class 2

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name: <unnamed>

log: C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\fall_2018_logs\class2.log

log type: text

opened on: 26 Sep 2018, 10:06:31

. use "C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\cps_mar_2000_new.dta", clear

. *class starts here

. ttest yrsed if age>=25 & age<=34, by(sex)

Two-sample t test with equal variances

------------------------------------------------------------------------------

Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

---------+--------------------------------------------------------------------

Male | 9027 13.31212 .0312351 2.967666 13.25089 13.37335

Female | 9511 13.55657 .0292693 2.854472 13.49919 13.61394

---------+--------------------------------------------------------------------

combined | 18538 13.43753 .0213921 2.912627 13.3956 13.47946

---------+--------------------------------------------------------------------

diff | -.2444469 .0427623 -.3282649 -.1606289

------------------------------------------------------------------------------

diff = mean(Male) - mean(Female) t = -5.7164

Ho: diff = 0 degrees of freedom = 18536

Ha: diff < 0 Ha: diff != 0 Ha: diff > 0

Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000

· When we left off last class, we had done a T-test on the difference between men and women’s educational attainment. The t-test came up with a statistic of -5.7, and a P value of 0.0000. What does this mean? Let’s get a more accurate P value.

. display 1- ttail(18536, -5.7164)

5.524e-09

. display ttail(18536, 5.7164)

5.524e-09

. display 2*ttail(18536, 5.7164)

1.105e-08

· This above is the two-tailed P value for this T-test, 1 in 100 million. That means that if men’s and women’s educations were actually the same in the US as a whole, we would have expected to see a difference this big (0.244 years) in a sample this size in either direction once in a hundred million trials. Since that P value is small, we reject the null hypothesis of no difference, and recognize that women in the US had more education than men.

. summarize incwelfr

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

incwelfr | 103226 40.62242 478.8231 0 25000

· What does it mean to say that average welfare income (within the CPS sample) is $40. It means that most people get zero welfare income.

. summarize incwelfr if age>=15 & incwelfr>0 & incwelfr~=.

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

incwelfr | 1289 3253.134 2813.505 1 25000

. summarize incwelfr if age>=15 & incwelfr>0 & incwelfr~=. [fweight= perwt_rounded]

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

incwelfr | 2551246 3072.095 2803.442 1 25000

· For the whole US, there were 2.5 million welfare recipients in 1999, and an average welfare income of $3000 for the year.

. summarize incwelfr if incwelfr>0 & incwelfr~=. [fweight= perwt_rounded]

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

incwelfr | 2551246 3072.095 2803.442 1 25000

. summarize incwelfr if incwelfr>0 [fweight= perwt_rounded]

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

incwelfr | 2551246 3072.095 2803.442 1 25000

. gen byte receives_welfare=0

· Here we go through the exercise of generating a new variable, and attaching value labels and a variable label to it:

. replace receives_welfare=1 if incwelfr>0 & incwelfr~=.

(1289 real changes made)

. tabulate receives_welfare

receives_we |

lfare | Freq. Percent Cum.

------------+-----------------------------------

0 | 132,421 99.04 99.04

1 | 1,289 0.96 100.00

------------+-----------------------------------

Total | 133,710 100.00

. label define receives_welfare_lbl 0 "no welfare" 1 "yes welfare"

. label val receives_welfare receives_welfare_lbl

. tabulate receives_welfare

receives_we |

lfare | Freq. Percent Cum.

------------+-----------------------------------

no welfare | 132,421 99.04 99.04

yes welfare | 1,289 0.96 100.00

------------+-----------------------------------

Total | 133,710 100.00

. label var receives_welfare "does respondent receive welfare"

. tabulate receives_welfare

does |

respondent |

receive |

welfare | Freq. Percent Cum.

------------+-----------------------------------

no welfare | 132,421 99.04 99.04

yes welfare | 1,289 0.96 100.00

------------+-----------------------------------

Total | 133,710 100.00

. sort receives_welfare

. by receives_welfare: summarize yrsed

-----------------------------------------------------------------------------------------------------

-> receives_welfare = no welfare

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

yrsed | 101937 12.79583 3.153618 0 17

-----------------------------------------------------------------------------------------------------

-> receives_welfare = yes welfare

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

yrsed | 1289 10.9903 2.817995 0 17

. table receives_welfare sex [fweight= perwt_rounded] , contents(freq mean age mean yrsed mean incwag

> e) row col

------------------------------------------------------------------------

does |

respondent |

receive | Sex

welfare | Male Female Total

------------+-----------------------------------------------------------

no welfare | 1.34e+08 1.38e+08 2.72e+08

| 34.216377258300781 36.402645111083984 35.327167510986328

| 12.92792 12.90996 12.9187

| 26619.92881 14124.35177 20203.23216

yes welfare | 357,702 2193544 2551246

| 34.846588134765625 32.796371459960938 33.083827972412109

| 10.75763 11.14463 11.09037

| 4196.737659 3577.073717 3663.954806

Total | 1.34e+08 1.40e+08 2.74e+08

| 34.218059539794922 36.346202850341797 35.306285858154297

| 12.92039 12.87497 12.89688

| 26542.14272 13915.27974 20005.84709

------------------------------------------------------------------------

. describe age

storage display value

variable name type format label variable label

-----------------------------------------------------------------------------------------------------

age byte %19.0g agelbl Age

. format age %4.1g

. describe age

storage display value

variable name type format label variable label

-----------------------------------------------------------------------------------------------------

age byte %4.1g agelbl Age

. table receives_welfare sex [fweight= perwt_rounded] , contents(freq mean age mean yrsed mean incwag

> e) row col

---------------------------------------------------

does |

respondent |

receive | Sex

welfare | Male Female Total

------------+--------------------------------------

no welfare | 1.34e+08 1.38e+08 2.72e+08

| 34 36 35

| 12.92792 12.90996 12.9187

| 26619.92881 14124.35177 20203.23216

yes welfare | 357,702 2193544 2551246

| 35 33 33

| 10.75763 11.14463 11.09037

| 4196.737659 3577.073717 3663.954806

Total | 1.34e+08 1.40e+08 2.74e+08

| 34 36 35

| 12.92039 12.87497 12.89688

| 26542.14272 13915.27974 20005.84709

---------------------------------------------------

. format age %4.2g

. table receives_welfare sex [fweight= perwt_rounded] , contents(freq mean age mean yrsed mean incwag

> e) row col

---------------------------------------------------

does |

respondent |

receive | Sex

welfare | Male Female Total

------------+--------------------------------------

no welfare | 1.34e+08 1.38e+08 2.72e+08

| 34 36 35

| 12.92792 12.90996 12.9187

| 26619.92881 14124.35177 20203.23216

yes welfare | 357,702 2193544 2551246

| 35 33 33

| 10.75763 11.14463 11.09037

| 4196.737659 3577.073717 3663.954806

Total | 1.34e+08 1.40e+08 2.74e+08

| 34 36 35

| 12.92039 12.87497 12.89688

| 26542.14272 13915.27974 20005.84709

· Here I was trying to reformat the display format of the age variable so as to get one decimal place, and it took me a few tries…

. format age %6.3g

. table receives_welfare sex [fweight= perwt_rounded] , contents(freq mean age mean yrsed mean incwag

> e) row col

---------------------------------------------------

does |

respondent |

receive | Sex

welfare | Male Female Total

------------+--------------------------------------

no welfare | 1.34e+08 1.38e+08 2.72e+08

| 34.2 36.4 35.3

| 12.92792 12.90996 12.9187

| 26619.92881 14124.35177 20203.23216

yes welfare | 357,702 2193544 2551246

| 34.8 32.8 33.1

| 10.75763 11.14463 11.09037

| 4196.737659 3577.073717 3663.954806

Total | 1.34e+08 1.40e+08 2.74e+08

| 34.2 36.3 35.3

| 12.92039 12.87497 12.89688

| 26542.14272 13915.27974 20005.84709

· The steps for ingesting data: First, unzip the file. Then, change directory, cd command, within stata to the directory that has the asci data file and the do file. Then use the File menu to go and select the do file, and run it.

. cd "C:\Users\mexmi\Documents\current class files\intro soc methods\1995 HW1 data"

C:\Users\mexmi\Documents\current class files\intro soc methods\1995 HW1 data

. clear all

. do "C:\Users\mexmi\Documents\current class files\intro soc methods\1995 HW1 data\cps_00006.do"

. /* Important: you need to put the .dat and .do files in one folder/

> directory and then set the working folder to that folder. */

. set more off

. clear

. infix ///

> int year 1-4 ///

> float perwt 5-12 ///

> byte age 13-14 ///

> byte sex 15 ///

> long inctot 16-21 ///

> using cps_00006.dat

(149642 observations read)

. replace perwt=perwt/100

(149642 real changes made)

. label var year `"Survey year"'

. label var perwt `"Person weight"'

. label var age `"Age"'

. label var sex `"Sex"'

. label var inctot `"Total personal income"'

. label define agelbl 00 `"Under 1 year"'

. label define agelbl 01 `"1"', add

. label define agelbl 02 `"2"', add

. label define agelbl 03 `"3"', add

. label define agelbl 04 `"4"', add

. label define agelbl 05 `"5"', add

. label define agelbl 06 `"6"', add

. label define agelbl 07 `"7"', add

. label define agelbl 08 `"8"', add

. label define agelbl 09 `"9"', add

. label define agelbl 10 `"10"', add

. label define agelbl 11 `"11"', add

. label define agelbl 12 `"12"', add

. label define agelbl 13 `"13"', add

. label define agelbl 14 `"14"', add

. label define agelbl 15 `"15"', add

. label define agelbl 16 `"16"', add

. label define agelbl 17 `"17"', add

. label define agelbl 18 `"18"', add

. label define agelbl 19 `"19"', add

. label define agelbl 20 `"20"', add

. label define agelbl 21 `"21"', add

. label define agelbl 22 `"22"', add

. label define agelbl 23 `"23"', add

. label define agelbl 24 `"24"', add

. label define agelbl 25 `"25"', add

. label define agelbl 26 `"26"', add

. label define agelbl 27 `"27"', add

. label define agelbl 28 `"28"', add

. label define agelbl 29 `"29"', add

. label define agelbl 30 `"30"', add

. label define agelbl 31 `"31"', add

. label define agelbl 32 `"32"', add

. label define agelbl 33 `"33"', add

. label define agelbl 34 `"34"', add

. label define agelbl 35 `"35"', add

. label define agelbl 36 `"36"', add

. label define agelbl 37 `"37"', add

. label define agelbl 38 `"38"', add

. label define agelbl 39 `"39"', add

. label define agelbl 40 `"40"', add

. label define agelbl 41 `"41"', add

. label define agelbl 42 `"42"', add

. label define agelbl 43 `"43"', add

. label define agelbl 44 `"44"', add

. label define agelbl 45 `"45"', add

. label define agelbl 46 `"46"', add

. label define agelbl 47 `"47"', add

. label define agelbl 48 `"48"', add

. label define agelbl 49 `"49"', add

. label define agelbl 50 `"50"', add

. label define agelbl 51 `"51"', add

. label define agelbl 52 `"52"', add

. label define agelbl 53 `"53"', add

. label define agelbl 54 `"54"', add

. label define agelbl 55 `"55"', add

. label define agelbl 56 `"56"', add

. label define agelbl 57 `"57"', add

. label define agelbl 58 `"58"', add

. label define agelbl 59 `"59"', add

. label define agelbl 60 `"60"', add

. label define agelbl 61 `"61"', add

. label define agelbl 62 `"62"', add

. label define agelbl 63 `"63"', add

. label define agelbl 64 `"64"', add

. label define agelbl 65 `"65"', add

. label define agelbl 66 `"66"', add

. label define agelbl 67 `"67"', add

. label define agelbl 68 `"68"', add

. label define agelbl 69 `"69"', add

. label define agelbl 70 `"70"', add

. label define agelbl 71 `"71"', add

. label define agelbl 72 `"72"', add

. label define agelbl 73 `"73"', add

. label define agelbl 74 `"74"', add

. label define agelbl 75 `"75"', add

. label define agelbl 76 `"76"', add

. label define agelbl 77 `"77"', add

. label define agelbl 78 `"78"', add

. label define agelbl 79 `"79"', add

. label define agelbl 80 `"80"', add

. label define agelbl 81 `"81"', add

. label define agelbl 82 `"82"', add

. label define agelbl 83 `"83"', add

. label define agelbl 84 `"84"', add

. label define agelbl 85 `"85"', add

. label define agelbl 86 `"86"', add

. label define agelbl 87 `"87"', add

. label define agelbl 88 `"88"', add

. label define agelbl 89 `"89"', add

. label define agelbl 90 `"90 (90+, 1988-2002)"', add

. label define agelbl 91 `"91"', add

. label define agelbl 92 `"92"', add

. label define agelbl 93 `"93"', add

. label define agelbl 94 `"94"', add

. label define agelbl 95 `"95"', add

. label define agelbl 96 `"96"', add

. label define agelbl 97 `"97"', add

. label define agelbl 98 `"98"', add

. label define agelbl 99 `"99+"', add

. label values age agelbl

. label define sexlbl 1 `"Male"'

. label define sexlbl 2 `"Female"', add

. label values sex sexlbl

end of do-file

. tabulate sex

Sex | Freq. Percent Cum.

------------+-----------------------------------

Male | 71,769 47.96 47.96

Female | 77,873 52.04 100.00

------------+-----------------------------------

Total | 149,642 100.00

. log close

name: <unnamed>

log: C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\fall_2018_logs\class2.log

log type: text

closed on: 26 Sep 2018, 12:41:04

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