Unit 2 - Data & Exploratory Data Analysis

Comprehensive vocabulary flashcards covering data types, EDA, graphical and numerical summaries, probability, regression, and hypothesis testing based on the DATA1001/1901 lecture transcript.

What rule states that expected frequencies must be at least 5 for the Chi-squared test to be valid?

Cochran's Rule: You need enough snacks at a party for everyone—5 of each type keeps it fun!

What do we collect to understand different subjects, like clues in a mystery?

Data: Think of it like pieces of a puzzle to see the full picture!

What type of variable can measure infinitely precise things, like height or temperature?

Continuous Variable: Like a flexible measuring tape, you can measure tiny lengths!

What type of variable counts in whole numbers only, like how many toys you have?

Discrete Variable: Counting Lego blocks—no half blocks allowed!

What do we call categories without a ranking, like different ice cream flavors?

Nominal Variable: It’s just a fun mix of choices—chocolate and vanilla are different, not ranked!

What type measures categories that have a clear order, like ranking your favorite games?

Ordinal Variable: Imagine a race—there's a clear order of winners!

What analysis gives us the first peek at data, like a sneak preview of a movie?

Exploratory Data Analysis (EDA): It’s like opening a treasure chest to see what’s inside!

What stage involves cleaning and organizing messy data, just like tidying your room?

Wrangling: Like sorting toys into neat piles to find your favorites faster!

What data format organizes each variable into its own column, like filing papers?

Tidy Data: Arranging your stickers so each type is easy to find!

What research method uses random assignment, like a surprise game night with friends?

Randomised Controlled Trial (RCT): Splitting guests into teams for a fair game!

What process combines data from different sources, like putting together pieces of a puzzle?

Data Linkage: Connecting different dots to create a beautiful mural!

What graph type helps compare one qualitative (label) and one quantitative (number) variable?

Comparative Boxplot: Like a scoreboard comparing different teams' performances!

What graph displays the relationship between two numbers, like tracking your height over years?

Scatterplot: Tossing candy and seeing where each piece lands!

What type of histogram counts items in bins, like sorting candies into jars?

Count Histogram: Separating jellybeans by color and counting how many are in each jar!

What histogram indicates how dense data is instead of just counting?

Density Histogram: Sweeping sprinkles on a cupcake—more sprinkles show where it’s thicker!

What range measures the middle spread of data, like slicing your pizza evenly?

Interquartile Range (IQR): Cutting a pizza to keep the cheesy middle slice!

What tells how far data points are from the average, like measuring friends' heights?

Standard Deviation (SD): Lining up friends by height shows their variety!

What rule tells us that adding a constant doesn’t affect spread?

Transformation Rule for spread (SD/IQR): Moving a table doesn’t change how far apart your toys are!

What is the bell-shaped curve that shows how data is spread?

Normal Distribution: Kids at a carnival gathering at the Ferris wheel—the biggest crowd equals the most popular spot!

What measures how closely two variables relate, like a dance partner?

Correlation (r): Moving in sync with your best friend at a dance!

What conditions define a situation with fixed trials and consistent success in each?

Binomial Distribution Requirements: Tossing a coin a set number of times, same chance for each flip!

What theorem explains how averages become normal regardless of the source?

Central Limit Theorem (Box Model): Pouring candies into a jar—the more you add, the average becomes even!

What is the mistake that mixes up evidence probability right and innocence probability?

Prosecutor's Fallacy: It’s not just the lucky hat that makes someone win!

What defines the slope and intercept of a regression line?

Regression Line Components: A ramp shows how high each level goes!

What gaps exist between actual data and the predicted line?

Residuals: How far off your basketball shot was from the target!

What shows how much of the variation in one variable is explained by another?

R-squared (R²): Knowing how many puzzle pieces fit perfectly into the whole picture!

What indicates the probability of extreme results under the null hypothesis?

P-value: It’s like rolling dice and guessing how often you land on a six!