Lecture
1
Administrative issues
Introduction to the course
Beginning of descriptive statistics
Data set used in class
Lecture
2
Descriptive Statistics of one variable
Data sets used in class
Lecture 3
Summary measures for one variable
Box Plots
Normal distribution and empirical rule
Data sets used in class
Sat.jmp from class 2
Frbsubst.jmp from class 2
Survey.jmp from class 1
Lecture 4
Discussion of the normal distribution as connection between summary measures and overall behavior.
Normal quantile plots
Looking at time-series data (stocks)
Begin discussion of Y vs X
Data sets used in class
Sat.jmp and Frbsubst.jmp from class 2
Rent.jmp (if time permits)
Lecture 5
Discussion of bivariate relationships
Relationship between a continuous and nominal variable
Relationship between two continuous variables (begin discussion).
1. Overall measures
2. Summary measures (covariance and correlation).
Data sets used in class
Rent.jmp (from Lecture 4)
Lecture 6
Beginning of discussion of measuring the relationship between Y (Continuous) vs X(Continuous)
Correlation and regression
Data sets used in class
Rent.jmp (from Lecture 4)
Sat.jmp (from Lecture 2)
Lecture 7
Summary measures including:
a) Correlation
b) The best line and connection with a)
c) Prediction
d) Residual plot
Data sets used in class
Sales.jmp and Mileage.jmp (from Lecture 6)
Survey.jmp (from Lecture 1)
Sat.jmp (from Lecture 2)
Lecture 8
Prediction in regression
Regression diagnostics
Categorical variables
Causality
Data sets used in class
Capm.jmp (from Lecture 7)
Miles.jmp (from Lecture 6)
Lecture 9
Relationships between nominal variables
Causality and Simpson’s paradox
Beginnings of probability
Data sets used in class
Election.jmp (from Lecture 8)
Salary-level.jmp (from Lecture 8)
Lecture
10
Begin discussion of probability
Lecture
11
Continue
with probability—Rules, Conditioning and
Lecture
12
Various rules involving updating of probabilities with applications
Begin discussing random variables
Lecture
13
Characterizing random variables
Summarizing random variables
Rules for random variables
Lecture
14
Families of discrete random variables
Continuous random variables
Lecture
15
Continuous review variables
Lecture
16
Bivariate Random Variables
Lecture
17
Portfolio problem with practical application
Lecture
18
Go over review problem for bivariate
Simulation for Stocks
Begin Central Limit Theorem
We will use the program:
Lecture
19
Central Limit Theorem: Discussion and Applications
Use for Binomial
Lecture 20
Review the Central Limit Theorem
Quality Control – will use the Shaft Example from Lecture 19
Sampling issues.
Lecture
21
Sampling - Assumptions
Introduction to Statistical Inference
Confidence intervals
We will use data sets such as GM92.jmp from Lecture 4.
Lecture 22
Confidence intervals for means and proportions
We will use data sets from previous classes and
New Data Sets
Lecture
23
Finish off confidence intervals
Data sets from Class 22
Introduce hypothesis testing
Lecture
24
One sample hypothesis tests
Discussion of ideas
Illustrate ideas for the mean with known variance
Discuss power
Data sets
Lecture 25
Data Sets
Tires from Lecture 24
Lecture 26
One sample Hypothesis Tests with unknown variance
Applications of One Sample Tests
Test for Medians
Test of Paired Data
Data Sets
Problem Sets
Data sets needed
Data sets needed
Data set
Exams