Unformatted Attachment Preview
Health Services and Nursing Scenario
Topic 1
Scenario 1
Year
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Predicting the Number of Babies Born
Review the data involving the number of babies born in Humboldt County from 2006-2015. Predict the n
be born in 2018.
Babies Born in
Hombolt County
275
280
320
366
358
336
375
390
455
487
ing Scenario
nty from 2006-2015. Predict the number of babies who will
Security and Criminal Justice Scenario
Topic 2
Scenario 2
Year
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Predicting the Number of People Arrested for Drug Possession
Review the data involving the number of people arrested for drug possession from 2006-2015. Predict the
people who will be arrested for drug possession in 2018.
People Arrested for
Drug Possession
1,519,760
1,361,658
1,321,824
1,387,915
1,179,728
1,143,931
1,237,708
1,203,323
982,169
801,560
Scenario
n from 2006-2015. Predict the number of
Humanities and Sciences Scenario
Topic 3
Scenario 3
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Predicting Student Smartphone Usage
Review the data involving the average number of hours students spent on their smartphones from 2002number of hours students will spend on their smartphones in 2018.
Average Number of Hours
Students Spend on their
Smartphones
0.1
0.75
1
1.5
1.75
5.5
6
9.3
9.5
8.9
9
11
12.5
10.6
Scenario
n their smartphones from 2002-2015. Predict the
Social Sciences Scenario
Topic 4
Scenario 4
Hours of Sleep
4
4.25
4.5
4.75
5
5.25
5.5
5.75
6
6.25
6.5
6.75
7
7.25
7.5
7.75
8
8.25
8.5
8.75
9
9.25
9.5
9.75
10
Predicting Test Performance Based on Sleep
Review the data involving the number of hours students sleep and their average score on a test they take
Predict the optimal hours of sleep students need the night before a test to achieve the highest score on t
Average Score
on Test
55
40
53
59
60
63
66
75
70
72
80
77
85
90
100
95
88
85
74
88
90
87
86
95
82
rio
average score on a test they take the next day.
o achieve the highest score on the test.
SUMMARY OUTPUT
5
Regression Statistics
Multiple R 0.777042
R Square
0.603795
Adjusted R Square
0.554269
Standard Error
2.021353
Observations
10
ANOVA
df
Regression
Residual
Total
SS
MS
F
Significance F
1 49.81306 49.81306 12.19155 0.008179
8 32.68694 4.085867
9
82.5
Coefficients
Standard Error t Stat
P-value Lower 95% Upper 95%Lower 95.0%
Upper 95.0%
Intercept
-5.3827 3.462696 -1.55448 0.158677 -13.3677 2.602289 -13.3677 2.602289
5325 0.001334 0.000382 3.49164 0.008179 0.000453 0.002215 0.000453 0.002215
RESIDUAL OUTPUT
ObservationPredicted 1
1 2.151453
2 2.451592
3 7.746044
4 5.60105
5 7.18445
6 8.120884
7 7.668674
8 8.547748
9 7.375205
10 8.152899
PROBABILITY OUTPUT
Residuals
Standard Residuals
-0.15145 -0.07947
0.548408 0.287765
-3.74604 -1.96565
-0.60105 -0.31539
-1.18445 -0.62151
-1.12088 -0.58816
0.331326 0.173856
0.452252 0.237309
2.624795 1.377303
2.847101 1.493953
Percentile
5
15
25
35
45
55
65
75
85
95
1
2
3
4
5
6
7
8
9
10
11
5325 Residual Plot
2
0
-2 -
2,000
-4
4,000
15
-6
10
1
Residuals
4
Normal
Probability Plot
8,000 10,000 12,000
6,000
5325
5
Series1
0
5
15 25 35 45 55 65 75 85 95
Sample Percentile
Business Scenario
Predicting Fuji Apple Purchases
Topic 5
Review the monthly data involving Fuji apples purchased at a large grocery store. Predict how many Fu
have available for the customers in December (month 12)?
Scenario 5
Fuji Apples
Purchased
5,325
5,648
5,873
9,842
8,234
9,421
10,123
9,784
10,443
9,564
10,147
Month
1
2
3
4
5
6
7
8
9
10
11
Hint: When determining the solution to this question remember that am
up around holidays. Seasonality is a characteristic of a time series in wh
and predictable changes that recur every calendar year. Any predictable
series that recurs or repeats over a one-year period can be said to be se
Linear Regression
12,000
Fruit Purchased
10,000
8,000
6,000
y = 502.34x + 5568.2
R² = 0.6998
4,000
2,000
0
2
4
6
Month
8
10
12
rio
ry store. Predict how many Fuji apples will need to be in stock to
his question remember that amounts needed in a store will go
acteristic of a time series in which the data experiences regular
calendar year. Any predictable change or pattern in a time
ear period can be said to be seasonal.
FUJI APPLE PREDICTED(12)
Fuji apples in December (y)=502.5x+5568
y=502.5(12)+5568
11596
Education Scenario
Topic 6
Elementary Education: Math Skills
Scenario 6
Review the data involving elementary students in Apache County who passed the AZ Merit Test fr
pass the AZ Merit test in 2018.
Fiscal Year
Students in Apache
County who Pass
the AZ Merit Test
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
12
7
14
18
25
37
33
39
45
42
cenario
passed the AZ Merit Test from 2006-2015. Predict the number of students who will