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 n
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 ca
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 Vid
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.
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
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 f
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 dis
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:
Some
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 distr
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
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
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 mode
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:
Residu
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 dat
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 con
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.
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
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
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, tasti
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:
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
conta
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 ape
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
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 distri
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