Business Finance - Accounting Week 6 Assignment - Repairing Customer Satisfaction - Progress Report 2

cf_WK6_Types_of_Data_Analysis.pdf

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BUS512 – Data-Driven Decision-Making for Leaders

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Reference Sheet: Types of Data Analytical Methods & Tools

Analytical Methods

Analytical methods are techniques used to structure data in a systematic way to add order and structure to our data. The

analytical Method you will choose depends on the context of the problem/opportunity you are seeking to solve. Remember

that in most cases, we will utilize several analytical methods as we prepare to craft communication to our audience. Below

are the most common analytical tools:

a. Descriptive: What happened? Use this method to communicate a Framed Problem/opportunity to your audience.

b. Diagnostic: Why Did it happen? Once we have the problem or opportunity framed, we need to uncover the root causes for a problem or the scope of possibilities for an opportunity. Diagnostic methods allow us to drill down to a specific focus.

c. Predictive: What is likely to happen in the future? After we analyze data, patterns, trends, and anomalies will be revealed. Predictive methods will allow us to make forecasts and show possibilities.

d. Prescriptive: What’s the best course of action to achieve our outcome?

BUS512 – Data-Driven Decision-Making for Leaders

further distributed, or otherwise disclosed in whole or in part, without the expressed written permission of Strayer University.

Analytical Tools

Data analysts use many different types of analysis when looking for patterns, correlations, and causations in data sets. Each type of analysis

serves a different purpose; therefore, it’s important to select the most useful option(s), depending upon your organization; the issue, problem or

opportunity; and the particular data sets that you have collected. This reference sheet is intended as a resource to support you in deciding

which type(s) of data analysis are most useful to apply to your work as you prepare for your Capstone project.

Type of Analysis Definition Primary purpose Recommended use

Grouping and Visualizing Definition: Grouping quantitative data and metrics into a limited set of clearly defined variables defines and matches that type of graphic illustrations and visualization medium that effectively communicates the analytical “story” to the targeted stakeholders.

Purpose: The purpose of grouping values in a selected data set is to create categories for analyses based on the defined analytical problem or opportunity.

Recommended use: Unique data visualizations are a more “user-friendly” way of communicating quantitative data and metrics to stakeholders.

BUS512 – Data-Driven Decision-Making for Leaders

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How To Steps: 1. Group the raw data into categories.

2. Identify and define 2 or 3 variables you want to measure.

3: Create a visual illustration to show your selected categories (e.g., bar chart, histogram, line graph, or pie chart)

Variance Definition: Statistical variance measures how the data distributes from the mean or the expected value.

Purpose: The variance is used to measure probability distributions. For example, the variance can help determine the risk an investor might take on when purchasing a specific security in the market.

Recommended use: Unlike the range that only looks at the extremes, the variance looks at all the data points or observations and then determines their distribution.

How To Steps: 1. Select a data set and calculate the MEAN.

2. For each number or observation in the data set, subtract the MEAN and square the results (squared differences)

3. Calculate the average of each squared differences

Standard Deviation Definition: A standard deviation (SD) is a statistical measure that is used to quantify the amount of variation or dispersion of a set of data values.

Purpose: A standard deviation assesses how far the values are spread above or below the mean of a selected population or sample data set.

Recommended use: A high standard deviation shows that the data is widely spread (less reliable) and a low standard deviation shows that the data are clustered closely around the mean (more reliable).

How To Steps: 1. Select a sample data set.

2. Calculate the mean of the sample.

3. Subtract mean value from each data value.

4. Square each result.

5. Find the sum of the squared values.

6. Divide by n-1, where n is the number of data points.

BUS512 – Data-Driven Decision-Making for Leaders

further distributed, or otherwise disclosed in whole or in part, without the expressed written permission of Strayer University.

Correlation Definition: A statistical measure that indicates either a positive or negative relationship between two or more variables.

Purpose: The purpose of correlation in analytics is to determine which variables are connected.

Recommended use: Correlations test the strength of the relationship between variables.

How To Steps: 1. If there are no associations between the selected variables tested, then there are no causal

connections between these variables.

2. If there is an association between the selected variables tested, then a correlation coefficient (R) in a

statistical table to represent the estimated strength of the linear relationship between these variables