Use the data to compute a correlation coefficient, r, to determine the correlation between claims and surplus.

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The following data are the claims (in $ millions) for BlueCross BlueShield benefits
for nine states, along with the surplus (in $ millions) that the company had in assets
in those states.
State
Claims
Surplus
Alabama
$1,425
$277
Colorado
273
100
Florida
915
120
Illinois
1,687
259
Maine
234
40
Montana
142
25
North Dakota
259
57
Oklahoma
258
31
Техas
894
141
Use the data to compute a correlation coefficient, r, to determine the correlation
between claims and surplus.
Transcribed Image Text:The following data are the claims (in $ millions) for BlueCross BlueShield benefits for nine states, along with the surplus (in $ millions) that the company had in assets in those states. State Claims Surplus Alabama $1,425 $277 Colorado 273 100 Florida 915 120 Illinois 1,687 259 Maine 234 40 Montana 142 25 North Dakota 259 57 Oklahoma 258 31 Техas 894 141 Use the data to compute a correlation coefficient, r, to determine the correlation between claims and surplus.
12.68 Study the following Excel regression output for an
analysis attempting to predict the number of union
members in the United States by the size of the labor
force for selected years over a 30-year period from data
published by the U.S. Bureau of Labor Statistics.
Analyze the computer output. Discuss the strength of
the model in terms of proportion of variation
accounted for, slope, and overall predictability. Using
the equation of the regression line, attempt to predict
the number of union members when the labor force is
110,000. Note that the model was developed with data
already recoded in 1,000 units. Use the data in the
model as is.
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
0.798
0.636
0.612
258.632
Observations
17
ANOVA
Significance F
0.00012
df
SS
MS
F
Regression
1756035.529
1756036
26.25
Residual
15
1003354.471
66890.3
Total
16
2759390
Coefficients
Standard Error
t Stat
P-value
Intercept
20704.3805
879.6067
23.54
0.00000
Total Employment
-0.0390
0.0076
-5.12
0.00012
Transcribed Image Text:12.68 Study the following Excel regression output for an analysis attempting to predict the number of union members in the United States by the size of the labor force for selected years over a 30-year period from data published by the U.S. Bureau of Labor Statistics. Analyze the computer output. Discuss the strength of the model in terms of proportion of variation accounted for, slope, and overall predictability. Using the equation of the regression line, attempt to predict the number of union members when the labor force is 110,000. Note that the model was developed with data already recoded in 1,000 units. Use the data in the model as is. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.798 0.636 0.612 258.632 Observations 17 ANOVA Significance F 0.00012 df SS MS F Regression 1756035.529 1756036 26.25 Residual 15 1003354.471 66890.3 Total 16 2759390 Coefficients Standard Error t Stat P-value Intercept 20704.3805 879.6067 23.54 0.00000 Total Employment -0.0390 0.0076 -5.12 0.00012
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