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Reading Working Papers in Linguistics 4 (2000) - The University of ...

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J. MARSHALL<br />

LIFMODLEXREC<br />

60<br />

50<br />

40<br />

LEXREC<br />

30<br />

LEXREC<br />

20<br />

10<br />

0<br />

0 5 10 15 20 25 30 35 40 45<br />

LIFMOD<br />

Next, the correlations for SOCNET were considered:<br />

phovar ssscor lexrec lifmod age soclas attdia natpri<br />

socne 0.056 0.040 0.197 0.186 0.186 -0.140 -0.116 -0.132<br />

SOCNET is not correlated with any other variable. It appears at this stage that<br />

the social network scores are not significant predictors <strong>of</strong> dialect use. This will<br />

be tested more rigorously once the regression model has been built. Next we<br />

look at a scatterplot show<strong>in</strong>g the correlation between SOCNET and<br />

PHOVAR:<br />

SOCNETPHOVAR<br />

60<br />

50<br />

40<br />

PHOVAR<br />

30<br />

PHOVAR<br />

20<br />

10<br />

0<br />

0 1 2 3 4 5 6 7 8 9<br />

SOCNET<br />

This scatterplot reveals that the two variables do not co-vary. It is what the<br />

statistician calls a ‘starry night’. <strong>The</strong> plots for the other two l<strong>in</strong>guistic<br />

variables are similar:<br />

162

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