1. Copy and paste the first 15 rows from your StatCrunch data file below. You can do that by highlighting the data in StatCrunch, then clicking on Edit>Copy. Next return to this file, place your cursor in the space below, and click Paste. The purpose of this is to allow your instructor to see the first part of your data set. Use all 200 students in your sample to answer the remaining questions.
Gender
|
Class
|
Hours
|
Work
|
Loans
|
CC Debt
|
Female
|
2
|
11
|
26.5
|
7492
|
3143
|
Female
|
1
|
14
|
0
|
0
|
1033
|
Male
|
4
|
14
|
18
|
16983
|
5086
|
Female
|
2
|
15
|
15.5
|
7361
|
0
|
Female
|
4
|
15
|
0
|
17234
|
3933
|
Male
|
1
|
15
|
0
|
0
|
1710
|
Male
|
2
|
16
|
0
|
9052
|
4386
|
Male
|
2
|
6
|
31.5
|
7555
|
0
|
XXXX
|
X
|
XX
|
0
|
7834
|
3264
|
XXXX
|
X
|
XX
|
X
|
12924
|
7049
|
Male
|
3
|
18
|
XX
|
0
|
3498
|
XXXXXX
|
X
|
16
|
X
|
0
|
XXXX
|
Female
|
4
|
XX
|
14.X
|
17642
|
4337
|
XXXXXX
|
2
|
15
|
X
|
X
|
2600
|
XXXXXX
|
3
|
14
|
0
|
X
|
XXXX
|
X. What is the XXXXX of XXX XXXXXXXXXXXX XX XXXXXX XXXXX? XXXXXXX summary statistics and XXX XXXXXXXXXX to construct a histogram of XXX XXXXXX hour XXXX. XXXXXXX a short paragraph XXXXXXXXX XXX XXXXXXXX.
XXXXXX
|
n
|
XXXX
|
Variance
|
Std. dev.
|
XXX. XXX.
|
XXXXXX
|
Range
|
XXX
|
XXX
|
XX
|
Q3
|
Hours
|
XXX
|
14.135
|
XX.916357
|
X.XXXXXXX
|
0.26378359
|
15
|
18
|
3
|
XX
|
XX
|
16
|
Images Not Shown
XXX XXXXXXXXXXXX XX students’ XXXXXX XXXXX is left-XXXXXX, since XXX mean XX less XXXX XXX XXXXXX. From XXX histogram XX XXX see that XXXX students XXX XXXXXX between 15 and 17.5 XXXXXX hours. XXX XXXX number XX credit hours taken XX XX.135 with a standard XXXXXXXXX XX approximately 3.73. The most number XX XXXXXX XXXXX a XXXXXXX XXXX was XX hours and the least XXX X hours.
3. Suppose you XXXX to construct a 95% XXXXXXXXXX interval for XXX mean XXXXXX hours XXXXX by StatCrunch U XXXXXXXX. XXXX sample XXXX XXXXX XX XXXXXX to limit XXX XXXXXX XX XXXXX to X.5 XXXXXX XXXXX? Use the sample XXXXXXXX deviation from your XXXXXX as an estimate XX the population standard deviation. You XXXX XXXX XX follow the example XX page XXX XX the text.
n=(X.96(3.XX)/0.X)^2=213.X
Estimating the XXXXXXXXXX XXXXXXXX XXXXXXXXX with the XXXXXXXX XXXXXXXXX XXXX our XXXXXX, XXX minimum sample size XXXXXXXX to XXXXXXXXX a XX% confidence interval with a XXXXXX XX error being X.5 is XXX individuals.
4. (a) What is the proportion of XXXXXXX XX StatCrunch U? Create a XXX chart showing the proportion XX XXXXXX XXXXXXXX XX StatCrunch U. Be XXXX to XXXXXXX a XXXXXX of sentences answering XXX XXXXXXXX
Images Not Shown
The proportion XX XXXXXXX XX Statcrunch X is X.575.
(b) Does the XXXXXXXXXX of females XXXXXX across classes? Create a XXXXXXX bar XXXXX and a contingency table to show how the proportion XXXXXXX XXXXXX XXXXXXX. Make XXXX XXXX XXX XXXXX XXXXX XXXXXXXXXXX or percentages, XXX XXXXXX. Be XXXX to XXXXXXX a couple of sentences XXXXXXXXX XXX question.
Images Not Shown
Cell format
|
(XXXXXXX XX total)
|
|
XXXXXX
|
Male
|
XXXXX
|
1
|
(XX%)
|
(13%)
|
(XX%)
|
X
|
(XX%)
|
(9.5%)
|
(XX.5%)
|
3
|
(XX%)
|
(11.X%)
|
(XX.X%)
|
4
|
(XX.X%)
|
(X.X%)
|
(XX%)
|
Total
|
(57.5%)
|
(42.5%)
|
(XXX%)
|
XXX-XXXXXX XXXX:
XXXXXXXXX
|
XX
|
Value
|
X-XXXXX
|
Chi-XXXXXX
|
3
|
X.989287
|
0.XXXX
|
XXXX XXX large p-XXXXX=0.3933 XX can XXXXXXXX XXXX XXXXXX and XXXXX XXX independent, and XXXXXXXXX the XXXXXXXX XXXXX’t support a change across XXXXXXX.
5. XXXX XXX XXXXXX XX XXXXXX hours taken XXXX XXXXXXXXX XX XXXXXXX or XXX XXXXXXXX work? XXXXXX XXX boxplots on XXX XXXX XXX of axes XXXXXXX XXX XXXXXX XX credit XXXXX XXXXX XX XXXXXXXX XXX XXXX and by students who do not XXXX. Describe the XXXXXXXXXXXXX XXX XXX XXXXXXXXXXXX or differences.
Images Not Shown
XXXX the XXXXXXXX XX XXX XXXXXXXX that students XXXX don’t XXXX XXXXXXXXXXXX XXXX around 15 hours, whereas student that work XXXX a much wider range of XXXXX taken with the median XXXXX taken XXXXX less than the median XXXXX XXXXX XX students XXXX XXX’t XXXX.
6. XXXX XXX XXXX XXXXXX of credit hours taken XX XXX students XXXXXX to XX significantly below XX? XXX XXXXXXXXXX XX conduct a one-XXXXXX t-XXXX. Be XXXX XX state the XXXX and XXXXXXXXX hypotheses, XXXXXXX the output from XXXXXXXXXX, and briefly answer the XXXXXXXX including justification for XXXX XXXXXX.
HA : μ &XX; 15 (XXXXXXX XXXXXX XX XXXXX XXXXX is XXXX than XX
XXXXXXXX
|
XXXXXX Mean
|
Std. XXX.
|
XX
|
T-XXXX
|
P-value
|
XXXXX
|
14.XXX
|
X.XXXXXXXX
|
XXX
|
-X.XXXXXXX
|
X.0006
|
XXXX the p-XXXXX being extremely small XX reject the XXXX, therefore the XXXXXXXX XXXXXXXX XXX alternative XXXXXXXXXX in XXXXX XXXXXXXX XXXX below 15 hours.
X. For students XXX work, are XXXXX XXXXXXXXXXX in XXX XXXXXXX loan amounts XXXXXX XXXXXXX? Use XXXXXXXXXX XX XXXXXXX an XXXXX XXXX to XXXXXX XXXX question. XX sure to state XXX null XXX XXXXXXXXX XXXXXXXXXX, XXXXXXX the output XXXX StatCrunch, and briefly XXXXXX the question.
XXXXXXXX statistics by XXXXXX
XXXXX
|
n
|
Mean
|
Std. XXX.
|
Std. Error
|
X
|
23
|
3217.5652
|
XXXX.1127
|
412.XXXXX
|
X
|
XX
|
7377.7917
|
XXXX.XXXX
|
XXX.18491
|
3
|
XX
|
XXXX.3571
|
XXXX.5506
|
XXXX.XXXX
|
X
|
23
|
XXXXX.XXX
|
XXXX.XXXX
|
XXX.18316
|
XXXXX table
XXXXXX
|
DF
|
XX
|
XX
|
F-Stat
|
P-value
|
XXXXX
|
X
|
1.XXXXXXXXX
|
X.XXXXXXXXX
|
XX.896467
|
<0.XXXX
|
XXXXX
|
XX
|
X.XXXXXXXXX
|
13612511
|
|
|
Total
|
XX
|
2.XXXXXXXXX
|
|
|
|
1 XXXXXXXXXX XXXX
|
XXXXXXXXXX
|
Lower
|
Upper
|
P-value
|
2
|
XXXX.2264
|
1335.XXXX
|
6985.0398
|
X.0013
|
3
|
XXXX.7919
|
XXX.XXXXX
|
7023.3742
|
X.0189
|
X
|
XXXXX.087
|
9434.3806
|
15143.793
|
&XX;X.XXXX
|
2 XXXXXXXXXX XXXX
|
XXXXXXXXXX
|
Lower
|
XXXXX
|
P-value
|
X
|
-418.43452
|
-XXXX.0457
|
XXXX.XXXX
|
X.XXXX
|
4
|
8128.XXXX
|
5304.0472
|
XXXXX.XXX
|
&XX;0.0001
|
X subtracted from
|
XXXXXXXXXX
|
Lower
|
XXXXX
|
P-value
|
4
|
XXXX.XXX
|
5265.XXXX
|
XXXXX.877
|
&XX;X.XXXX
|
H0: Student XXXXX are the XXXX XXXXXX all classes
XX: Student loans XXX NOT the same across XXX classes
With the extremely XXX p-XXXXX XXXX XXX XXXXX XX reject XXX XXXX XXX say that XXX evidence supports that XXXXXXX XXXXX are not XXX XXXX across the classes.
8. XXX students that XXXX, is XXXXX a XXXXXXXXXXXX XXXXXXX XXX dollar XXXXXX of loans XXXX XXXX XXX the number of XXXXX XXX XXXX that they XXXX?
Parameter estimates:
XXXXXXXXX
|
Estimate
|
XXX. XXX.
|
Alternative
|
XX
|
T-Stat
|
P-value
|
Intercept
|
XXXXX.98
|
XXXX.XXXX
|
≠ 0
|
XX
|
6.XXXXXXX
|
&XX;0.0001
|
Slope
|
-XXX.12756
|
138.XXXXX
|
≠ 0
|
82
|
-X.2997572
|
0.024
|
XXXXXXXX XX variance table for XXXXXXXXXX model:
Source
|
DF
|
SS
|
MS
|
F-stat
|
P-value
|
XXXXX
|
X
|
X.7707362e8
|
X.XXXXXXXXX
|
X.XXXXXXX
|
X.024
|
Error
|
82
|
X.7453882e9
|
XXXXXXXX
|
|
|
XXXXX
|
XX
|
X.9224618e9
|
|
|
|
XXXXXXXXX a XXXXXXX plot with Work Hours on XXX x-XXXX and XXXX Amount on XXX y-axis.
Images Not Shown
XXXXXXX the XXXXXXXXXXX of XXXXXXXXXXX, the XXXXXXXXXXX of determination, and the XXXXXX regression XXXXXXXX.
Does there appear to be a relationship between work hours and loan XXXXXX? Explain your answer.
No XXXXX does not XXXXXX to XX a XXXXXX relationship between XXXX hours and loan amount, XXXX XXX small r-XXXXXXX XXXXX.
XXXXXXX XXX meaning XX the coefficient of determination XX it XXXXXXX XXXXXXXXXXXX to XXXX XXXXXXX.
Only about 6% XX the variance in loans is explained by XXX XXXXXXXXXX.
XXXXXXX XXX XXXXXXX XX the slope in the XXXXXXXXXX XXXXXXXX as it XXXXXXX XXXXXXXXXXXX to this XXXXXXX. XX this the relationship you XXXXXXXX? Explain. XX it isn’t XXXX you XXXXXXXX, XXXXXXX XXX it XXXXX XXXX XXXXXXXX.
XXX XXXXX being XXXXXXXX says XXXX as XXX XXXX XXXX increased XX X XXX loan amounts decreased by XXX.XXXXX. This intuitively XXXXX XXXXX XX XX, since people XXXX work more should XXXX XXXX money to pay for XXXXXX.
Does a linear XXXXX appear to be appropriate XXX this XXXXXXXXXX? XXXXXXX.
No a XXXXXX model XXXX XXX XXXXXX XX XXX the data XXXX. (But XXXXX XXXX XXX appear to XX a XXXXXX XXXXX from the scatterplot.)
X. For XXXXXXXX with credit card debt, XXXX XXXXX XXXXXX to XX a difference in XXX XXXX XXXXXX XX XXXXXX card debt XXXXX on gender XX the student? Conduct an XXXXXXXXXXX two-sample t-test. XX XXXX XX state the XXXX and alternate hypotheses, XXXXXXX XXX output from StatCrunch, and briefly answer XXX question.
(with pooled variances)
Difference
|
Sample Diff.
|
Std. Err.
|
DF
|
X-XXXX
|
P-value
|
μ1 - XX
|
877.XXXXX
|
299.25947
|
XXX
|
2.XXXXXXX
|
X.XXXX
|
The null hypothesis is XXXX XXX average credit card debt XX equal between XXX XXXXXXX. XXX XXXXXXXXXXX XX XXXX XXX XXXXXXX XXXXXX card XXXX differs between the genders. With XXX XXXXX p-value we reject the null XXX XXX XXX difference in XXXXXX XXXX XXXX XXXXXXX the XXXXXXX XX XXXXXXXXXXX.
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