Mat section 8X
Mat section 8
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Description
The unemployment data is important to the United States in making macro and microeconomic policies (Rufus, 2010). The average of the unemployment is 7.767, which is the general average of unemployment in all the states. However, at 95% critical value, the standard deviation is 1.88 while the variance from the mean is 3.554. This shows that the variances between the figures of mean are large enough. Cowan, (2012), the disparity is large due to the economic activities and endowment of various regions. On the other hand, the Skewedness is less than 4 and the Kurtosis is less than then 2, this means that the data do not violate the assumptions of the statistical analyses. The Skewedness and kurtosis is within the acceptable limits (Gary, Elder, Fast &, Hill, 2012; Kimberly, 2007).
Descriptive statistic
Anderson-Darling A-Squared 0.100
p 0.996
95% Critical Value 0.787
99% Critical Value 1.092
Mean 7.767
Mode 6.800
Standard Deviation 1.885
Variance 3.554
Skewedness -0.016
Kurtosis 0.102
N 52.000
Minimum 3.300
1st Quartile 6.600
Median 7.900
3rd Quartile 9.000
Maximum 12.600
Confidence Interval 0.525
for Mean (Mu) 7.242
0.95 8.292
For Stdev (sigma) 1.580
2.338
for Median 7.000
8.300
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Anova: Two Factor With Replication α 0.05 SUMMARY Data1 Total Nevada Count 2 2 Sum 23.7 23.7 Average 11.85 11.85 Variance 1.125 1.125 Rhode Island Count 2 2 Sum 21.2 21.2 Average 10.6 10.6 Variance 0.08 0.08 Mississippi Count 2 2 Sum 20.3 20.3 Average 10.15 10.15 Variance 0.125 0.125 North Carolina Count 2 2 Sum 19.7 19.7 Average 9.85 9.85 Variance 0.005 0.005 Georgia Count 2 2 Sum 19.2 19.2 Average 9.6 9.6 Variance 0.02 0.02 Michigan Count 2 2 Sum 18.4 18.4 Average 9.2 9.2 Variance 0.02 0.02 Indiana Count 2 2 Sum 18 18 Average 9 9 Variance 0 0 Oregon Count 2 2 Sum 17.6 17.6 Average 8.8 8.8 Variance 0.02 0.02 Arizona Count 2 2 Sum 17 17 Average 8.5 8.5 Variance 0.08 0.08 Washington Count 2 2 Sum 16.9 16.9 Average 8.45 8.45 Variance 0.005 0.005 Connecticut Count 2 2 Sum 16.3 16.3 Average 8.15 8.15 Variance 0.005 0.005 Ohio Count 2 2 Sum 16.1 16.1 Average 8.05 8.05 Variance 0.005 0.005 New York Count 2 2 Sum 15.9 15.9 Average 7.95 7.95 Variance 0.005 0.005 West Virginia Count 2 2 Sum 15.7 15.7 Average 7.85 7.85 Variance 0.005 0.005 Arkansas Count 2 2 Sum 15.3 15.3 Average 7.65 7.65 Variance 0.005 0.005 Delaware Count 2 2 Sum 14.7 14.7 Average 7.35 7.35 Variance 0.005 0.005 Wisconsin Count 2 2 Sum 14.1 14.1 Average 7.05 7.05 Variance 0.005 0.005 Louisiana Count 2 2 Sum 13.6 13.6 Average 6.8 6.8 Variance 0 0 Montana Count 2 2 Sum 13.5 13.5 Average 6.75 6.75 Variance 0.005 0.005 Hawaii Count 2 2 Sum 13.2 13.2 Average 6.6 6.6 Variance 0 0 Kansas Count 2 2 Sum 12.5 12.5 Average 6.25 6.25 Variance 0.005 0.005 Oklahoma Count 2 2 Sum 12.1 12.1 Average 6.05 6.05 Variance 0.005 0.005 Wyoming Count 2 2 Sum 11.5 11.5 Average 5.75 5.75 Variance 0.005 0.005 Iowa Count 2 2 Sum 10.7 10.7 Average 5.35 5.35 Variance 0.125 0.125 New Hampshire Count 2 2 Sum 9.3 9.3 Average 4.65 4.65 Variance 0.405 0.405 Nebraska Count 2 2 Sum 7.4 7.4 Average 3.7 3.7 Variance 0.32 0.32 Total Count 52 52 Sum 403.9 403.9 Average 7.767308 7.767308 Variance 3.554008 3.554008 k
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Exercise 1
Ho: Saint Leo University is the largest of the three catholic Universities in Florida
H1: Saint Leo University is not the largest of the three catholic universities in Florida
23 20
12 12
23 45
67 11
46 13
25 23
45 21
89 15
t-Test: Two-Sample Assuming Unequal Variances 0.05
Equal Sample Sizes Data1 Data2
Mean 41.25 20
Variance 680.7857 122
Observations 8 8
Hypothesized Mean Difference 0 df 9 t Stat 2.121 P(T<=t) one-tail 0.031 T Critical one-tail 1.833 P(T<=t) two-tail 0.063 T Critical Two-tail 2.262
Decision
Reject Null Hypothesis because p < 0.05 (Means are Different)
Exercise 2:
The sample data is as shown below: 62, 67, 71, 74, 76, 77,
formula
This is also expressed as
Course assignment Assignment Grade Percentage of Course Grade
A 62 10
B 67 10
C 71 30
Total Percent Listed 50 Course Average for Listed Assignments 68.4 References
Rufus K., (2010). “Unemployment rates – Unemployment rates by State”. CNN Money,
Kimberly H., (2007). “What is the difference between seasonally adjusted and non-seasonally adjusted data?” Nebraska Department of Labor.
Cowan G., (2012).Statistical Data Analysis. Oxford Science Publications. Oxford.
Gary M, J. Elder, A, Fast &T. Hill (2012), publisher Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications.NY Sage
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