Page 339, Table 9.1
use "A:table91.dta", clear
rename col1 plant
rename col2 downtime
sort plant
by plant: list downtime
_______________________________________________________________________________
-> plant = 1
+----------+
| downtime |
|----------|
1. | 5 |
2. | 7 |
3. | 9 |
4. | 0 |
5. | 11 |
|----------|
6. | 2 |
7. | 8 |
8. | 4 |
9. | 3 |
10. | 5 |
+----------+
_______________________________________________________________________________
-> plant = 2
+----------+
| downtime |
|----------|
1. | 4 |
2. | 3 |
3. | 7 |
4. | 2 |
5. | 11 |
|----------|
6. | 0 |
7. | 1 |
8. | 9 |
9. | 4 |
10. | 3 |
|----------|
11. | 2 |
12. | 1 |
13. | 5 |
+----------+
_______________________________________________________________________________
-> plant = 3
+----------+
| downtime |
|----------|
1. | 5 |
2. | 6 |
3. | 4 |
4. | 11 |
5. | 12 |
|----------|
6. | 0 |
7. | 1 |
8. | 8 |
9. | 4 |
+----------+
_______________________________________________________________________________
-> plant = 4
+----------+
| downtime |
|----------|
1. | 6 |
2. | 4 |
3. | 0 |
4. | 1 |
5. | 0 |
|----------|
6. | 9 |
7. | 8 |
8. | 4 |
9. | 6 |
10. | 10 |
+----------+
_______________________________________________________________________________
-> plant = 5
+----------+
| downtime |
|----------|
1. | 11 |
2. | 4 |
3. | 3 |
4. | 1 |
5. | 0 |
|----------|
6. | 2 |
7. | 8 |
8. | 6 |
9. | 5 |
10. | 3 |
+----------+
_______________________________________________________________________________
-> plant = 6
+----------+
| downtime |
|----------|
1. | 12 |
2. | 11 |
3. | 3 |
4. | 4 |
5. | 2 |
|----------|
6. | 0 |
7. | 0 |
8. | 1 |
9. | 4 |
10. | 3 |
|----------|
11. | 2 |
12. | 4 |
+----------+
_______________________________________________________________________________
-> plant = 7
+----------+
| downtime |
|----------|
1. | 3 |
2. | 7 |
3. | 6 |
4. | 7 |
5. | 8 |
|----------|
6. | 4 |
7. | 3 |
8. | 2 |
+----------+
_______________________________________________________________________________
-> plant = 8
+----------+
| downtime |
|----------|
1. | 3 |
2. | 6 |
3. | 4 |
4. | 3 |
5. | 2 |
|----------|
6. | 2 |
7. | 8 |
8. | 4 |
9. | 0 |
10. | 4 |
|----------|
11. | 5 |
12. | 6 |
13. | 3 |
+----------+
_______________________________________________________________________________
-> plant = 9
+----------+
| downtime |
|----------|
1. | 6 |
2. | 4 |
3. | 7 |
4. | 3 |
5. | 9 |
|----------|
6. | 1 |
7. | 4 |
8. | 5 |
+----------+
_______________________________________________________________________________
-> plant = 10
+----------+
| downtime |
|----------|
1. | 6 |
2. | 7 |
3. | 5 |
4. | 10 |
5. | 11 |
|----------|
6. | 2 |
7. | 1 |
8. | 4 |
9. | 0 |
10. | 5 |
|----------|
11. | 4 |
+----------+
by plant: tabstat downtime, s(mean var)
_______________________________________________________________________________
-> plant = 1
variable | mean variance
-------------+--------------------
downtime | 5.4 11.37778
----------------------------------
_______________________________________________________________________________
-> plant = 2
variable | mean variance
-------------+--------------------
downtime | 4 10.66667
----------------------------------
_______________________________________________________________________________
-> plant = 3
variable | mean variance
-------------+--------------------
downtime | 5.666667 16.75
----------------------------------
_______________________________________________________________________________
-> plant = 4
variable | mean variance
-------------+--------------------
downtime | 4.8 13.28889
----------------------------------
_______________________________________________________________________________
-> plant = 5
variable | mean variance
-------------+--------------------
downtime | 4.3 11.12222
----------------------------------
_______________________________________________________________________________
-> plant = 6
variable | mean variance
-------------+--------------------
downtime | 3.833333 14.87879
----------------------------------
_______________________________________________________________________________
-> plant = 7
variable | mean variance
-------------+--------------------
downtime | 5 5.142857
----------------------------------
_______________________________________________________________________________
-> plant = 8
variable | mean variance
-------------+--------------------
downtime | 3.846154 4.307692
----------------------------------
_______________________________________________________________________________
-> plant = 9
variable | mean variance
-------------+--------------------
downtime | 4.875 6.125
----------------------------------
_______________________________________________________________________________
-> plant = 10
variable | mean variance
-------------+--------------------
downtime | 5 11.8
----------------------------------
_______________________________________________________________________________
Page 340
NOTE: Some of the data needed for the calculations below need to be entered by hand, or you can merge the table9.1 and table9.2 datasets.
use "A:page340.dta", clear
bysort plant: gen num = _n
reshape wide hours, i(plant) j(num)
(note: j = 1 2 3 4 5 6 7 8 9 10 11 12 13)
Data long -> wide
-----------------------------------------------------------------------------
Number of obs. 104 -> 10
Number of variables 7 -> 18
j variable (13 values) num -> (dropped)
xij variables:
hours -> hours1 hours2 ... hours13
-----------------------------------------------------------------------------
egen y_bar = rowmean( hours1 hours2 hours3 hours4 hours5 hours6 hours7 hours8 hours9 hours10 hours11 hours12 hours13)
egen sd = rowsd( hours1 hours2 hours3 hours4 hours5 hours6 hours7 hours8 hours9 hours10 hours11 hours12 hours13)
gen miyi = M*y_bar
tabstat m y_bar miyi, s(n mean p50 sd)
stats | m y_bar miyi
---------+------------------------------
N | 10 10 10
mean | 10.4 4.672115 240.0179
p50 | 10 4.8375 242.1231
sd | 1.837873 .6469981 27.71871
----------------------------------------
Page 341-342
use "A:page340.dta", clear
gen p1=nplant/10
gen p2=M/m
gen pwt=p1*p2
svyset plant [pweight=pwt], fpc(nplant) vce(linearized) || _n, fpc(nmachine)
pweight: pwt
VCE: linearized
Strata 1: <one>
SU 1: plant
FPC 1: nplant
Strata 2: <one>
SU 2: <observations>
FPC 2: nmachine
svy: mean hours
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 1 Number of obs = 104
Number of PSUs = 10 Population size = 4698
Design df = 9
--------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
hours | 4.598045 .2268273 4.084926 5.111164
--------------------------------------------------------------
svy: total hours
(running total on estimation sample)
Survey: Total estimation
Number of strata = 1 Number of obs = 104
Number of PSUs = 10 Population size = 4698
Design df = 9
--------------------------------------------------------------
| Linearized
| Total Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
hours | 21601.62 894.421 19578.29 23624.94
--------------------------------------------------------------
NOTE: The estimate of the mean given by Stata (and other statistics packages) is somewhat different from that shown in the text. The difference is in the way the mean is calculated. In the text, they say that the total number of elements in the population is 4500, so they divide the total by that to get the 4.8. In Stata, the total number of elements in the population is estimated (by summing the probability weights) to be 4698, so Stata divides the total by 4698 to get 4.598. Although it may seem that Stata’s estimate of the mean is less precise than that given in the text, remember that both numbers are merely estimates of the true population mean, which is unknown. If a different sample was drawn, you would get a different estimate of the mean both using the textbook’s method and from Stata. If you know the total number of elements in the population, you can do the division yourself so that only one number (the total) is an estimate.
Page 345
use "A:table92.dta", clear rename col1 plant rename col2 Mi rename col3 mi rename col4 pi
Page 347
gen Mipi = Mi*pigen diff = Mipi - .34*Mi gen within = Mi*(Mi-mi)*pi*(1 - pi)/(mi - 1) list
+------------------------------------------------------+
| plant Mi mi pi Mipi diff within |
|------------------------------------------------------|
1. | 1 50 10 .4 20 3 53.33333 |
2. | 2 65 13 .38 24.7 2.599999 66.36066 |
3. | 3 45 9 .22 9.9 -5.400001 34.749 |
4. | 4 48 10 .3 14.4 -1.919999 42.56 |
5. | 5 52 10 .5 26 8.32 60.66667 |
|------------------------------------------------------|
6. | 6 58 12 .25 14.5 -5.22 45.47727 |
7. | 7 42 8 .38 15.96 1.68 48.0624 |
8. | 8 66 13 .31 20.46 -1.979999 62.35185 |
9. | 9 40 8 .25 10 -3.6 34.28571 |
10. | 10 56 11 .36 20.16 1.12 58.0608 |
+------------------------------------------------------+
tabstat Mi mi Mipi diff, s(n mean median sd)
stats | Mi mi Mipi diff
---------+----------------------------------------
N | 10 10 10 10
mean | 52.2 10.4 17.608 -.14
p50 | 51 10 17.98 -.3999998
sd | 9.003703 1.837873 5.588203 4.292608
--------------------------------------------------
tabstat Mipi Mi, s(sum)
stats | Mipi Mi
---------+--------------------
sum | 176.08 522
------------------------------
di 176.08/522 .33731801