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Chapter 10

SOCC31H3 Chapter Notes - Chapter 10: Scatter Plot, Null Hypothesis

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Shirin Montazer

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SOCB06 chapter 10:
Correlation: age, intelligence and education attainment vary from one person to another and therefore
referred to as variables.
Many relationships are statistically significant- stronger than you would expect to obtain just as
a result of sampling error alone.
Correlations vary with respect to their strength, we visualize differences in strengths of
correlations by means of a scatter plot or scatter diagram, a graph that shows the way of scores
on any two variables, X and Y, are scatter throughout the range of possible score values.
Scatter plot: set up as the X is arranged horizontally, Y is measured across the vertical line.
- Directions of Correlation:
It can be either positive or negative in terms of direction.
Positive correlation: respondents getting high scores on the X variables also tend to get high
scores on the Y variable.
Negative correlation: respondents have high scores on the X variable and low scores on the Y
variable. Such an example is education and prejudice.
- Curvilinear Correlation:
One variable can increase while the other increase, until the other reverses itself so that one
variable can increase while the other decrease.
Correlation Coefficient: expresses both strengths and weakness and direction of straight-line
correlation. You have -1.00 and +1.00.
-1.00, -.60,-.30 and -.10 signify a negative relationship and +1.00, +.60, +.30 and +.10 indicate
positive correlation.
- Pearsons Correlation Coefficient: we can determine the strengths of X and Y variables, at the interval
level. Pearsons r gives us a measure of the strength and direction of the correlation in the sample being
studied. If we take a random sample from a specified population, we may still seek to determine
whether the obtained association between X and Y exist in the population and is not merely due to
sampling error.
- To test a measure of correlation, we must set up a null hypothesis that no relationship exist in the
population. The null hypothesis states that the population correlation p(rho) is zero .. That is p=0.
- Requirements for Use of Pearsons r Correlation Coefficent: finding out an association between X and Y
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