NJIT using R program for Multivariate Data Analytical Review to find ways to increase sales of orthopedic products from our company to all hospitals in the

NJIT using R program for Multivariate Data Analytical Review to find ways to increase sales of orthopedic products from our company to all hospitals in the United States. Find those who have high consumption of such equipment but where our sales are zero. Come up with a selected group where you think our efforts will be rewarded. (a few hospitals 5 or 10 or 15). Estimate the potential or expected sales on those hospitals. Final Report:
(Due May 8 but possibly extended to May 10-12)
SALES OF ORTHOPEDIC EQUIPMENT
The objective of this study is to find ways to increase sales of orthopedic products from our
company to all hospitals in the United States. Find those who have high consumption of such
equipment but where our sales are zero. Come up with a selected group where you think our efforts
will be rewarded. (a few hospitals 5 or 10 or 15). Estimate the potential or expected sales on those
hospitals.
The following description of the dataset includes variable names and some summaries of
variable.
A file with a shell SAS program that follows the analysis steps is provided in another link.
At the bottom of the file is also some additional R code.
Dataset: hospitalUSA.csv
VARIABLES:
ZIP
HID
CITY
STATE
BEDS
RBEDS
OUT-V
ADM
SIR
SALES
HIP
KNEE
TH
TRAUMA
REHAB
HIP2
KNEE2
FEMUR2
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:
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US POSTAL CODE
HOSPITAL ID
CITY NAME
STATE NAME
NUMBER OF HOSPITAL BEDS
NUMBER OF REHAB BEDS
NUMBER OF OUTPATIENT VISITS
ADMINISTRATIVE COST(In $1000’s per year)
REVENUE FROM INPATIENT
: SALES OF REHAB. EQUIP in $1000’s per year
: NUMBER OF HIP OPERATIONS
: NUMBER OF KNEE OPERATIONS
: TEACHING HOSPITAL? 0, 1
: DO THEY HAVE A TRAUMA UNIT? 0, 1
: DO THEY HAVE A REHAB UNIT? 0, 1
: NUMBER HIP OPERATIONS Year 2
: NUMBER KNEE OPERATIONS Year 2
: NUMBER FEMUR OPERATIONS Year 2
Overview of the Analysis
Part 1. Select your market segment-s.
1. Dataset: hospitalUSA.csv
Select a group of states for the study (it is enough to select about 3000-3500
hospitals at random). Set the zero values on SALES to missing values.
Separate the variables into the following groups:
Response:
SALES, SALES=0 => SALES=NA
Demographics:
BEDS, RBEDS, OUTV, ADM, SIR, TH, TRAUMA, REHAB
Operation numbers: HIP, KNEE, HIP2, KNEE2, FEMUR2
2. Transformations:
Look at each individual variable and decide “if and which” transformation is
appropriate. Some transformations are log(1+c*x) where the constant c changes
from variable to variable ( 0.1,0.01,0.001,…) or sqrt transformation or any other.
Typical transformations should be of the type below but not exactly, so you need
to try several possibilities for each variable until the histogram looks acceptable.
HIP = sqrt(HIP) or SALES = log(1+0.1*SALES)
3. Dimension reduction.
Use the factor method to summarize the demographic variables and the
operation variables and come out with a final reduced list of factor variables
(perhaps 3 or 4). Use the rotated factors in order to find a good interpretation of
the factors and try to make a good story.
4. Market segmentations.
i) Independent variables are used to divide the list of hospitals (all possible
clients = the market) into subsets which we call market segments or
clusters.
Use cluster analysis to find the market segments or clusters. Since we are
summarizing the variables with factors then use the factors. One way of
choosing the number of clusters is to move the data into R and apply the
silhouette function with pam to calculate the silhouette statistic and of
cluster it to decide the number clusters. Then move the cluster variable
back to SAS if you prefer.
ii) Once the clusters are chosen, we must study the summary statistics for
each cluster and try to describe their content. Interpretation is very
important at this stage. You do a boxplot of SALES or transformed SALES
VS CLUSTER_NUMBER and choose clusters with the highest SALES and
focus on the top cluster or clusters.
iii) Finally, we select the cluster or clusters that agree with our objectives.
These are clusters with high sales and with good characteristics, such as
high number of operations, etc.
In this study you are looking for segments with over all high sales but
where there are hospitals were the company’s sales is NA so they are not
yet our customers. Some segments will have mostly low sales. This means
that those hospitals have few patients who would need our products, so we
are not interested in them.
Part 2. Estimating potential gain in sales. Potential gain in sales is the difference
between current sales and the average of sales to similar hospitals. If you are
analyzing a very small cluster (N
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