Study Guide for Exam #1
Study Guide for Exam #1 STAT 201
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This 5 page Study Guide was uploaded by AlliSlaten on Wednesday September 21, 2016. The Study Guide belongs to STAT 201 at Colorado State University taught by Kirk Ketelsen in Fall 2016. Since its upload, it has received 131 views.
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Date Created: 09/21/16
Typpe Visual Why It Is Used and When Histogram compares data on a numerical scale as opposed to categories. Like a bar chart but measures different numbers ex. ACT scores and the frequency in a population Pareto Chart When analyzing speciﬁcs in a category. ex. customers complaining about product quality would be the category and a pareto chart would compare the complaints that only dealt with product quality Time Series Plot Used to identify trends so that a prediction can be made about the data. ex. a time plot of how many airline passengers ﬂy internationally over the last 12 years. There is always a spike during the holidays and therefore we can predict that there will always be a spike during the holidays Box Plot Useful for showing the 5 number summary- minimum, Q1, median, Q3, and maximum Type Visual Why It Is Used and When Pie Chart Used to display percentages of categories and the circle represents the whole and the “pieces of the pie” display the amount in each category Multiple Time Series Plot Plotting several time serious on one chart Multiple Pie Chart Multiple pie charts are used to compare groups of data Bar Chart shows different categories and the amounts in each category. ex. comparing dogs to cats Multiple Box Plot Used when comparing multiple sets of data from individual sources ex. comparing test scores from the ACT from school different school districts Categorial- Comparing categories ex. dogs and cats Quantitative- comparing numerical values ex. test scores Nominal- limited categories but no order Continuous- can measure any variable between 2 numbers( height, there can be decimals) Discrete- data that can only take certain values like students in a class (can’t have half a person) Formula Sheet - S^2- Sample variance - Sample Standard Deviation- sample variance square rooted - Sum of Squared Deviation- (x- mean)^2 (add up the last row on the chart) - Left Skew VS Right Skew- the tail of the graph pulls left VS the tail of the graph pulling right - 1- P(x) is the probability that event x does NOT occur - X —> Z(Z score) —> P(Probability, between 0-1) - Chebyshev’s Rule • Applies to any kind of distribution. • Tells us what percent of the values fall between 1,2, and 3 standard deviations of the mean - Empirical Rule • Used only for normal distributions • Tells us what percent of the values fall between 1,2, and 3 standard deviations of the mean - Example from Class - Aproblem from class where the mu was given as 98.2 and the sigma was given as 0.6 Temperature 98 97.5 97 96.5 98.4 99 Z Score -0.33 -1.1667 -2 -2.833 0.333 1.333 Z= x- mu / sigma Probability of 0.37 0.091 Z-score or more extreme - On the calculator probability of z-score and extreme (above or below)- 2nd, distribution, (lowest value, highest value), enter - Example from Class - What Z score separates the top 10% from the other 90% inACT scores? • Mean = 21 • 1. 2nd, distribution, 3 (invnom) —> 0.9,0,1 = 1.28 • 2. X= mu + Z(sigma) —> 21 + 1.28(4.7) = 27. 06 3. The score that separates the top 10% from the bottom 90% is 27. 06 •
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