MATH110 Introduction to Statistics Module 1 Summary Study
guide 2025 Portage Learning Online
Summary: Statistics Module 1
1. What Is Statistics?
Statistics is the science of collecting, organizing, analyzing, and interpreting data to make
decisions. It helps us draw conclusions about large groups (populations) by studying smaller
subsets (samples).
2. Understanding Data
Data = collected information.
Element = the individual item you're collecting data about (e.g., a
student). Variable = measurable characteristic (e.g., major, exam
score).
Observation = all data collected for one element.
Example:
In a table of student grades, each student is an element. Their major, class, and grades are
variables. One full row = one observation.
3. Types of Data
Type Description Example
Qualitative ---------------- Describes categories/qualities Major,
Campus, Class Quantitative --------------- Numerical values (how
much/many) Exam Scores, GPA
4. Levels of Measurement
Level Features Examples
Nominal Names only Zip Code,
Major Ordinal -------------------- Order matters, no math--------------Class Rank
Interval -------------------- Order + meaningful differences, no true zero Temperature in °C
Ratio ---------------------- All of the above + true zero-------------------GPA, Income, Age
Tip: If you can say “twice as much,” it’s ratio level.
5. Data Collection Methods
Type Description
Cross-sectional - Collected at a single point in time
Time series - Collected over a period (e.g., stock prices over months)
6. Experimental vs. Observational Studies
guide 2025 Portage Learning Online
Summary: Statistics Module 1
1. What Is Statistics?
Statistics is the science of collecting, organizing, analyzing, and interpreting data to make
decisions. It helps us draw conclusions about large groups (populations) by studying smaller
subsets (samples).
2. Understanding Data
Data = collected information.
Element = the individual item you're collecting data about (e.g., a
student). Variable = measurable characteristic (e.g., major, exam
score).
Observation = all data collected for one element.
Example:
In a table of student grades, each student is an element. Their major, class, and grades are
variables. One full row = one observation.
3. Types of Data
Type Description Example
Qualitative ---------------- Describes categories/qualities Major,
Campus, Class Quantitative --------------- Numerical values (how
much/many) Exam Scores, GPA
4. Levels of Measurement
Level Features Examples
Nominal Names only Zip Code,
Major Ordinal -------------------- Order matters, no math--------------Class Rank
Interval -------------------- Order + meaningful differences, no true zero Temperature in °C
Ratio ---------------------- All of the above + true zero-------------------GPA, Income, Age
Tip: If you can say “twice as much,” it’s ratio level.
5. Data Collection Methods
Type Description
Cross-sectional - Collected at a single point in time
Time series - Collected over a period (e.g., stock prices over months)
6. Experimental vs. Observational Studies
