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Econ.3640.15.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 Instructor: Sanghoon Lee Lecture 15: Ch.4 Random Variables and Probability Distributions 4.5 The Normal Distribution Eg. 4.17 Assume that the length of time, x, between charges of a cellular phone is normally distributed with a m

Economics.3640.Lecture.11.Exam.2.Solution
School: Utah
Course: Prob&stat Inference
Economics 3640001 2nd Exam Instructor: Sanghoon Lee 1. A pair of dice is tossed. Define the following events: A: {You will roll a 7 (i.e., the sum of the numbers of dots on the upper faces of the two dice is equal to 7)} B: {At least one of the tw

Econ.3640.Test.05.Take.Home.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 5th Test Instructor: Sanghoon Lee 1. Suppose a research neurologist is testing the effect of a drug on response time by injecting 100 rats with a unit dose of the drug, subjecting each to a neurological stimulus, and recording it

E1_Prac_F12
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Practice Questions #1 Fall 2012, Hyeon Kim Practice Questions for the 1st Midterm 1. The first midterm will cover from chapter 1 to chapter 3. 2. This practice questions will be the best material for the exam: All of exam questions will be s

E2_Prac_F12
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Practice Questions #2 Fall 2012, Hyeon Kim Practice Questions for the 2nd Midterm 1. The second midterm will cover from chapter 4 to chapter 5. 2. This practice questions will be the best material for the exam: All of exam questions will be

E3_Prac_F12
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Practice Questions (Final) Fall 2012, Hyeon Kim Practice Questions for the Final 1. The final exam will cover from chapter 6 to chapter 8. 2. This practice questions will be the best material for the exam: All of exam questions will be simil

3640F12s
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Fall 2012 Probability and Statistical Inference for Economists Classroom and Schedule: WEB L114, T. H. 9:10  10:30 am Office Hours: By appointment Instructor: Hyeon Kim Office: OSH #357 Contact Info: epigonos.oikos@gmail.com Course Descript

Lecture_0
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note_Intro Fall 2012, Hyeon Kim Lecture_Intro: What Is Statistics? (Reference: Dennis D. Wackerly, William Mendenhall III and Richard L. Scheaffer, Mathematical Statistics with Applications, sixth edition, Duxbury, 2002) Statistical

Lecture_1
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #1 Fall 2012, Hyeon Kim Lecture #1: Examining Distributions (Chapter 1) Introduction In a study, we collect informationdatafrom individuals. Individuals can be people, animals, plants, or any object of interest. A variable is an

Lecture_2
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #2 Fall 2012, Hyeon Kim Lecture #2: Examining Relationships (Chapter 2) Introduction Most statistical studies involve more than one variable in which we are usually interested in the relationships between those variables. Specif

Lecture_3
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #3 Fall 2012, Hyeon Kim Lecture #3: Producing Data (Chapter 3) Introduction Now we turn to statistical ideas for producing data and the concept of statistical inference: observation and experiment. Observation and experiment Ob

Lecture_4
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #4 Fall 2012, Hyeon Kim Lecture #4: Probability and Sampling Distributions (Chapter 4) 1. Randomness Terminology A phenomenon is random if individual outcomes are uncertain, but there is nonetheless a regular distribution of ou

Lecture_5
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #5 Fall 2012, Hyeon Kim Lecture Note #5: Probability Theory (Chapter 5) 1. General probability rules Reminder of general rules for probability (from Chapter 4) 1. 0 P ( A) 1 ; 2. P ( S ) 1; S=sample space 3. Complement rule: For

Lecture_6
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #6 Fall 2012, Hyeon Kim Lecture Note #6 Introduction to Inference (Chapter 6) 1. Introduction Note that Methods for drawing conclusions about a population from sample data are called statistical inference. Which Methods? Confi

Lecture_7
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #7 Fall 2012, Hyeon Kim Lecture Note #7 Inference for Distributions (Chapter 7) 1. Inference for the mean of a population Both confidence intervals and tests of significance for the mean of a Normal population are based on the s

Economics.3640.Lecture.34.Exam.6
School: Utah
Course: Prob&stat Inference
Economics 3640001 6th Exam True or False? If false, explain why. Instructor: Sanghoon Lee 1. Suppose that we have a Simple Linear Regression Model: y = 0 + 1x + We assume that the variance of the probability distribution of the random error is co

Economics.3640.Lecture.29
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 29 * Reading Assignment: Ch.9 Simple Linear Regression (9.1 & 9.3) Instructor: Sanghoon Lee So far, we have studied:  Basic principles (describing data, probability, random variable, probability distribution)  Methods f

Economics.3640.Syllabus
School: Utah
Course: Prob&stat Inference
Economics 3640001 Probability and Statistical Inference for Economists MWF 11:50AM 12:40PM / FAMB 203 Instructor Name: Sanghoon Lee Email: slee@economics.utah.edu Office: BUC #7 Office Hours: After class or by appointment * Please send an email to

Econ.3640.23.Fall.2006
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 23 9.8 Using the Model for Estimation and Prediction Instructor: Sanghoon Lee The first is the use of the model for estimating the mean value of y, E(y), for a specific value of x. The second use of the model entails pred

Economics.3640.Lecture.04
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 4: Review Instructor: Sanghoon Lee * Answer to Q9 has been corrected. * The test questions come from the Lecture Notes 1, 2, 3, and the questions below. * Be sure to show how you got the answer, i.e. do not just show the

Econ.3640.14.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 Instructor: Sanghoon Lee Lecture 14: Ch.4 Random Variables and Probability Distributions 4.4 Probability Distribution for Continuous Random Variables The curve above, a function of x, is denoted by f(x) and is called a probabili

Economics.3640.Lecture.08
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 8 * Reading Assignment: Ch.3 Probability (3.5 ~ 3.6) 3.5 Conditional Probability Instructor: Sanghoon Lee The probability of observing an even number (event A) on a toss of a die is 1/2. But suppose we're given the inform

Econ.3640.06.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 6: Ch.3 Probability 3.1 Events, Sample Spaces, and Probability Eg. 3.1 Two coins are tossed, and their up faces are recorded. Instructor: Sanghoon Lee Def. 3.1 An experiment is an act or process of observation that leads

Econ.3640.35.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 35: Ch.9 Simple Linear Regression 9.6 The Coefficient of Correlation Instructor: Sanghoon Lee Def. 9.2 The coefficient correlation, r, is a measure of the strength of the linear relationship between two variables x and y.

Econ.3640.30.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 30: Ch.7 Comparing Population Means Instructor: Sanghoon Lee 7.1 Comparing Two Population Means: Independent Sampling (LargeSample)  Confidence interval for the difference between two population means, (1 2).  Assumin

Econ.3640.14.Fall.2006
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 14 5.3 SmallSample Confidence Interval for a Population Mean Instructor: Sanghoon Lee The interpretation of the smallsample confidence interval is exactly the same as the largesample confidence interval. The only chang

Econ.3640.18.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 Instructor: Sanghoon Lee Lecture 18: Ch.4 Random Variables and Probability Distributions 4.8 Sampling Distribution Def. 4.10 A parameter is a numerical descriptive measure of a population. Because it is based on the observations

Econ.3640.10.Fall.2006
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 10 4.8 Sampling Distribution Instructor: Sanghoon Lee Def. 4.10) A parameter is a numerical descriptive measure of a population. Because it is based on the observations in the population, its value is almost always unknow

Economics.3640.Lecture.14
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 14 Instructor: Sanghoon Lee * Reading Assignment: Ch.4 Random Variables and Probability Distributions (4.5) 4.5 The Normal Distribution One of the most commonly observed continuous random variables is a bellshaped probab

Economics.3640.Lecture.30
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 30 * Reading Assignment: Ch.9 Simple Linear Regression (9.2) 9.2 Fitting the Model: The Least Squares Approach Plot the sample data in a scattergram. If you see a general tendency for y to increase (or decrease) as x increa

Econ.3640.10.Spring.2007
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 10: Ch.3 Probability  Mutually exclusive events are dependent events. Ex. Instructor: Sanghoon Lee Ex. Ex. 3.107 Two events, A and B, are independent, with P(A) = .3 and P(B) = .1. a. Are A and B mutually exclusive? Why

Economics.3640.Lecture.13
School: Utah
Course: Prob&stat Inference
Economics 3640001 Lecture 13 Instructor: Sanghoon Lee * Reading Assignment: Ch.4 Random Variables and Probability Distributions (4.3) Ex. 4.16 Toss three fair coins and let x equal the number of heads observed. a. Identify the sample points associ

Lecture_8
School: Utah
Course: Prob&Stat Inference
Econ 3640001 Lecture Note #8 Fall 2012, Hyeon Kim Lecture Note #8 Inference for Proportions (Chapter 8) 1. Inference for a single proportion Many studies collect data on categorical variables, such as race or occupation of a person, the make of a car, et