Review of Econ424

Fall 2007

– open book

– understand the concepts

– use them in real examples

– Dec. 14, 8am-12pm, Plant Sciences 1129

– Vote• Option 1(2) Option 2(3) Option 3 (4)

• Decision: Option 3 for the Excel Part, which means Do not turn in Excel file, the hard copy will ask more detailed questions to incorporate step-to-step calculation

Format of Final Exam

Course evaluation

• Course Evaluation

www.courseevalum.umd.edu

• Teaching theater evaluation

www.oit.umd.edu/tt/st_fdbck.htm

Concepts to grasp (1)

• Population / sample• Population

– Cdf (prob(var<x))– Pdf (first derivative of cdf)– population mean, population std. dev.

• Sample– Histogram, quartiles, percentiles, sample mean, sample std. dev.

• Population sample – Central limit theorem xbar~N(µ, σ/sqrt(n))

• Sample Population – Xbar is a proxy of µ with noise

Concepts to grasp (2)

• Inference– Type I error, Type II error

– Confidence level α

– Confidence interval

– Hypothesis testing• H0

• H1

• Accept/reject?

• One-tail, two-tail test

Technical stuff

• Excel – midterm review• SAS – notes, old exams

Summary of Excel (1)

• Basic excel – open, save and close files

– cut, paste and paste special

– change format for cell, row or columns

– sort data by one or two variables

– chart wizard

– freeze panes

– drag cells

– use excel functions

Summary of Excel (2)

• Data description – mean, median, trimmed mean

– standard deviation, variance

– quartiles

– mode, skewness, kurtosis

– histogram (absolute frequency)

– relative frequency polygon

Summary of Excel (3)

• Probability theory – PDF, CDF

– mean and standard deviation

– bernoulli, binomial

– uniform, normal

– how to simulate them in Excel?

– Central limit theorem

– how to see central limit theorem in excel?

Summary of Excel (4)

• Estimation and Hypothesis testing– use sample mean to estimate population mean

– confidence interval

– type I error and type II error

– null hypothesis (H0) and alternative hypothesis (H1)

– one-tail vs. two-tail

– t-statistics, critical value, p-value

– one-sample test

– two-sample test (independent)

– two-sample test (matched pair)

Summary of Excel (5)

• Linear regression– model

• one variable on the right hand side

• more than one variables on the right hand side

• create and use binary variables

– fit of the model• R square

• F test

• scatter plot

• correlation coefficient

– coefficient estimates• point estimate

• hypothesis testing

• omitted variable bias

Summary of SAS (1)

• Why do we need Excel and SAS? – What is the advantage of SAS?

– What is the advantage of Excel?

• .sas, .log, .lst– How to edit, save, and run .sas in your machine? What

commands need change?

– How to generate and read .log in your machine?

– How to generate and read .lst in your machine?

– How to define library? What does “work” library mean?

– How to find and use datasets in your library?• Data newdata; set mydata; …; run;

• Proc … data=mydata; ..; run;

Summary of SAS (2)

• How to generate summary statistics in SAS?– Proc means (for the full sample, or by groups?)

– Proc univariate

– Proc means with output written in a data file

– Proc freq

– Proc chart

– Proc plot

• How to conduct mean comparison?– Two groups

– More than two groups

Summary of SAS (3)

• How to run and read regressions in SAS? – Proc reg

– Proc glm

– Regressions with fixed effects?

– Compare different regressions?

Final words

• Warning #1:– “Now I can use fancy and sophisticated

statistics everywhere!”

– Excel and SAS are tools that may be useful for your research question. Their usage should be driven by your research question, not the other way around.

Final words

• Warning #2:– “Now I am a master of statistics!”– Materials taught in this class are at most a

starting point for future learning and application of statistics.

– Be aware of the limitations of basic statistics. For example, a typical OLS regression requires a set of strong assumptions. Every time when you apply an OLS regression, think hard why you choose to run the regression in this way.

Final words

• Is economic statistics an art or a science?– there might be multiple interpretations for a

simple statistics. Be aware of how the numbers are created and what assumptions have been made between the pure numbers and their economic meanings.

– Some answers are definitely wrong, especially those that jump to the conclusion!