13.1 Intro
Objective: Reviewing categorical data
So far in inferencing we have been working with quantitative data. In the next chapter
will be doing inferencing on quantitative datas nemesis which is hold the
suspense categorical data.
Categorical Data:
13.2 Day 2
2 test of independence
Objective: Using the
Ex: A survey consisting of 114 randomly selected students was gathered to determine if
there was an association between handedness and eye color
Eye Color
Handedness
Brown
Blue
Green
Other
Totals
Lef
13.2 Day 1
2 test of homogeneity
Objective: Using the
Ex: Medical researches enlisted 90 subjects for an experiment comparing treatments for
depression. The subjects were randomly divided into three groups and given pills to take
for a period of three mo
13.2 Day 3
Objective: Understand the difference between the three different 2 tests:
1) 2 of goodness of fit: A test of whether the distribution of counts in one categorical
variable matches the distribution predicted by the model. It has n-1 degrees of f
2.1 Density curves and Normal Distributions
Objective: Discuss density curves
So far we have learned how to describe quantitive variables by
1) Graphs2) Overall pattern3) Center4) SpreadNew Step:
Sometimes a shape of a larger # of observations is so regul
2.1 Normal Curve and Standard Deviation
Normal Curve:
Normal Distribution:
Inflection Point:
Normal distribution with mean _ and S.D. _ will be seen written as
68-95-99.7 Rule
If the N(,):
Ex: The distribution of grades in the Stats classes is approximate
2.2 Day 1
Standard Normal Calculations
Standardized Values (z-scores)-
Active Stats 6-3 (Normal Model Wrench)
Standard Normal Distribution:
a) Former NBA superstar Michael Jordan is 78 in. tall, and former WNBA basketball
player Rebecca Lobo is 76 in. tal
3.1 Day 2 Interpreting Scatterplots
Adding categorical variables to scatterplots
Warmup: Active Stats 7-2 Scatter Plot Axes
How do you interpret a scatterplot?
1) Direction
2) Form
3) Strength
Lastly look at the outliers
Graph our pulse rates using a scat
Chapter 3
3.1 Scatterplots
Introduction: In this chapter we will concentrate on relationships among several
variables for the same group of individuals
Response Variable: Measures an outcome of study.
Explanatory Variable: Attempts to explain the observed
3.2 Correlation
Review: When describing an association there is four objectives(not including the
outlier(s) discussion) to be covered. They are _, _,
_, and _.
Intro: Active Stats 7-3 Correlation Video with the r sign, play the 1st half of the film.
Corr
3.2 Correlation Day 2
Straightening Scatterplots
Intro:
Some camera lenses have an adjustable aperture, the hole that lets the light in. The size of
the aperture is expressed in a mysterious number called the f/stop. Each increase of one
f/stop number cor
3.3 Day 2
R - The Variation Accounted For
2
Objective: Interpreting the variation of a model
Variation in the residuals (r2) is the key to assessing how well a model fits.
Meaning if r =1 and the model predicted exercise pulse perfectly, the residuals wou
3.3 Day 3
Residual Plots
Outliers and Influential Observations in Regression
Residual Plot: Plots the residuals on the vertical axis against the explanatory variable on
the horizontal axis.
Ex:
Residuals from least-squares regression have a special proper
4.3 Simpsons Paradox
Simpson Paradox:
1) Moe argues that hes the better pilot of the two, since he managed to land 83% of his
last 120 flights on time compared with Jills 78%. But lets look at the data more closely.
Here are the results for each of their
4.3 Day 1
Relations in Categorical Data
2-Way Table: Describes relationship between 2 categorical variables.
Marginal Distribution: Right and bottom margins of a two-way table
Find the marginal distribution of the survival and classes in counts of the Tit
4.1 Day 2
Objective: More re-expressing to find models that fit nonlinear data.
Planet Distances and Years: The table below shows the average distance of each of
the nine planes from the sun, and the length
Question 13 from Bock Packet:
a) The scatterplot
4.1 Modeling Nonlinear Data
Objective: Find a regression model that fits to nonlinear data
Review on Logs:
a) Log(AB)=
b) Log bx =
c) Log 2x =
d) Log 10x=
Ex: Some camera lenses have an adjustable aperture, the hole that lets the light
in. The size of the
5.3 Day 1
Simulations
Ex: Suppose a cereal manufacturer put pictures of famous athletes on cards in boxes of
cereal in the hope of boosting sales. The manufacture announces that 20% of the boxes
contain a picture of Tiger Woods, 30% a picture of Lance Arm
AP Stats
5.2 Designing Experiments
Observational Study: Observe and measure specific characteristics, but we dont
attempt to modify the subjects being studied. Not given any treatment
Ex:
Experiment: Apply some treatment and then proceed to observe its ef
5.2 Day 2
The Four Principles of Experimental Design
1) Control
2) Randomize
3) Replicate
4) Block
Example: An ad for Optigro plant fertilizer claims that with this product you will grow
juicer, tastier tomatoes. Youd like to test this claim, and wonder w
6.1 Day 1
Randomness
Objective: Understanding randomness and intro to probability
Ex: Red/Green Light
Random is not a synonym for haphazard but a description of a kind
_
Ex: When we flip a coin we the probability of getting heads is .5 mean heads occurs
h
7.2 Means and Variance of Random Variables
What does probability do for us?
Ex: An insurance company offers a death and disability policy that pays $10,000 when
you die or $5000 if you are permantely disabled. It charges a premium of only $50 a year
for t
Name: _
A.P. STATISTICS Quiz 7.1
_
Total: 41pts
Date:_
Use the following data for questions 1-5.
The probabilities that a customer selects 1,2,3,4, or 5 items at a convenience store are
0.33, 0.14, 0.23, 0.17, and 0.13 respectively.
1) Construct a probabi
7.1 Day 2: Continuous Random Variable
Continuous Random Variable: Take all values in some interval of numbers. Has
infinitely many values, and those values can be associated with measurements
on a continuous scale in such a way that there are no gaps or i
Chapter 7
Random Variables
7.1 Discrete and Continuous Random Variables
Random Variable: Variable whose value is a numerical outcome of a random
phenomenon.
Discrete Random Variable: Either a finite number of values or a countable number of
values, where