Stat II Jargon  
Central Limit Theorem,  Type I and Type II error, P-value, Biased estimation, mean 
squared error.

Ch. 5:
Bivariate data, Define error in regression,
Problem of common response, Problem of confounding,
What criterion is used in regression  (Hint: least squares
minimizes error sum of squares)
Slope of a regression, Intercept of a regression,
What are the limits in which a simple correlation 
coefficient must lie?  ANS(-1 to 1)

What test should one use for the overall regression model?

What test for individual regression coefficients?
What is R-square and Adjusted R-square?


What is the command for regression of y on x1 and x2?
ANS:  lm(y~x1+x2)

Give two R commands, which run above regression and print the main regression 
results.
reg1=lm(y~x1+x2)
summary(reg1)


In the above command how to ensure that the regression line does go through the 
origin  (no intercept)?
ANS:  lm(y~x1+x2-1)

What is the R command to compute the correlation coefficient between x1 and x2 
and test it? (ANS: cor.test(x1,x2)  )

What is the R command to test the null that the mean of y is zero?
(ANS:  t.test(y)  )

What is the R command for analysis of variance?
(ANS:  anova(lm(y~X1+x2))  )

What is the R package needed to do basic descriptive statistics conveniently?
(ANS:  fBasics)

What are the R commands for computing the mean and standard deviation of y?
mean(y)
sd(y)

What are the R commands for: 
1) Reading data  ANS  read.table(file="filename")
2) Running regressions including multiple regressions  ANS:  lm(y~X1+x2)
3) Descriptive stats  (which package do you use and how?) basicStats(y)
5) Plots and plot headings  plot(x,y, main="Heading")
6) Confidence intervals  ANS: confint(lm(y~X1+x2))
7) Analysis of variance. ANS:  anova(lm(y~X1+x2)) 
8) Finding the residuals of a regression ANS:  resid(lm(y~X1+x2)) 
9) Printing the results of regression to the screen.ANS:  summary(lm(y~X1+x2)) 
10) Computing the portion of test stats which comes from z, t and F tables. qnorm, 
qt, qf
11) Computing the portion of p-value statistics which comes from z, t and F 
tables.pnorm, pt, fp

#Finding CRITICAL VALUES from t table and F table using qt and qf
alp=0.05 #the alpha value
alpby2=alp/2 #defines alpha by 2 for typical t test
dft=length(y)-3 #defines degrees of freedom for t
qt(alpby2,df=dft)#quantile of t distribution 

qf(alp, df1=2, df2=dft, lower.tail=F)#quantile of F distribution


#Finding p-values from t table and F table using pt and pf
#pvalues are already given by R
#for example t value in summary table is -2.306 for the pie-sales example
pt(-2.306,df=dft, lower.tail=T)*2
# we multiply tail area by 2 to get the p value= 0.03976325
#doubling is needed because t test is two-sided


Part 2 of the final will have 3 questions
1) Use excel to do anova  worth 2%
2) Given a payoff table, find maximax, maximin, minimax regret, max expected value and minimum expected regret    worth 3%
3) 6 questions from jargon  worth 3%
see 
http://www.fordham.edu/economics/vinod/jargon2.txt 
file at my website for possible list for the third question.