Statistics and Desmos, taught with nursing examples. Pick the page for this week or jump to practice.
🎯 What this section is
MAT 300 in nursing language. Every topic on the course study guide is here, taught with patients, labs and vital signs instead of dice and coins, with a diagram for everything, and a Desmos recipe next to every calculation.
Start with the page for this week (map below), then do the matching practice questions. Formulas repeat on purpose — you will see the important ones three or four times.
🗺️ The whole course in one line
1 Collecta sample from a population
2 Describegraphs, center, spread
3 Relatecorrelation & regression
4 Model chanceprobability, binomial
5 Normal curvez-scores, CLT
6 InferCI and hypothesis tests
The one picture the whole course leans on: the normal curve and the 68–95–99.7 rule. z-scores, confidence intervals and p-values are all areas under it.
The mean is the balance point. The SD is the typical distance from it.
🔮 z-score
z = (x − μ) / σ
How many SDs away from the mean. Same formula works for a sample mean if you use σ/√n.
📏 Standard error
σx̄ = σ / √nσp̂ = √( p(1−p) / n )
Bigger samples → smaller error. Quadruple n to halve it.
⚖️ Confidence interval
estimate ± (critical value)(standard error)
x̄ ± t* s/√n · p̂ ± z* √(p̂q̂/n)
🎲 Binomial
μ = np σ = √(npq)
n tries, each success with probability p. Desmos: binomialdist(n,p).
📉 Regression
ŷ = mx + b
m = change in y per 1 unit of x. Desmos: y₁ ~ m x₁ + b.
Shape first, always: symmetric, skewed right, skewed left, uniform. It decides whether you report the mean or the median.
🎥 Your recordings and files
🎥 48 MAT 300 lecture recordings
All of them are on the Lectures page, grouped by week: Desmos walk-throughs, the best-fit line class, discrete vs continuous, sampling distributions in week 9, and the week-14 final review.
📁 Your own MAT 300 folder on Drive has the course statistics study guide, the Desmos study guide, the formula sheet, the module documents and the final-exam study questions. Those files are private to you, so they are not linked from this public page — open them from Drive. These pages were built from them.
🧠 Ten things to know cold before the final
Parameter describes a population (μ, σ, p). Statistic describes a sample (x̄, s, p̂).
Mean chases outliers; median does not. Skewed right → mean > median.
68–95–99.7: within 1, 2 and 3 SDs of the mean on a normal curve.
z > 2 or z < −2 is “unusual”.
r runs −1 to +1; sign = direction, size = strength. Correlation is not causation.
P(A or B) = P(A) + P(B) − P(A and B). P(A and B) = P(A)·P(B | A).
CLT: sample means are normal with SD σ/√n once n ≥ 30, whatever the population looks like.
Confidence level up → interval wider. Sample size up → interval narrower.
p-value ≤ α → reject H₀. You never “accept” H₀; you fail to reject it.
Type I = rejected a true H₀ (false alarm, probability α). Type II = missed a false H₀.
MAT 300 · built from your own course files (the statistics study guide, the Desmos guide, the formula sheet and the final-exam study questions) with nursing examples. Not a substitute for the textbook — check any number against your own notes before an exam.