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Lecture

# Week 10 Lecture

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University of Toronto St. George

Political Science

POL242Y1

Anthony Sealey

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 relationshipthen 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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