Question

d. Interpret the results of your t-test from #3b… Can we reject
the null hypothesis? Is it safe to say that the mean Average Daily
Sales for Location Type A and for Location Type B are the same,
based on this data? **Please Interpret the data and if the
hypothesis can be rejected?**

t-Test: Two-Sample Assuming Equal Variances | ||||

Variable 1 |
Variable 2 |
|||

Mean | 472.0135 | 549.6954372 | ||

Variance | 133320.6 | 123688.0235 | ||

Observations | 2 | 2 | ||

Pooled Variance | 128504.3 | |||

Hypothesized Mean Difference | 0 | |||

df | 2 | |||

t Stat |
-0.2167 |
|||

P(T<=t) one-tail | 0.424268 | |||

t Critical one-tail | 2.919986 | |||

P(T<=t) two-tail | 0.848537 | |||

t Critical two-tail |
4.302653 |

Answer #1

Conduct an independent sample t-test (alpha= .05) in Excel to
determine whether there is a significant difference in the age of
offenders at prison admissions between White and Black offenders.
For this t-test, assume that the variances of the two groups are
equal. In your summary, please discuss the null hypothesis,
alternative hypothesis, the result of this hypothesis test, and
interpret the result.
t-Test: Two-Sample Assuming Equal Variances
White
Black
Mean
235.5714286
199.164557
Variance
44025.35714
87049.21616
Observations
21
79
Pooled...

t-Test: Two-Sample Assuming Equal Variances
Eagles Age
Patriots Age
Mean
27.76662516
28.15171678
Variance
12.09082453
12.65872713
Observations
77
77
Pooled
Variance
12.37477583
Hypothesized Mean Difference
0
df
152
t
Stat
-0.679243926
P(T<=t) one-tail
0.249008166
t
Critical one-tail
1.654940175
P(T<=t) two-tail
0.498016332
t
Critical two-tail
1.975693928
What can I concluded by this?

t-Test: Two-Sample Assuming Equal Variances
Eagles Weight
Patriots Weight
Mean
238.6623377
246.7012987
Variance
2206.647642
2141.501709
Observations
77
77
Pooled
Variance
2174.074675
Hypothesized Mean Difference
0
df
152
t
Stat
-1.069776516
P(T<=t) one-tail
0.143207882
t
Critical one-tail
1.654940175
P(T<=t) two-tail
0.286415763
t
Critical two-tail
1.975693928
Conclusion of this t-test? What can I concluded by this?

t-Test: Two-Sample Assuming Equal Variances
Eagles Height in inches
Patriots Height in inches
Mean
73.51948052
73.94805195
Variance
7.095010253
7.786739576
Observations
77
77
Pooled
Variance
7.440874915
Hypothesized Mean Difference
0
df
152
t
Stat
-0.974858488
P(T<=t) one-tail
0.165589676
t
Critical one-tail
1.654940175
P(T<=t) two-tail
0.331179353
t
Critical two-tail
1.975693928
What can I conclude by this?

Test the Hypotheses Below
Null
Hypothesis: Mean Student Debt in 2011 is equal to Mean Student Debt
in 2007
Alternative
Hypothesis: Mean Student Debt in 2011 is not equal to Mean Student
Debt in 2007
Alpha Level = 0.05
t-Test: Two-Sample Assuming Equal Variances
Variable 1
Variable 2
Mean
3925.76
2876.82
Variance
222129.8188
140278.3547
Observations
50
50
Pooled Variance
181204.0867
Hypothesized Mean Difference
0
df
98
t Stat
12.32073615
P(T<=t) one-tail
6.27467E-22
t Critical one-tail
1.660551217
P(T<=t) two-tail
1.25493E-21
t...

Test the Hypotheses Below
Null
Hypothesis: Mean Student Debt in 2011 is equal to Mean Student Debt
in 2007
Alternative
Hypothesis: Mean Student Debt in 2011 is not equal to Mean Student
Debt in 2007
Alpha Level = 0.05
Variable 1
Variable 2
Mean
3925.76
2876.82
Variance
222129.8188
140278.3547
Observations
50
50
Pooled Variance
181204.0867
Hypothesized Mean Difference
0
df
98
t Stat
12.32073615
P(T<=t) one-tail
6.27467E-22
t Critical one-tail
1.660551217
P(T<=t) two-tail
1.25493E-21
t Critical two-tail
1.984467455
Run the...

Please explain what the t-test results determine below: are the
new customers growing faster than the old? Are the new customers
spending less or more than the old customers?
New Customer
Old Customer
Mean
265.9284611
204.5386019
Variance
14221.64096
13546.62069
Observations
5569
4549
Pooled Variance
13918.16209
Hypothesized Mean Difference
0
df
10116
t Stat
26.0378387
P(T<=t) one-tail
5.0467E-145
t Critical one-tail
1.64500427
P(T<=t) two-tail
1.0093E-144
t Critical two-tail
1.960198519

Use the following to answer the next 2 questions. Do government
employees take longer coffee breaks than private sector workers?
That is a question that interested a management consultant. To
examine the issue, he took a random sample of ten government
employees and another random sample of ten private sector workers
and measured the amount of time (in minutes) they spent in coffee
breaks during the day (the samples are assumed to be
independent).
PS: Relevant outputs are given below;...

Please interpret the output below. What type of test was used
and why? Are energy costs lower for LEED-certified buildings in
this output? These data have an effect size of d = .39. How does
that add to your interpretation of the results?
Yearly Energy Costs LEED- Certified Buildings
(in thousands of $)
Yearly Energy Costs non-LEED- Certified Buildings
(in thousands of $)
Mean
18.22
21.46
Variance
1548.27
1318.33
Observations
60
55
Hypothesized Mean Difference
0
df
73
t Stat...

Use the following to answer the next question. Do government
employees take longer coffee breaks than private sector workers?
That is a question that interested a management consultant. To
examine the issue, he took a random sample of ten government
employees and another random sample of ten private sector workers
and measured the amount of time (in minutes) they spent in coffee
breaks during the day (the samples are assumed to be
independent).
PS: Relevant outputs are given below; please...

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