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Chapter 15

summary for chapter 15

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Department
Sociology
Course Code
SOC202H1
Professor
Brent Berry

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Chapter 15
Persons r correlation coefficient is the statistic we use to test the hypothesis of the
existence of a relationship/w 2 interval ratio variables, an independent variable X
and a dependent of Y
When to test a hypothesise using bivariate correlation and regression analysis (t-
distribution, df n-2)
1)In general testing a hypothesis that a relationship exists between 2 interval ratio
variables
2)There are two interval-ration variables
3)There are no restriction on sample size, but generally the larger the n, the better
4)A scatter plot of the coordinates of the two variables fits a linear pattern
When using a sample data we must keep in mind to try and answer the following
question: does a linear relationship between X and Y truly exist in the population, or
is the linear pattern in this sample the result of sampling error
As with any hypothesis test, the real interest lies in the parameter, the summary
measurements that applies to the entire population
Persons r correlation coefficient allows us to test a hypothesise to answer this
question
For the population the corresponding parameter is symbolized by the Greek letter
rho (p)- it is the correlation coefficient that would be obtained if Persons correlation
coefficient were computed for the entire population
The effect of the test is the difference between an observed sample statistic and the
expected parameter when the null hypothesis is true. For a correlation hypothesise
the effect is the difference between the observed sample Persons r and the expected
rho of 0.
The null hypothesis => Ho;p=0 (no relationship between X and Y)
The statement of the alternative hypothesis can be two tailed, non directional, one
tailed positive or one tailed negative
The standard error is inversely related to sample size, the larger the sample size the
smaller the standard error. The test statistic is (look in book p.557)
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Description
Chapter 15 Persons r correlation coefficient is the statistic we use to test the hypothesis of the existence of a relationshipw 2 interval ratio variables, an independent variable X and a dependent of Y When to test a hypothesise using bivariate correlation and regression analysis (t- distribution, df n-2) 1) In general testing a hypothesis that a relationship exists between 2 interval ratio variables 2) There are two interval-ration variables 3) There are no restriction on sample size, but generally the larger the n, the better 4) A scatter plot of the coordinates of the two variables fits a linear pattern When using a sample data we must keep in mind to try and answer the following question: does a linear relationship between X and Y truly exist in the population, or is the linear pattern in this sample the result of sampling error As with any hypothesis test, the real interest lies in the parameter, the summary measurements that applies to the entire popula
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