Lecture 3
Section 1.3
Experiments and Observational Studies
Association versus Causation
Confounding Variables
Observational Studies vs. Experiments
Randomized Experiments
Association and Causation
T
Section 5.1: Normal Distribution: Finding proportions and
probabilities
Density Curve
A density curve is a theoretical model to describe a
variables distribution
Think of a density curve as an ideal
STEP 1:
Quantitative variables are defined as variable that are measured on a numerical scale, or have a
numerical value to them.
Qualitative variables are defined as variables that have no numerical
Section 2.5
Two Quantitative
Variables:
Scatterplot and
Correlation
Statistics: Unlocking the Power of
Lock5
Outline
Two quantitative variables
Visualization:
Summary
scatterplot
statistic: correlat
Descriptive Statistics
Part II
Statistics: Unlocking the Power of Data
Lock5
Section 2.3
One Quantitative
Variable:
Measures of
Spread/ Variation &
Location
Statistics: Unlocking the Power of Data
Lo
Section 5.1: Normal Distribution: Finding Endpoints/Values
Recall: Parameters of a Normal
Two Features distinguish one normal density from another:
THe mena is the center of symmetry
The standard d
Section 11.1: Probability Rules:
Event
An event is something that either happens or doesnt happen,
or something that either is true or is not true.
Examples:
A randomly selected card is heart
The
Section 6.1: Distribution of a Sample Proportion (p-hat)
Standard Error for P-Hat
The standard error for P-Hat is
(1-/n
The larger the sample size, the smaller the standard
error
CLT for P-Hat
If
Chapter 1: The Three Rules of Epidemics
Gladwell asserts that most trends, styles, and phenomena are born and spread according to
routes of transmission and conveyance that are strikingly similar. In
Lecture 5
Section 2.3
One Quantitative Variable: Measures of Spread/Variation and
Location
Outline:
One Quantitative Variable:
Standard Deviation (measure of variation)
Measures of Location
z-scor
Data Collection
Branches of Statistics
Descriptive Statistics: organizing displaying and
summarizing data.
Examples: Mean, Median, Variance ,Pie Chart, Bar Chart,
etc
Inferential Statistics: using
Data Structures
Data
Cases and Variables
Categorical and Quantitative variables
explanatory and response variables
data to answer questions
Why Stats?
Stats is all about data
Collecting data
descri
Lecture 6
Section 2.4
Outliers, Boxplots, and Quantitative/Categorical Relationships
Outline
One Quantitative Variable (continued)
Formal Rule for outliers (IQR method for detection)
Boxplots
One
Lecture 7
Section 2.5
Two Quantitative Variables
Visualization: scatterplot
summary statistic: correlation coefficient
Direct Association
A positive association means that values of one variable
te
Lecture 4
Section 2.1
Descriptive Statistics Part 1
One Categorical, two categorical, and one quantitative variables.
One Categorical Variable
Summary Stats: frequency table, proportion
Visualizatio
Section 2.6: two Quantitative Variables: Simple Linear
Regression
Equation of the line:
= a + bx or = intercept + slope * x
= predicted response
x is the explanatory variable
Prediction
The regressio
Lecture 10: 11.3
Random Variables and Probability Functions
Random Variable (r.v.)
A random variable is a numeric quantity that changes from
trial to trial in a random process
Examples
X = number o
Lecture 6.4: Distribution of a Sample Mean (x-bar)
SE for X bar
The standard error for x bar is:
SE= /n
SEE LECTURE FOR EXAMPLES
CLT for X
If n
bar
is sufficiently large:
xbar ~N(/n)
If the populat
Section 1.3
Experiments and
Observational Studies
Association versus Causation
Confounding Variables
Observational Studies vs Experiments
Randomized Experiments
Statistics: Un