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One-tail T Testing and Stating Hypotheses

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T test to evaluate one set of data points with another set of data points (Use t test testing to evaluate which set of data points (population) will have a larger mean difference). I need help with evaluating the information and stating the hypotheses and the final decision (to reject or fail to reject H0). Some of the information I have calculated and some of the computations I need help with and understanding.

The Hypotheses

The descriptive data points collected from the current squawk feedback database/spreadsheet will be used in the first phase of testing, designed to compare the results of the first six months of data points before the implementation of the modified squawk feedback process. The process was evaluated using a descriptive analysis.
The second set of data points will be used to test Hypothesis One. The data from the database/spreadsheet will compare the results of the six months of data after the implementation of the op card squawk feedback process this process was evaluated using a descriptive analysis.
The third analysis will compare the first set of data points with the second set of data points to determine whether there was a significant change in the descriptive analysis, the process was evaluated using a t-test. The level of significance for this one tailed test was chosen to be .05.
Hypothesis Two: analysis will compare the data points, squawks written at the point of origin collected from the current squawk feedback process and squawks written after op card squawk feedback process implementation to determine if the same amounts of squawks are being generated from the same SWBS. The success indicators will be the overall squawk count at various points in the process are reduced or eliminated. Additionally, the scrap, rework, repair cost per production hour ratio should reflect an overall reduction. A comparison of the descriptive statistics of the current squawk feedback process with the operation card squawk feedback process to determine the significant differences will be graphed on a bar graph to help with visualization of the results.
Descriptive data points collected from the current squawk feedback, first phase of testing:
T test (testing with one tail)
Stating the Hypotheses: (How do I state the hypotheses)
a)
H0:
H1:
Hypotheses 1 (data points)
Months Data
Jan. 69.3
Feb. 64.6
March 67.4
April 67.5
May 70.0
June 69.9

Hypotheses 2 (data points)
Months Data
July 69.9
Aug. 69.0
Sept. 68.6
Oct. 68.8
Nov. 68.0
Dec. 67.0

b) Collect data and compute statistics Hypotheses 1
N = 6, Mean = 68.12, St Dev. 2.07, Level of Significance = 0.05, df = 5

b) Collect data and compute statistics Hypotheses 2
N = 6, Mean = 68.55, St Dev. 0.98, Level of Significance = 0.05, df = 5

c) Locate the Critical Region; df (degree of freedom) for both Hypotheses 1 and 2
df = (n1-1) + (n2-1)
Find critical value for t
What is alpha
d) Computations for Hypotheses 1 ands 2
a) pooled variance
b) estimated standard error
c) independent measures t statistics
e) make a decision (reject or fail to reject H0)
f) Calculate Cohen's d
Magnitude of effect size is standardized by measuring the mean difference between two treatments in terms of the standard deviation

Evaluate using the following criteria
i) 0<d<0.2 _ _ > small effect, ii) 0.2,d<0.8 _ _ > medium effect,
iii) d>0.8 _ _ > large effect

g) The third analysis will compare the first set of data points with the second set of data points to determine whether there was a significant change in the descriptive analysis, the process was evaluated using a t-test. The level of significance for this one tailed test was chosen to be .05.

h) Calculate and display a normal distribution (bell curve) and bar graph for Hypotheses 1 and 2.

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Word and Excel documents answer a long list of questions on critical region, etc. in a hypothesis test.

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