are 1 independent variable and 1 dependent variable o Independent affects dependent variable  Generally speaking, simple linear relationships among interval variables are rare.  Analyze data that is best modeled in a different way. o At eh macro level, how does political ideology of a palce affect public policy? o Measuring political ideologu on left right spectrum and public support as “decent standard of living”  Drawing a scatterplot  What type of relationship is this? o Negative relationship  Generally speaking, as we increase the alue of the independent variable, values of dependent decreases.  There are three relationships o Positive o Negative o No relationship  Generally for
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