Week 2 — Readings & Resources · Summarizing Data

Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Objective covered: Objective 2 — Summarize and display univariate data — its shape, center, and spread (this week: the display half — tables and pictures of one variable).

How to use this page

Everything here is a link to an external resource — open it in your browser, the same way you'd open a YouTube link. Nothing needs to be downloaded.

This week's load is deliberately light: ~4 short readings + ~3 short videos, grouped by the four ideas from the lecture. Read or watch one item per group and you're ready for the assignment; do all of them and you'll be very comfortable. Total time is roughly 40–50 minutes if you do everything, far less if you pick one per group.

Reading order that matches the lecture: ① frequency & relative-frequency tables → ② building and reading histograms → ③ shapes of distributions → ④ outliers and how they distort the picture.

A habit to start now: as you read or watch, describe every distribution you see with the same four-word checklist from class — Shape · Center · Spread · Outliers (S-C-S-O). And remember the week's mantra: skew is named for the tail, not the lump.

① Frequency & Relative-Frequency Tables

Maps to Lecture Segment 2. The first move from a pile to a picture is a table: frequency = how many, relative frequency = what share (count ÷ total, and the shares add to 1).

Reading — "Frequency Distribution | Tables, Types & Examples" (Scribbr)
🔗 https://www.scribbr.com/statistics/frequency-distributions/
Why it's assigned: the cleanest plain-language walk-through of building a frequency table and turning counts into relative (and cumulative) frequencies — exactly the table we built on the board, including how to choose classes.
⏱ ~9 min

② Building & Reading Histograms

Maps to Lecture Segment 3. The picture of a frequency table for quantitative data. Remember: bars touch → histogram (a number line); bars apart → bar chart (categories).

Reading — "Histograms review" (Khan Academy, article)
🔗 https://www.khanacademy.org/math/statistics-probability/displaying-describing-data/quantitative-data-graphs/a/histograms-review
Why it's assigned: a tight, example-driven refresher on what each axis and bar means and how to read a histogram — the same "read it without arithmetic" skill from Segment 3.
⏱ ~5 min

Video — "How to create a histogram" (Khan Academy)
🔗 https://www.youtube.com/watch?v=gSEYtAjuZ-Y
Why it earns the click: watches someone turn a raw list into bins and bars step by step — the exact build you'll do in a spreadsheet, at floor difficulty.
⏱ ~4 min

Video — "Charts Are Like Pasta — Data Visualization Part 1: Crash Course Statistics #5"
🔗 https://www.youtube.com/watch?v=hEWY6kkBdpo
Why it earns the click: the liveliest tour of bar charts vs. histograms and what a picture can and can't tell you — nails the histogram-vs-bar-chart distinction from Segment 3, and how graphs can mislead.
⏱ ~11 min

③ Shapes of Distributions

Maps to Lecture Segment 5. Five shapes to recognize — symmetric, skewed left, skewed right, uniform, bimodal — and the rule that the tail names the skew.

Reading — "Skewness | Definition, Examples & Formula" (Scribbr)
🔗 https://www.scribbr.com/statistics/skewness/
Why it's assigned: settles the week's #1 trap — left- vs. right-skew named by the tail — and previews why the mean and median split apart in skewed data (our Week-3 hook).
⏱ ~7 min

④ Outliers & How They Distort the Picture

Maps to Lecture Segment 6. An outlier sits far from the rest; it drags the mean (but barely moves the median) and can flatten a whole histogram.

Reading — "How to Find Outliers | 4 Ways with Examples & Explanation" (Scribbr)
🔗 https://www.scribbr.com/statistics/outliers/
Why it's assigned: shows how to spot an outlier (including the 1.5 × IQR idea we previewed) and why it matters — the exact reason a neighborhood's "average" home price can mislead.
⏱ ~8 min

Video — "Plots, Outliers, and Justin Timberlake — Data Visualization Part 2: Crash Course Statistics #6"
🔗 https://www.youtube.com/watch?v=HMkllhBI91Y
Why it earns the click: puts outliers and the spread of data on screen with vivid examples — exactly the "one wild value distorts the story" lesson from Segment 6.
⏱ ~11 min

Optional one-stop reference (free online text)

If you'd like one optional reference to skim all term, OpenIntro Statistics keeps its full text and per-section videos free to read online. Chapter 2 ("Summarizing Data") covers everything in this week — examining numerical data, histograms, shape, and considering categorical data.
🔗 https://www.openintro.org/book/os/
Why it's here: a reputable, currently-available reference you can return to in later weeks — entirely optional this week.

Pick-one quick path (≈15 min total)

In a hurry? Do exactly these four and you'll be ready for the assignment:
1. Read Frequency Distribution (group ①).
2. Watch How to create a histogram (group ②).
3. Read Skewness (group ③).
4. Read How to Find Outliers (group ④).

Heads-up (links rot): these point to outside sites that occasionally move or rename pages. If a link ever fails, tell Prof. Rivera and use the OpenIntro reference above in the meantime.