Course Notes.

• Table of Contents.

• Intro and Chapter 1.

• Chapter 2 Part I.

• Chapter 2 Part II.

• Chapter 3.

• Chapter 4.

• Chapter 5.

• Chapter 6.

• Chapter 7.

• Chapter 8.

• Chapter 9.

• Chapter 10.

Assignments.

• Final Project description.

• Assignment 1. (Due 09/06/12)

• Assignment 2. (Due 09/11/12)

• Assignment 3. (Due 09/20/12)

• Assignment 4. (Due 09/25/12)

• Assignment 5. (Due 10/02/12)

• Assignment 6. (Due 10/11/12)

• Assignment 7. (Due 10/16/12)

• Assignment 8. (Due 11/08/12)

• Assignment 9. (Due 11/15/12)

• Assignment 10. (Due 11/20/12)

• Assignment 11. (Due 11/29/12)

• Assignment 12.

R Code. (page numbers in parentheses correspond to course notes).

• File `confintt.r' needed as source for t-based confidence intervals in R.

• SRS for Figure 1 (31).

• Data set for SRS for Figure 1.

• SRS for Figure 2 (33).

• Data set for SRS for Figure 2.

• SRS for Figure 3 (40).

• Data set for SRS for Figure 3.

• Stratified SRS for Figure 5a (50).

• Data set for Stratified SRS for Figure 5a.

• Stratified SRS for Figure 5b (52).

• Data set for Stratified SRS for Figure 5b.

• Data set for Stratified SRS (proportion) for Figure 6 (61).

• R code for bootstrapping the mean -- symmetry present (77).

• R code for bootstrapping the mean -- skewness present (79).

• R code for bootstrapping Figure 1 data (SRS) (86).

• R code for bootstrapping Figure 2 data (SRS) (88).

• R code for bootstrapping Figure 5a data (stratified SRS) (90).

• R code for bootstrapping Figure 5b data (stratified SRS) (92).

• R code for bootstrapping the correlation coefficient (95).

• R code for ratio estimation) (105).

• R code for bootstrapping ratio estimation) (107).

• R code for domain estimation (116).

• Regression estimation for the alligator data set (Case 1) (125).

• Regression estimation for the alligator data set (Case 2) (126-127).

• Regression estimation for the alligator data set (Case 3) (127-128).

• Regression estimation for the alligator data set (Case 4) (129).

• Regression estimation with known N and t_x (Linear regression) (133).

• R code for one-stage cluster sample -- Figure 7 (153).

• R code for one-stage cluster sample -- Figure 8b (154).

• R code for systematic sample -- Figure 9a (161).

• R code for systematic sample -- Figure 9b (162).

• R code for unequal-sized clusters -- Figure 10 (171).

• R code for Hansen-Hurwitz estimation for unequal-sized cluster sample -- Figure 12 (174).

• R code for Hansen-Hurwitz estimation for unequal-sized cluster sample -- proportion estimation (178).

SAS Code (page numbers in parentheses correspond to course notes).

• SRS for Figure 1, Figure 2, and Figure 3 (35-36,41).

• SRS for Figure 5a (53).

• SRS for Figure 5b (54).

• SRS for Figure 6 (62).

• Ratio estimation for agsrs.dat data set (105-106).

• The agsrs.dat data set.

• The agsrs.csv data set (comma-separated Excel format.

• Regression estimation for the alligator data set (Case 1) (126).

• Regression estimation for the alligator data set (Case 4) (128).

• Regression estimation with known N and t_x (Linear regression) (130).

• Domain estimation for agsrs.dat data set (Case I) (xxx).

• Domain estimation for agsrs.dat data set (Case II) (xxx).

• SAS code for one-stage cluster sample -- Figure 7 (155).

• SAS code for one-stage cluster sample -- Figure 8b (156).

• SAS code for systematic sample -- Figure 9a (162).

• SAS code for systematic sample -- Figure 9b (163).

• SAS code for unequal-sized cluster sample -- Figure 10 (173).

• SAS code for Hansen-Hurwitz estimation for unequal-sized cluster sample -- Figure 12 (175).

• SAS code for estimation of population proportion for unequal-sized cluster sample (179).