Towards Standardizing Evaluation Test Sets for Compound Analysers
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The document proposes standardizing test sets for evaluating compound analyzers by establishing parameters for a standard test set. It discusses evaluating compound analyzers on different sized test sets containing compound words, non-compound words, and error words. Experiments compare analyzer performance on test sets of varying sizes, finding sizes below 250 words are too small and sizes above 1250 words show no significant differences in results. The proposed standard test set consists of 500 examples each of compounds, non-compounds, and errors for a total of 1500 words.
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1. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Materials used.
b. Factory overhead.
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d. Total cost of goods in process.
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2. Check your cost of goods manufactured with the instructor. If it is correct, proceed to part (3).
3. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Net sales.
b. Cost of goods sold.
c. Gross profit.
d. Total operating expenses.
e. Net income or loss before taxes.
CALCULATE T TEST
Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations.
Answer
The null hypothesis tested is
H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2)
The alternative hypothesis is
H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2)
The test statistic used is
12
12
2
~
NN
DM
MM
tt
S
+-
-
=
Where
22
1122
1212
(1)(1)
11
2
DM
NsNs
S
NNNN
éùéù
-+-
=+
êúêú
+-
ëûëû
Here M1 = 62,200, M2 = 63,700
s1 = 9330.95, s2 = 6912.95
N1 = 10, N2 = 10 (See the excel sheet)
Then,
(
)
(
)
22
(101)9330.95(101)6912.95
11
101021010
DM
S
éù
-+-
éù
=+
êú
êú
+-
ëû
êú
ëû
= 3672.267768
Therefore test statistic,
62,20063,700
3672.267768
t
-
=
= -0.408466946
Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18
Let the significance level be 0.05.
Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level.
The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level.
Upper critical value = 2.1
Lower critical value = -2.1
0
.
4
0
.
3
0
.
2
0
.
1
0
.
0
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.
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5
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D
i
s
t
r
i
b
u
t
i
o
n
P
l
o
t
T
,
d
f
=
1
8
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c. Gross profit.
d. Total operating expenses.
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Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations.
Answer
The null hypothesis tested is
H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2)
The alternative hypothesis is
H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2)
The test statistic used is
12
12
2
~
NN
DM
MM
tt
S
+-
-
=
Where
22
1122
1212
(1)(1)
11
2
DM
NsNs
S
NNNN
éùéù
-+-
=+
êúêú
+-
ëûëû
Here M1 = 62,200, M2 = 63,700
s1 = 9330.95, s2 = 6912.95
N1 = 10, N2 = 10 (See the excel sheet)
Then,
(
)
(
)
22
(101)9330.95(101)6912.95
11
101021010
DM
S
éù
-+-
éù
=+
êú
êú
+-
ëû
êú
ëû
= 3672.267768
Therefore test statistic,
62,20063,700
3672.267768
t
-
=
= -0.408466946
Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18
Let the significance level be 0.05.
Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level.
The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level.
Upper critical value = 2.1
Lower critical value = -2.1
0
.
4
0
.
3
0
.
2
0
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1
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.
0
X
D
e
n
s
i
t
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t
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i
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Qc trg 16 apr 2020
The document provides information about smoke testing vs sanity testing, including their definitions, key differences, and examples. Some key points:
- Smoke testing verifies critical functionality is working as expected after a new build, while sanity testing checks that new or fixed functionality works as intended after minor code changes.
- Smoke testing aims to check stability before detailed testing, while sanity testing aims to check rationality before detailed testing.
- Smoke testing can be done by developers or testers and is usually scripted, while sanity testing is done by testers and is typically not scripted.
- Smoke testing is a type of acceptance testing, and sanity testing is a type of regression testing.
The document also provides
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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- 19. ANOVA Variable p-value Compounds-NonCompounds-Errors 0.000 Instance-Word 0.000 Size 0.991
- 20. TukeyHSD Variable p-value Errors-Compounds 0.000 NonCompounds-Compounds 0.000 NonCompounds-Errors 0.000 Words-Instances 0.000 500-250 0.999 750-250 0.981 1000-250 0.997 1250-250 0.999 … … 4750-4250 1.000 5000-4250 1.000 4750-4500 1.000 5000-4500 1.000
- 22. ANOVA Variable p-value Compounds-NonCompounds-Errors 0.000 Instance-Word 0.000 Size 0.000
- 23. TukeyHSD Variable p-value Errors-Compounds 0.000 NonCompounds-Compounds 0.000 NonCompounds-Errors 0.000 Words-Instances 0.000 20-10 0.986 30-10 0.375 40-10 0.0000005 50-10 0.044 … … 250-30 0.322 50-40 0.783 60-40 1.000 … …
- 26. Thank you!