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Lecture

March 19(4).docx

4 Pages
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Department
Sociology
Course Code
SOC 325
Professor
Elizabeth Quinlan

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Soc 325 March 19 2014 1 • If you can draw a line close to the dots you have a linear relationship • A curve would be a curvilinear relationship • Strength of the relationship depends on how many dots are close to the line • The greater the slant the greater the effect x has on y Our Example: A moderate, positive relationship Regression line: Formula • The formula defines the regression line o Y = a + bX  a = intercept or value of y when x=zero, exactly where it meets the vertical axis/crosses the y access.  Easy to find once we have the b  Y = a+bx --> y-bx = a  a = 3.33 - (0.69)(2.67)  a = 3.33 - 1.84  a = 1.49  We have a family with no children where the family is doing 1.49 hours of housework/week  b = slope of the regression line, the amount of change in y that is produced by an individual unit of change in the independent variable (x)  Formula ** ignore formula 13.3 ** ∑ (x−́x)( y−́y) b= (x−x) 2 ∑  What we can learn from b when it has a value Value of b Interpretation of b + 2.0 For every additional child, husbands do 2 hours more of housework For every additional child, husbands do 3/4 hour more + 0. 66 housework  For every additional child, husbands do 4 and a 1/2 .5 hours less housework  Measure of effect of the independent variable on the dependent variable  One of the ways we can check is to see what the value would be if we had different values as x  From table 13.3 (pg 377)  B = 0.69  Closer to point 0.66 than 2 hours  Once we have the b we can easily find the a  X = score of the independent variable  Y = score of dependent variable Soc 325 March 19 2014 2  The regression like Y = a +bX can be used tp predict a score of Y from score on X  Y = 1.49 - 0.69X  Y = 1.49 - O.69(6)  = 5.63  Do not need a data point of 6 children to predict how many hours a husband spends if there were 6 kids o So, for the relationship between number of children and husbands housework  B = 0.69  A = 1.49  Y = 1.49 - 0.69X o Once we specify a and b we will have a particular line Pearson's r correlation coefficient • A measure of association for two interval-ratio variables o Formula 13.5 2 ∑ (y−y) ¿ (x−x) ¿ ∑ ¿ √¿ ∑ (x−x)(y−y)́ r= ¿ o Adds precision into the strength of the relationship o top is the same as b b= ∑ (x−x́)(y−́y)  R takes in both in the denominator  Based on formula 13.5 2 ∑ (y−y) ¿ 2
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