What is the rejection region for the test (at a=0.05)?concludes that the data provide sufficient evidence to indicate that the mean user maintenance cost does vary among the three state installations. Would we be able to know which two state installations vary? Briefly explain. How can we find a 95% confidence interval for the difference between the mean cost in Texas and the mean cost in Kentucky? If Texas was chosen as base level and a similar model was fit to the data, what would be the value of the estimated intercept? ex 5.5 one qualitative variable for BIDMAINT DataThe REG ProcedureModel: MODEL1Dependent Variable: COSTNumber of Observations Read30Number of Observations Used30Analysis of VarianceSum ofMeanSourceDFSquares SquareF ValuePr>FModel198772993863.480.0452Error2777067128543Corrected Total29969443Root MSE168.94782 R-Square0.2050Dependent Mean372.43333Adj R-Sq0.1462Coeff Var45.36324Parameter EstimatesParameterStandardVariableDFEstimateErrort ValuePr> It|95% Confidence LimitsIntercept1279.6000053.425995.23<,0001169.97892389.22108X1180.3000075.555761.060.2973-74.72762235.32762X21198.2000075.555762.620.014143.17238353.22762

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Description: What is the rejection region for the test (at a=0.05)?concludes that the data provide sufficient evidence to indicate that the mean user maintenance cost does vary among the three state installations. Would we be able to know which two state installa

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MATLAB: An Introduction with Applications

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What is the rejection region for the test (at a=0.05)?
concludes that the data provide sufficient evidence to indicate that the mean user maintenance cost does vary among the three state installations. Would we be able to know which two state installations vary? Briefly explain.
How can we find a 95% confidence interval for the difference between the mean cost in Texas and the mean cost in Kentucky?
If Texas was chosen as base level and a similar model was fit to the data, what would be the value of the estimated intercept?

ex 5.5 one qualitative variable for BIDMAINT Data
The REG Procedure
Model: MODEL1
Dependent Variable: COST
Number of Observations Read
30
Number of Observations Used
30
Analysis of Variance
Sum of
Mean
Source
DF
Squares Square
F Value
Pr>F
Model
198772
99386
3.48
0.0452
Error
27
770671
28543
Corrected Total
29
969443
Root MSE
168.94782 R-Square
0.2050
Dependent Mean
372.43333
Adj R-Sq
0.1462
Coeff Var
45.36324
Parameter Estimates
Parameter
Standard
Variable
DF
Estimate
Error
t Value
Pr> It|
95% Confidence Limits
Intercept
1
279.60000
53.42599
5.23
<,0001
169.97892
389.22108
X1
1
80.30000
75.55576
1.06
0.2973
-74.72762
235.32762
X2
1
198.20000
75.55576
2.62
0.0141
43.17238
353.22762

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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