Test the significance of the sample correlation and develop regression equation with scatter plot.
1. In the chi-square test, the null hypothesis (no difference between sets of observed and expected frequencies) is rejected when the
A) Computed chi-square is less than the critical value.
B) Difference between the observed and expected frequencies is significant.
C) Difference between the observed and expected frequencies is small.
D) Difference between the observed and expected frequencies occurs by chance.
E) None of the above.
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2.Sample Number
|
|
Assets ($ millions)
|
Return (%)
|
|
1
|
AARP
|
622.2
|
10.8
|
|
2
|
Babson
|
160.4
|
11.3
|
|
3
|
Compass
|
275.7
|
11.4
|
|
4
|
Galaxy
|
433.2
|
9.1
|
|
5
|
Keystone
|
437.9
|
9.2
|
|
6
|
MFS Bond A
|
494.5
|
11.6
|
|
7
|
Nichols
|
158.3
|
9.5
|
|
8
|
T Rowe
|
681
|
8.2
|
|
9
|
Thompson
|
241.3
|
6.8
|
The table above contains data from 9 mutual funds -- total assets held by the fund and last year\\\'s return on the investment of the assets (cash). Please:
(1) Construct a scatter diagram -- be sure you choose the correct dependent and independent variable. Think about these questions: Which variable influences the other? What is the direction of the relationship? Do assets influence the return or does the return influence the assets? What you choose as the "causing" variable will be X, what you choose as the variable being "caused" or influenced will be Y. Discuss your diagram.
(2) Compute the coefficients of correlation and determination and discuss what they mean.
(3) Test the significance of the sample correlation computed in #2.
(4) Develop a regression equation. What return would you predict for $400M in assets? Is the regression equation you developed meaningful? Why or why not?
Be sure to highlight every statistic needed to address the problem and discuss what each means. Be sure to show all 5 steps necessary to test the significance of the correlation.