Final - Requires Respondus LockDown Browser + Webcam
- Due May 5 at 11:59pm
- Points 125
- Questions 18
- Available after May 5 at 8am
- Time Limit 120 Minutes
- Requires Respondus LockDown Browser
Instructions
General Formulas
\(\displaystyle\overline{x}=\frac{\sum x}{n}\)
\(\displaystyle\mu=\frac{\sum x}{N}\)
\(\displaystyle s=\sqrt{\frac{\sum\left(x-\overline{x}\right)^2}{n-1}}\)
\(\displaystyle \sigma=\sqrt{\frac{\sum\left(x-\mu\right)^2}{N}}\)
\(\displaystyle \textrm{CV}=\frac{s}{\overline{x}}(100\%)\)
\(\displaystyle \textrm{CV}=\frac{\sigma}\mu{}(100\%)\)
\(\displaystyle z=\frac{x-\overline{x}}{s}\)
\(\displaystyle z=\frac{x-\mu}{\sigma}\)
Probability/Counting Formulas
\(\displaystyle P(A\textrm{ or }B) = P(A)+ P(B) - P(A\textrm{ and }B)\)
If \(A\) and \(B\) are disjoint, then \(P(A\textrm{ or }B)=P(A)+P(B)\)
\(\displaystyle P(A\textrm{ and }B) = P(A)\cdot P(B|A)\)
If \(A\) and \(B\) are independent, then \(P(A\textrm{ and }B)=P(A)\cdot P(B)\)
\(\displaystyle P(B|A)=\frac{P(A\textrm{ and }B)}{P(A)}\)
Baye's Theorem: \(\displaystyle P(A|B)=\frac{P(A)\cdot P(B|A)}{P(B)}\)
\(\displaystyle nPr=\frac{n!}{(n-r)!}\)
\(\displaystyle nCr=\frac{n!}{(n-r)!r!}\)
Distribution Formulas
|
Applies to all Distributions |
Binomial Distribution |
Poisson Distribution |
|
\(\displaystyle\mu=\sum\left[x\cdot P(x)\right]\) |
\(\displaystyle P(x)=\frac{n!}{(n-x)!x!}\cdot p^x\cdot q^{n-x}\) |
\(\displaystyle P(x)=\frac{\mu^x\cdot \textrm{e}^{-\mu}}{x!}\) |
Formulas involving the standard normal distribution and sampling distributions
\(z=\frac{x-\mu}{\sigma}\)
\(\mu_{\overline{x}}=\mu\)
\(\sigma_{\overline{x}}=\frac{\sigma}{\sqrt{n}}\)
Formulas involving sample proportions
\(\displaystyle E=z_\frac{\alpha}{2}\sqrt{\frac{\hat{p}\hat{q}}{n}}\)
\(\displaystyle n=\frac{\left[z_\frac{\alpha}{2}\right]^2(0.25)}{E^2}\)
Parameters and Corresponding Test Statistic for Hypothesis Testing
| Parameter | Sampling Distribution | Test Statistic |
| Proportion \(p\) | Normal (\(z\)) | \(\displaystyle z=\frac{\hat{p}-p}{\sqrt{\frac{pq}{n}}}\) |
| Mean \(\mu\) (\(\sigma\) unknown) | \(t\) | \(\displaystyle t=\frac{\overline{x}-\mu}{\frac{s}{\sqrt{n}}}\) |
| Mean \(\mu\) (\(\sigma\) known) | Normal (\(z\)) | \(\displaystyle z=\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\) |
| \(\sigma\) or \(\sigma^2\) | \(\chi^2\) | \(\displaystyle \chi^2=\frac{(n-1)s^2}{\sigma^2}\) |