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COMM 2002 (65)
Lecture

# Semester 1 - Nov 25, 2014 - Statistical Significance.docx

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School
Department
Communication Studies
Course
COMM 2002
Professor
Heather Pyman
Semester
Fall

Description
November 25, 2013 Statistical Significance  Regression “best fit” line • Y= a + bx (smallest total distance of all data points in the distribution line) • B=slope (unit increases in y associated with 1 unit increase in x) • Direction (positive or negative) • R value (strength – how close are all the points in the distribution to the line) Pearson’s R • Called the “least squares” measure because it asks whether the squared distances from the regression line are less than the squared differences from the mean • R= explained variance (total variance – unexplained variance) total variance • (distance of values from the mean = total variance) • (distance of values from the regression line = unexplained variance) • r=strength of two interval level variables • R2- how much variation in “y” is explained by “x” Statistical significance • Allows us to estimate how close our sample statistics are to the actual population values • Uses what we know about the normal curve -68% of all cases fall btw. + - 1 SD -95% of all cases fall btw. +- 2 SD -99% of all cases fall btw. +- 3 SD …And what we know about a sampling distribution • Repeated drawing of samples would produce a normal curve of samples • This distribution would have a mean and a standard deviation o Grand Mean o Standard Error Sampling distribution example • Population of 4 ages = 15, 17, 18, 22 • Mean of the popula
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