2-4 Measures of Center
Day 1
Objective: Finding four types of measures of center
1) Mean
2) Median
3) Mode
4) Midrange
Measure of Center: Value at the center or middle of a data.
Mean: Average of all
NAME: _ PER. _ DATE: _
STATISTICS
REVIEW TEST-CHAPTER 2
1. When running a business would you want the variation to be large or
small, meaning would you want your clients to have similar views or would
2-3 Pictures of Data
Histogram: A bar graph in which the horizontal scale represents classes and the vertical
scale represents frequencies. The heights of the bars correspond to frequency values, and
2-2 Summarizing Data with Frequency Tables
Frequency table - Lists classes (or categories) of values, along with frequencies (or
counts) of the number of values that fall into each class.
Ex:
Lower cl
Objective:
2-5 Measures of Variation(Spread)
1) Discuss why we look at the spread of data when observing distributions
of data.
2) Finding the range, standard deviation, and variance of data.
3) Discu
2-7 Exploratory Data Analysis (EDA)
Exploratory Data Analysis: The process of using statistical tools to investigate data
sets in order to understand their important characteristics.
Outlier:
1) An ou
2-6 Measures of Position
Standard score (z score): The number of standard deviations that a given value is above
or below the mean.
Formula for standard score of sample and population:
Sample:
Populat
Warmup Problem 2-6
7) An industrial psychologist for the Citation Corporation develops two different tests to
measure job satisfaction. Which score is better: a score of 72 on the management test,
whi
2-5 Warmup Problem
1) Ages of Presidents: Given below are the ages of the U.S. Presidents when they were
inaugurated.
57
61
57
57
58
57
61
54
68
51
49
64
50
48
65
52
56
46
54
49
51
47
55
55
54
42
51
5
Warmup Problem for 2-4
Find the mean, median, mode, and midrange of the given data:
32, 37, 36, 51, 53, 33, 61, 35, 45, 55, 39
a) mean:
b) median:
c) mode:
d) midrange:
Find the mean of the frequency
2-3 Warm-Up Problem
Name: _
Ex: On a math test the scores of 20 students were as follows
61, 75, 83, 84, 76, 94, 62, 53, 98, 90 , 62, 79, 81, 56, 97, 66, 65, 58, 73, 92
a) Construct a frequency table
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
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
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 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
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
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
2.2 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
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
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.
3.2 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
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
3.3 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
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
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
4.1 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
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
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: