A
retail store manager
uses time series models to understand shopping trends. Time series
models are particularly useful to track variables such as revenues, costs, and profits over time.
Time series models help evaluate performance and make predictions.
Review the scatter plot of the store’s sales from 2010 through 2021 to answer the questions.
You may also review the
annual sales data and chart
in Excel, if desired.
According to Todd (2021), t
he primary difference, though, between discrete
and continuous data is that discrete data is a finite value that can be
counted, whereas continuous data has an infinite number of possible values
that can be measured. An example of discrete data is pulling information
about medication dosage and dispensing. Newman (2017) states h
ere is the
same data with each data element captured discretely:
Medication Name: Prilosec; Dosage Qty: 1; Dosage Strength: 20; Dosage
Units: mg; Dosage Form: Tablet; Frequency: BID; Duration Number: 3;
Duration Length: Days. The discrete data can be sliced and analyzed
independently when querying the data. However, continuous data that can
be qualitative are vital signs collected: Blood Pressure, Pulse, Height, Weight,
Temperature, and Pulse Oxygen. These variables can yield continuous data
and numerous examples due to the number of patients whose information
can be pulled and queried. The sampling measurement I am most familiar
with is simple random sampling. All patients assigned to a provider or clinic
are placed in an Excel sheet, each given a number based on a formula
(=RAND), and sorted from smallest to largest three times. The top X patients
can then be selected to participate in surveys or provide feedback. Another
random sampling is placing comment cards in the rooms and allowing
whichever patient to complete the comment card. This provides a sample of
data for further investigation and is only one data point.
INTERNAL
USE
