Analytical report

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Description

Phase 2 – Exploring data (100 points)

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In this phase, you will perform exploratory data analysis (EDA), the most important first step of any data analysis project.

a) Data visualization

Generate visualization using JMP for your variables that you want to analyze.
Create only the graphs that are relevant to your data.
For numerical variables, visualization (e.g., histograms) should be created, and shape and skewness should be discussed.
It is very important to explain your findings and insights from each visualization.

Please note, substantial points (20% to 30%) will be deducted if no interpretations and insights are found on your report.

b) Descriptive statistics

Calculate statistical summary measures for the numeric and categorical (qualitative) variables using JMP.
Summaries can include, but are not limited to, measures of center, measures of variation, measures of association (correlation), and quartiles.
Identify and comment on outliers and missing values.
It is very important to write your interpretations for each numerical measure. For example, you should interpret the mean, median, standard deviation, correlation coefficients, and the three quartiles.

Note: substantial points (20% to 30%) will be deducted if no interpretations and insights are found on your report.

Unformatted Attachment Preview

1
Tech Sales Report
Tech Sales Report
Name of Data Set
•
Tech Sales Data
Variables
1. Sales Rep
2. Business
3. Age
2
4. Female
5. Years
6. College
7. Personality
8. Certificates
9. Feedback
10. Salary
11. NPS
Data classification by type
a) Quantitative Variables
1. Age
2. Years
3. Certificates
4. Feedback
5. NPS
6. Salary
b) Qualitative Variables
1. Sales Rep
2. Business
3. Female
4. College
5. Personality
c) Discrete Variables
1. Age
2. Years
3. Certificates
4. Feedback
5. NPS
3
d) Continuous Variables
1. Salary
Results Summary
According to the tech sales data, an analysis of the various data points reveals
classification of the information into both qualitative and quantitative variables. Qualitative
variables often describe characteristics that cannot be quantified easily such as the sales rep
names, type of business, gender, college attended and personality (Stine & Foster 2011). On
the other hand, quantitative variable represents numerical values that are measurable and can
be analyzed such as age, years of experience, number of certificates, feedback scores and
NPS.
Additionally, quantitative data is further classified into discrete and continuous
variables. Salary is the only continuous variable since it takes on any numerical value within
a specific range (Stine & Foster 2011). On the other hand, age, years, certificates, feedback
and NPS are all considered as discrete variables since they represent distinct values in whole
numbers.
Data classification by level of measurement
a)
Nominal Variables
These variables represent categories that have no intrinsic order.
1. Sales Rep
2. Business
3. Female
4. College
5. Personality
b)
Interval
These variables have a meaningful zero point and can be used to show meaningful
comparisons of intervals.
1. Age
4
2. Years
3. Certificates
4. Feedback
5. Salary
6. NPS
Inferences Drawn
Analysis of the dataset shows that the average of the sales representatives is 41.5
years with a standard deviation of 11.4 year indicating that ages are spread our around the
mean. Age has a skewness of 0.0915 which is very close to 0 suggesting that the distribution
of ages is relatively symmetrical. The median age is 41 years which is similar to the mean
further supporting the symmetrical distribution. The mode is 50 years indicating there are
more sales representatives at the age of 50 than any other age in the sample.
Analysis of the variable years shows that the average sales representatives in the
sample has 2.64 years of experience. The standard deviation is 2.43 years indicating that the
years of service are spread out around the mean. This suggests that there are more sales
representatives with fewer years of experience than those with more experience. Years of
service is positively skewed with a skewness of 2.04. This suggests that there are more sales
representatives with fewer years of experience than those with more experience. The median
is 2 years which is lower than the mean supporting a positive skew. Likewise, the mode of 2
indicates that there are more sales representatives with 2 years of experience.
Analysis of the variable certificates indicates that on average a sales representative in
the sample possesses 2.61 certificate. The standard deviation suggest that the number of
certificates held by sales representatives varies around the mean by 1.64 certificates with a
slightly positive skewness of 0.226. This suggests that there might be slightly more
individuals with a higher number of certificates compared to those with a lower number.
Likewise, half of the sales representatives have 2 or fewer certificates as evident from the
5
median. This aligns with the mean further supporting the near symmetrical distribution. The
most frequent number of certificates held is 2.
On average a sales representative in the sample earns $75,687 with a standard
deviation of $23,360. The skewness is 0.907 which is slightly positively skewed. This
suggests there might be slightly more individuals with a higher salary compared to those with
a lower salary. However, the distribution is relatively symmetrical. Half of the sales
representative earn $71,600 or less evident from the median. Additionally, the mode indicates
that there are more sales representatives earning $67,000 than any other salary range.
On average customers gave a score of 6.27 with a standard deviation of 2.16
indicating individual NPS scores vary around the mean by 2.16. Thew skewness is of 0.062
which is close to 0 suggesting that the distribution of NPS scores is relatively symmetrical.
Half of the customers gave a score of 6 evident from the median while the mode indicates
more customers gave a score of 5.
Sales_Rep
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Business
Hardware
Hardware
Software
Hardware
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Hardware
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Hardware
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Hardware
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Age
59
52
47
61
39
28
25
51
34
38
53
41
40
41
46
38
39
52
54
24
53
37
32
33
55
27
58
52
26
28
62
44
42
36
52
53
56
51
40
49
43
40
43
59
22
48
Female
1
0
1
0
0
0
1
1
0
1
1
1
0
0
1
1
0
1
1
0
0
1
1
0
1
1
1
1
0
1
0
0
1
1
1
0
1
1
0
0
0
0
0
0
0
0
Years
2
10
1
2
1
6
1
10
4
1
11
1
1
2
2
4
2
1
2
1
1
1
1
2
1
2
2
7
1
8
7
2
2
1
1
2
2
2
2
2
2
2
4
6
1
2
College
Yes
Yes
Yes
Yes
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Yes
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Yes
Yes
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No
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Personality
Diplomat
Diplomat
Explorer
Diplomat
Diplomat
Explorer
Explorer
Explorer
Diplomat
Explorer
Explorer
Sentinel
Diplomat
Explorer
Analyst
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Certficates
1
4
1
3
5
1
5
0
2
5
2
1
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3
1
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5
1
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2
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4
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26
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2
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52
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42
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27
28
48
36
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4
4
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64
59
41
55
36
25
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46
33
56
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31
49
40
53
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38
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25
30
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24
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28
60
58
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27
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37
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32
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51
54
35
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32
47
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3
1
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564
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579
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583
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585
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600
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29
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1
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56
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2
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4
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38
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3
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812
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