Formulate the Discussion section of your selected topic.  In this section, you need to analyze and interpret the Results. Then, go back to your initially stated Hypothesis(es) to compare and contrast with the final Results.

Selected Topic: : – Strategies for detecting elderly abuse in nursing homes

Results- Strategies for detecting elderly abuse in nursing homes

Formulation of the results

The strategies for detecting elderly abuse in nursing homes differ with the data that researchers aim to collect. For instance, this social aspect can be determined using daily cases, annual cases, and how it affect different genders. This study formulates the results from different databases such as EBSChhost, by selecting the MEDLINE complete and CINAHL complete. The visuals below reflects the results of elderly abuse in nursing homes between 2015 and 2020. The objective of this formulation is represented in the table below:

https://i.stack.imgur.com/LMu4W.gif  this link is for the erd diagram

Business scenario:

Please install AdventureWorks database, read the AdventureWorks case study, and analyze the ERD OLTP schema before starting this assignment.

Q1:

The CEO of AdventureWorks Inc. wants to build a data mart to keep track of the sale orders’ performance. Specifically, he wants to know the total order quantity for each products, total order price, and total tax order percentage paid by customer (defined by total tax amount divided by total order price). Provide a 4-steps dimensional model in the SQL comment section and create a data mart solution in SQL for the dimension model that you propose

Q2:

The CEO of AdventureWorks Inc. wants to build a data mart to keep track of the sale orders’ performance. The figure below shows the current design of the data mart. However, the data mart designer(s) assume that one order can only be paid by one credit card. If one order can be paid by multiple credit cards, what changes should we make to keep track with total order amount and total tax amount for each credit card? Create data mart solution in SQL for the dimension model that you propose

HW: Nearest Neighbor
1) With the goblet data set, complete the following: Apply other agglomerative methods
to determine appropriate clusters for the standardized and non-standardized data.
Do the results agree with those from the nearest neighbor method examined in
class?
2)Do hierarchical clustering of the tennis racquet data. For clustering, you may pick your
favorite specific approach( . Give the partitions of racquets into clusters, give some
plot(s) to visualize the cluster structure and try to justify #of clusters and characterize
the clusters.
The racquet data can be read in with the following code:
racq.data <- read.table(“racquetsdata.txt”,header=T)
racquet.names <- as.character(racq.data[,1])
racquet.numeric.data <- racq.data[,-1]
The variables in the tennis racquets data set are:
X1 = length of racquet (in inches)
X2 = static weight (in ounces) = this is how much the racquet actually weighs on a scale
X3 = balance (in inches) = this is a measure of whether the racquet is heavier in on the
head end or on the handle end;
more negative values indicate a more head-heavy racquet; positive values indicate a
more head-light racquet;
zero indicates an even balance.
X4 = swingweight = this is a complicated measure of how heavy the racquet FEELS
when it is swung
X5 = headsize (in square inches) = the size of the racquet face (the strung area)
X6 = beamwidth (in mm) = the width of the cross-section (edge) of the racquet