MAS 261 - Lecture 1

Housekeeping

• Introduction 👋

• Syllabus 📃

• Navigating Slides ⬇️

• Today’s plan 📋

  • Navigating R and RStudio 🪄

  • General Introduction to Statistics and Analytics 📈

  • Population vs. Sample 🧮

  • Types of Data 🧮

A little about me… 👋

Introduction to R and RStudio 🪄

• In this course we will use R and RStudio to understand statistical concepts.

• You will access R and RStudio through Posit Cloud.

  • Sign up for a Free Posit Cloud Account

• I will post R/RStudio files on Posit Cloud that you can access in provided links.

• I will also provide demo videos that show how to access files and complete exercises.

• NOTE: The free Posit Cloud account is limited to 25 hours per month.

  • I will demo how to download completed work so that you can use this allotment efficiently.

  • For those who want to go further with R/RStudio:

    • After Test 1, I will provide videos on how to download the software (R/RStudio/Quarto) and lecture files to your computer

What is Statistics, Analytics, etc.? 📈

• Statistics (the discipline) allows us to answer questions about (almost) anything we want to know that we can collect data for.

Statistics, Analytics, Data Science…huh ❓

• Statistics is a discipline (what we study) and a statistic is an estimate based on data.

  • This dual meaning confuses people.

  • A newer term to describe the discipline of organizing, analyzing, and presenting data is Analytics.

• How do these terms, Statistics and Analytics, differ?

  • It depends on who you are talking to.
  • Analytics (like the Business Analytics major at Whitman) is the more modern term.
  • Another (overlapping) term is Data Science which is similar but more encompassing.

• Why so many terms? Good Question!

• What I Suggest: Allow clients, colleagues, employers to use terms they are comfortable with.

Where do YOU fit in to Statistics and Analytics ❓

• Data, statistics, and analytics are essential to everyday life.

  • This is especially true for management and business professionals.
  • Understanding the pandemic, politics, sports, weather, investments, all require data skills.

• Where you (students) are needed:

  • You are needed to understand data and communicate statistical information CORRECTLY, HONESTLY, AND ETHICALLY to your peers, and the world at large.

• You could google How do I communicate statistics?

• You could write a similar question in your AI tool of choice.

• This class will provide tools and information that those searches don’t provide.

  • More to come on how to use AI effectively and ethically in this course.

First Two Terms: Population vs. Sample

Bachelor’s Degrees by County

• What percent of people in each county of the United States have a Bachelor’s degree?

💥Lecture 1 In-class Exercises - Q1 💥

Poll Everywhere

You are writing an article about freshman students at SU to find out how far they are from home and how long it took them to travel to campus.

You randomly select 100 freshman students and ask them some questions to collect your data.

The POPULATION of interest is

A. All students at SU

B. All freshman students at SU

C. All freshman students in the USA

D. The 100 students you selected

💥Lecture 1 In-class Exercises - Q2 💥

Poll Everywhere

You are writing an article about freshman students at SU to find out how far they are from home and how long it took them to travel to campus.

You randomly select 100 freshman students and ask them some questions to collect your data.

The SAMPLE is

A. All students at SU

B. All freshman students at SU

C. All freshman students in the USA

D. The 100 students you selected

Components of Dataset

Within a dataset there are different TERMS for for each part of the dataset, e.g. the rows, columns and values.

Types of Variables in a Dataset

There are Four main types of data:

Types of Variables in a Dataset

Look at BOTH the data definitions (Data Dictionary) and the data values to determine data type.

Types of Variables in a Dataset

How does R define these variables 🧮

# A tibble: 3 × 6
  Obs_No Manufacturer Model        Body_Style  Num_Gears City_MPG
   <int> <chr>        <chr>        <chr>       <fct>        <dbl>
1      1 Ford         GT           coupe       7               11
2      2 Ferrari      458 Speciale coupe       7               13
3      3 Ferrari      458 Spider   convertible 7               13

Why define Num_Gears as Categorical Ordinal?

• If we treat Num_Gears as Categorical, then we can create an informative plot.

• Left plot shows average City Miles per Gallon for each body style and number of gears.

• The right plot is not useful because Num_Gears data are treated as integers.

Another Data Example

Country Demographics and Labor Information

💥Lecture 1 In-class Exercises - Q3 💥

Poll Everywhere

Which variable in the Country data set is categorical and ordinal?

A. Country

B. Income Level

C. Year

D. Population

E. Urban_Pop_Pct

# A tibble: 3 × 6
# Groups:   Country [3]
  Country       Income_Level         Year Population Urban_Pop_Pct Labor_Force
  <chr>         <chr>               <dbl>      <dbl>         <dbl>       <dbl>
1 Lesotho       Lower middle income  2018    2006756          0.34      674904
2 United States High income          2018  316740705          0.83   150798813
3 Angola        Lower middle income  2018   29783592          0.61    10738475

💥Lecture 1 In-class Exercises - Q4 💥

Poll Everywhere

Which variable in the Country data set is categorical and nominal?

A. Country

B. Income Level

C. Year

D. Population

E. Urban_Pop_Pct

# A tibble: 3 × 6
# Groups:   Country [3]
  Country       Income_Level         Year Population Urban_Pop_Pct Labor_Force
  <chr>         <chr>               <dbl>      <dbl>         <dbl>       <dbl>
1 Lesotho       Lower middle income  2018    2006756          0.34      674904
2 United States High income          2018  316740705          0.83   150798813
3 Angola        Lower middle income  2018   29783592          0.61    10738475

💥Lecture 1 In-class Exercises - Q5 💥

Poll Everywhere

Which variable in the Country data set is quantitative and continuous?

A. Country

B. Income Level

C. Year

D. Population

E. Urban_Pop_Pct

# A tibble: 3 × 6
# Groups:   Country [3]
  Country       Income_Level         Year Population Urban_Pop_Pct Labor_Force
  <chr>         <chr>               <dbl>      <dbl>         <dbl>       <dbl>
1 Lesotho       Lower middle income  2018    2006756          0.34      674904
2 United States High income          2018  316740705          0.83   150798813
3 Angola        Lower middle income  2018   29783592          0.61    10738475

💥Lecture 1 In-class Exercises - Q6 💥

Poll Everywhere

How many variables in this dataset are quantitative and discrete?

A. 0

B. 1

C. 2

D. 3

E. 4

# A tibble: 3 × 6
# Groups:   Country [3]
  Country       Income_Level         Year Population Urban_Pop_Pct Labor_Force
  <chr>         <chr>               <dbl>      <dbl>         <dbl>       <dbl>
1 Lesotho       Lower middle income  2018    2006756          0.34      674904
2 United States High income          2018  316740705          0.83   150798813
3 Angola        Lower middle income  2018   29783592          0.61    10738475

Country Variables

Key Points from Today

• Different terms for parts of a data set

  • Columns are Variables
  • Rows are Observations
  • Individual values are Data

• 4 Main Types of variables

  • Categorical Nominal (e.g. names, ID values)
  • Categorical Ordinal (e.g Letter Grades, Quality Ratings)
  • Quantitative Discrete (e.g., number of pets, years of education)
  • Quantitative Continuous (e.g. credit card balance, height, weight)