MGMT 421 - UNIT 1

Data Analytics

What is the practice of examining prepared data to uncover valuable insights and guide decision-making?

Data Visualization

What is the practice of graphically representing data to help people see and understand patterns, insights and other discoveries hidden inside information?

Data Storytelling

What discipline translates seeing into meaning by weaving a narrative around data to answer questions and support decision-making?

Florence Nightingale

Which 19th-century woman is best known as the founder of modern nursing, for her work during the Crimean War and for using the Polar Area Diagram?

 

Charles Minard

Who created the famous flow map depicting Napoleon’s 1812 Russian campaign, widely regarded as one of the greatest examples of data visualization?

 

John Snow

Which 19th-century physician created the famous map of cholera cases in London, helping to identify contaminated water as the source of the outbreak?

William Playfair

Who is credited with inventing modern statistical graphics, including the line chart, bar chart, and pie chart, in the late 18th century?

Joseph Priestley

made a visual timeline showing the rise and fall of civilizations, empires, and nations. It reflected his belief that history could be studied as patterns of progress and decline.

Statistics definition

is the methodology of extracting

useful information from a data set.

To do good statistical analysis, you must:

1. Find the right data.

2. Use the appropriate statistical tools.

3. Clearly communicate the numerical information into

written language.

Two branches of statistics

1. Descriptive statistics

• Collecting, organizing, and presenting the data. (Summarizing)

2. Inferential statistics

• Drawing conclusions about a population based on sample data from that population.

Population

Consists of all items of interest.

Sample

A subset of the population.

sample statistic

is calculated from the sample data and is

used to make inferences about the unknown population

parameter.

Structured data

Data that has a well-defined length and format.

• Numbers, dates, groups of words and numbers (strings), and so on.

Unstructured data

Data that does not conform to a predefined row-column format.

• Reports, emails, multimedia, and so on.

Big data

Massive volume of data that is difficult to manage, process, and analyze

using traditional tools.

variable

the general characteristic being observed on objects of interest.

• Types of variables

• Qualitative: gender, race, political affiliation.

• Quantitative: test scores, age, weight.

• Discrete

• Continuous

Discrete

A discrete variable assumes a countable number of distinct values.

• Examples: Number of children in a family, number of points scored in a

basketball game.

Continuous

A continuous variable can assume an uncountable number of values

within an interval.

• Examples: Weight, height, time, investment return.

Qualitative variables versus quantitative variables:

Qualitative variables use labels or names to categorize.

• Nominal

• Ordinal

Quantitative variables assume meaningful numeric values.

• Interval

• Ratio

The Nominal Scale

The least sophisticated level of measurement.

• Data are simply categories for grouping the data.

Ordinal Scale

Ordinal data may be categorized and ranked with respect to some characteristic or trait.

• For example, instructors are often evaluated on an ordinal scale

(excellent, good, fair, poor).

• Differences between categories are meaningless because the actual numbers used may be arbitrary.

• There is no objective way to interpret the difference between instructor

quality.

The Interval Scale

• Data may be categorized and ranked with respect to some characteristic or trait.

• Differences between interval values are meaningful.

• Thus the arithmetic operations of addition and subtraction are meaningful.

• No “absolute 0” or starting point defined.

• Meaningful ratios may not be obtained.

• The differences between scale values are meaningful.

• For example, consider the Fahrenheit scale of temperature.

• This scale is interval because the data are ranked and differences (+ or −) may be obtained

• there is no “absolute 0”.

Ratio Scale

The strongest level of measurement.

• Ratio data may be categorized and ranked with respect to

some characteristic or trait.

• Differences between interval values are meaningful.

• There is an “absolute 0” or defined starting point.

• Zero does mean “the absence of …”

• Thus, meaningful ratios may be obtained.

• General examples measured on a ratio scale: weight, time, and distance.

• Business examples: sales, profits, and inventory levels.

• Unlike qualitative data, arithmetic operations are valid on interval- and ratio-scaled

values.

pth percentile

pth percentile divides a data set into two

parts:

1. Approximately p percent of the observations have values less than the

pth percentile.

2. Approximately (100 − p) percent of the observations have values greater

than the pth percentile.

• The median is also called the 50th percentile

boxplot (box-and-whisker plot)

allows you to:

• Graphically display the distribution of a data set.

• Compare two or more distributions.

• Identify outliers in a data set.

Measures of dispersion include:

• Range.

• Mean absolute deviation (MAD).

• Variance and standard deviation.

• Coefficient of variation (CV}

• Mean absolute deviation (MAD).

avg of absolute (think absolute value) differences btwn the observations and the mean

the variance and standard deviation

relative location / empirical rule

z scores