WooldridgeIE_Chapter2_Exercises
Ha Cam Van
12/10/2017
Exercises
C1
setwd("/Users/vancam/Documents/WAIKATO-Thesis/Rworking/Wooldridge/StataFile")
library(foreign)
library(tidyverse)## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statsir401k <- read.dta("401K.DTA")## Warning in read.dta("401K.DTA"): cannot read factor labels from Stata 5
## files#1. Average participation rate and the average match rate
summary(ir401k$prate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 78.02 95.70 87.36 100.00 100.00summary(ir401k$mrate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0100 0.3000 0.4600 0.7315 0.8300 4.9100#2. Estimate the simple regression equation
ir401kreg <- lm(data = ir401k, prate~mrate)
summary(ir401kreg)##
## Call:
## lm(formula = prate ~ mrate, data = ir401k)
##
## Residuals:
## Min 1Q Median 3Q Max
## -82.303 -8.184 5.178 12.712 16.807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.0755 0.5633 147.48 <2e-16 ***
## mrate 5.8611 0.5270 11.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.09 on 1532 degrees of freedom
## Multiple R-squared: 0.0747, Adjusted R-squared: 0.0741
## F-statistic: 123.7 on 1 and 1532 DF, p-value: < 2.2e-16#3. Verbal explaination
#4. Pridicted "prate" when "mrate" = 3.5
83.0755 + 5.6*3.5## [1] 102.6755#5. How much of the variation in "prate" is explained by "mrate": Rsquare = 0.0747C2
ceoal2 <- read.dta("CEOSAL2.DTA")## Warning in read.dta("CEOSAL2.DTA"): cannot read factor labels from Stata 5
## files#1. Average salary and tenure
summary(ceoal2$salary)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 100.0 471.0 707.0 865.9 1119.0 5299.0summary(ceoal2$ceoten)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.000 6.000 7.955 11.000 37.000#2. How many CEOs at the first year as CEO (ceoten=0)
sum(ceoal2$ceoten==0, na.rm = TRUE)## [1] 5# Max value of "ceoten" is 37 (in the previous code)
#3. Estimation
ceoreg <- lm(data = ceoal2, log(salary)~ceoten)
summary(ceoreg)##
## Call:
## lm(formula = log(salary) ~ ceoten, data = ceoal2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.15314 -0.38319 -0.02251 0.44439 1.94337
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.505498 0.067991 95.682 <2e-16 ***
## ceoten 0.009724 0.006364 1.528 0.128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6038 on 175 degrees of freedom
## Multiple R-squared: 0.01316, Adjusted R-squared: 0.007523
## F-statistic: 2.334 on 1 and 175 DF, p-value: 0.1284# One more year as CEO raise approximate 0.0097*100 = 0.97% in predicted percentage of salaryC3
sleep75 <- read.dta("SLEEP75.DTA")## Warning in read.dta("SLEEP75.DTA"): cannot read factor labels from Stata 5
## files#1. Regression
irfanlovessleeping_reg <- lm(data=sleep75, sleep~totwrk)
summary(irfanlovessleeping_reg)##
## Call:
## lm(formula = sleep ~ totwrk, data = sleep75)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2429.94 -240.25 4.91 250.53 1339.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3586.37695 38.91243 92.165 <2e-16 ***
## totwrk -0.15075 0.01674 -9.005 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 421.1 on 704 degrees of freedom
## Multiple R-squared: 0.1033, Adjusted R-squared: 0.102
## F-statistic: 81.09 on 1 and 704 DF, p-value: < 2.2e-16#2. Verbal explaination
#3. If "totwrk" - total work increses by 2 hours, then "sleep" is estimated to fall: 2*0.15 = 0.3 C4
wage2 <- read.dta("WAGE2.DTA")## Warning in read.dta("WAGE2.DTA"): cannot read factor labels from Stata 5
## files#1. Average IQ
summary(wage2$IQ) ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50.0 92.0 102.0 101.3 112.0 145.0# Average IQ in the sample is 102.0
sd(wage2$IQ)## [1] 15.05264#Standard deviation of IQ is 15.05
#2. Estimation
wage2reg <- lm(data = wage2, wage~IQ)
summary(wage2reg)##
## Call:
## lm(formula = wage ~ IQ, data = wage2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -898.7 -256.5 -47.3 201.1 2072.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 116.9916 85.6415 1.366 0.172
## IQ 8.3031 0.8364 9.927 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 384.8 on 933 degrees of freedom
## Multiple R-squared: 0.09554, Adjusted R-squared: 0.09457
## F-statistic: 98.55 on 1 and 933 DF, p-value: < 2.2e-16# An increase in IQ of 15 points will lead to an incsrease in predicted wage of 124.5
8.30*15## [1] 124.5# R-square = 0.095 => IQ can explain around 9.5% change in wage
#3.
wage2reg1 <- lm(data = wage2, log(wage)~IQ)
summary(wage2reg1)##
## Call:
## lm(formula = log(wage) ~ IQ, data = wage2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.09324 -0.25547 0.02261 0.27544 1.21486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8869943 0.0890206 66.13 <2e-16 ***
## IQ 0.0088072 0.0008694 10.13 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3999 on 933 degrees of freedom
## Multiple R-squared: 0.09909, Adjusted R-squared: 0.09813
## F-statistic: 102.6 on 1 and 933 DF, p-value: < 2.2e-16# An increase in IQ of 15 points will lead to estimated of 13.2% change in wage
(15*0.0088)*100 ## [1] 13.2C5
rdchem <- read.dta("RDCHEM.DTA")## Warning in read.dta("RDCHEM.DTA"): cannot read factor labels from Stata 5
## files#2. Regression
rdchemreg <- lm(data = rdchem, rd~sales)
summary(rdchemreg)##
## Call:
## lm(formula = rd ~ sales, data = rdchem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -184.65 -37.60 -10.92 0.02 333.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.577217 20.515489 -0.028 0.978
## sales 0.040626 0.002449 16.591 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 103.5 on 30 degrees of freedom
## Multiple R-squared: 0.9017, Adjusted R-squared: 0.8985
## F-statistic: 275.3 on 1 and 30 DF, p-value: < 2.2e-16#Verbal explainationC6
meap93 <- read.dta("MEAP93.DTA")## Warning in read.dta("MEAP93.DTA"): cannot read factor labels from Stata 5
## files#3. Estimation
meap93reg <- lm(data = meap93, math10~log(expend))
summary(meap93reg)##
## Call:
## lm(formula = math10 ~ log(expend), data = meap93)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.343 -7.100 -0.914 6.148 39.093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -69.341 26.530 -2.614 0.009290 **
## log(expend) 11.164 3.169 3.523 0.000475 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.35 on 406 degrees of freedom
## Multiple R-squared: 0.02966, Adjusted R-squared: 0.02727
## F-statistic: 12.41 on 1 and 406 DF, p-value: 0.0004752#4. If spending increase by 10% will lead to 1.116 point increase in math10??
0.1*11.164## [1] 1.1164# log-log form
meap93reg2 <- lm(data = meap93, log(math10)~log(expend))
summary(meap93reg2)##
## Call:
## lm(formula = log(math10) ~ log(expend), data = meap93)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.38927 -0.24846 0.06635 0.33480 1.08064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4319 1.2735 0.339 0.7347
## log(expend) 0.3158 0.1521 2.076 0.0385 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4968 on 406 degrees of freedom
## Multiple R-squared: 0.01051, Adjusted R-squared: 0.008069
## F-statistic: 4.311 on 1 and 406 DF, p-value: 0.0385#5. Irfan C7
charity <- read.dta("CHARITY.DTA")
summary(charity$gift)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 7.444 10.000 250.000#1 Average gift is 7.44
sum(charity$gift==0, na.rm = TRUE)## [1] 2561# 2561/4268 = 60 percent of people give no gift
2561/4268## [1] 0.6000469#2. Average of mailings
summary(charity$mailsyear)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.25 1.75 2.00 2.05 2.50 3.50#3. Estimation
charityreg <- lm(data = charity, gift~mailsyear)
summary(charityreg)##
## Call:
## lm(formula = gift ~ mailsyear, data = charity)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.287 -7.976 -5.976 2.687 245.999
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0141 0.7395 2.724 0.00648 **
## mailsyear 2.6495 0.3431 7.723 1.4e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.96 on 4266 degrees of freedom
## Multiple R-squared: 0.01379, Adjusted R-squared: 0.01356
## F-statistic: 59.65 on 1 and 4266 DF, p-value: 1.404e-14#4.5. Verbal explaination
##Extral work
library(ggplot2)
ggplot(charity, aes(gift, mailsyear))+geom_point()+geom_smooth(method = "lm")
C8 - A little challenging
#1.Generate xi
#Number of obs to create
nobs <- 500
min <- 0
max <- 10
xi <- runif(nobs, min = 0, max = 10)
#Mean and median of xi
summary(xi)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01751 3.19800 5.63000 5.34200 7.51700 9.96500#Standard deviation of xi
sd(xi)## [1] 2.704125#2. Generate ui
ui <- rnorm(nobs, mean = 5.015, sd = 2.8859)
summary(ui)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.289 3.194 4.935 5.083 6.956 14.240ui## [1] 2.38981143 4.48561958 7.38560864 7.17436688 7.87905298
## [6] 4.89201449 7.93730075 6.32043468 6.86316244 6.39207581
## [11] 9.14020667 6.73723200 3.80999050 8.76974142 10.77211229
## [16] 4.64302330 8.14013182 3.85494679 3.76782922 2.26803906
## [21] 5.18518652 0.56354784 -0.48448623 5.64415860 4.46421591
## [26] 3.63706666 6.33458740 3.64407956 3.76678462 4.65794585
## [31] 5.37753634 3.22939337 3.82679222 4.00739960 1.34159881
## [36] 3.61441148 6.84196899 2.41589983 5.11180358 5.87994084
## [41] 3.90430416 1.72536835 9.12847145 4.12917794 3.90082692
## [46] -1.02361170 3.57895293 5.08851693 5.81528666 4.85879914
## [51] 7.40960538 3.03786538 4.43716176 6.11265931 5.21066350
## [56] 6.17449277 3.75260218 3.32874288 0.56389257 5.17960662
## [61] 7.99537885 5.14726546 7.11772561 7.15288841 5.22491190
## [66] 1.34865590 7.55631350 3.55239538 7.08354667 3.79797618
## [71] 3.94893200 10.74464210 4.10385212 3.58169403 2.85343223
## [76] 5.95288703 2.06044403 2.23259638 1.89720990 1.63688342
## [81] 4.31914924 8.22936632 -1.04197808 3.58824061 3.01066535
## [86] 0.21366790 8.06408271 6.70728555 0.66338889 9.02180931
## [91] 4.44773349 6.04986249 8.80041332 0.19319742 5.95290142
## [96] 7.45147765 6.28840257 10.36641978 6.86739084 4.58667052
## [101] 5.65505854 1.68041789 13.31370635 5.72695820 4.78404929
## [106] 6.10814327 -0.73527833 5.34844456 4.92474260 6.32514797
## [111] 1.09480502 8.53751531 7.38592358 5.15903713 10.73189894
## [116] 2.56391294 0.37955101 4.34929667 4.17097883 7.34253163
## [121] 4.14226142 4.89518560 3.52765492 3.69794048 9.41643243
## [126] 4.82401220 4.43199710 6.30169901 2.85320011 4.34256358
## [131] 2.82106819 7.27486493 5.59821353 3.78653211 7.09759311
## [136] 6.75987555 -2.20348078 4.74425368 6.11544351 2.71871743
## [141] 4.89859201 2.82571271 3.71537619 7.40206836 7.35339833
## [146] 7.85352743 6.50520517 4.45220585 6.40375045 7.87983156
## [151] 4.08888193 6.12021397 5.91136621 3.22264909 4.98582667
## [156] 2.42412777 1.83361482 6.47858250 5.42863449 11.47303131
## [161] 1.82440516 9.10593285 6.19213120 3.66258474 5.80760299
## [166] 8.96582002 2.79622337 1.95918163 6.09008073 4.56060699
## [171] -3.28881186 8.72102502 3.41157935 3.00064155 8.56797037
## [176] 5.87709043 4.58919037 7.19520060 5.92070293 4.58421969
## [181] 4.66349728 4.80402481 8.98970825 6.41143962 10.16216459
## [186] 6.55178311 6.25281256 -1.60375547 4.41352524 8.25113698
## [191] 2.71373874 6.64917607 7.60113349 5.47080973 5.78675296
## [196] 2.10884479 8.72858817 7.24691836 6.33962679 6.02422974
## [201] 11.86022468 2.24811893 0.82706790 9.16265548 2.39039493
## [206] 8.42720406 3.27373871 9.20406599 6.50171933 4.75585751
## [211] 5.43816807 0.93311019 5.19557630 1.03065288 6.97188731
## [216] 12.55997047 3.89384803 7.13073287 14.24027915 3.12836136
## [221] 2.68411253 6.64401012 6.00462525 1.31791594 4.94162569
## [226] 6.84761800 8.22555167 6.69974903 5.46577507 10.16981120
## [231] 5.42160260 9.41414903 0.86420232 8.60869357 2.19719394
## [236] 0.46030719 5.19110625 3.55389620 1.97165023 3.42489732
## [241] 2.16704521 1.71000077 2.13840438 6.15411828 6.77937509
## [246] 3.19418570 8.03173178 4.20008277 3.31284959 10.07954292
## [251] 8.35208802 4.11474931 8.49800283 9.99729022 4.88314349
## [256] 10.32510482 6.73654528 8.29469842 8.74179985 6.57748742
## [261] 6.18739518 5.16857838 2.68453950 5.18774918 3.48763321
## [266] 8.05033307 6.71163644 1.61884840 10.26818192 3.80081122
## [271] 8.49208065 11.54970536 -0.71054951 4.56756230 -0.72000226
## [276] 6.48033123 5.26160244 7.08176530 1.11889987 7.02853309
## [281] 5.07148617 7.95661950 3.17705336 1.33153215 1.62696374
## [286] 12.66919808 5.46900762 3.20737879 4.83600236 5.32818435
## [291] 4.81925205 5.87996247 9.00383841 3.77982269 3.26728871
## [296] 4.37780571 6.99375718 -1.51946993 10.48330788 4.14905814
## [301] -0.07533938 4.72302222 4.48645838 5.97851991 -0.03295655
## [306] 3.55485089 9.70684391 2.86785040 2.44790600 8.68181536
## [311] 6.03929225 6.66258037 1.62409700 3.06492993 5.73927725
## [316] 5.03960295 4.72049607 6.10942716 0.33566980 4.09022329
## [321] 6.36110517 5.13463164 11.97606979 2.26948282 3.19386301
## [326] 3.89993026 4.12591651 -0.44341556 1.44996982 4.38784180
## [331] 4.76417671 4.79134674 6.59804642 5.51104454 3.62871700
## [336] 7.53912599 0.69480438 8.57044211 4.42135880 3.94592555
## [341] 7.47874602 11.18133544 2.49011064 1.72714606 6.82546075
## [346] 8.17444478 2.39094687 5.12348745 11.19212226 5.37505307
## [351] 7.25431967 0.25687369 1.67593967 7.55982294 0.51801885
## [356] 0.22123562 7.11692437 -0.21536214 5.02436485 9.54353530
## [361] 3.31343357 4.38677104 4.92809803 6.91002278 2.41253350
## [366] 2.06255118 6.45939548 7.73524237 5.64457002 0.85705531
## [371] 4.45799030 8.80318116 6.53321366 1.28147326 0.62015711
## [376] 5.12717975 7.71087491 7.72111392 7.68088944 2.72013573
## [381] 6.28465779 8.81704504 8.47265795 -1.69149584 4.89423290
## [386] 4.39706747 6.83131149 6.95134545 2.80444319 5.38165805
## [391] 1.86379239 10.61072905 7.86813587 3.65532996 4.07141511
## [396] 3.81269285 7.79005549 6.20840529 4.92072386 3.57621950
## [401] 2.92392928 7.66207430 6.98147283 8.86875884 6.85721548
## [406] 2.31787792 8.65178831 9.04749420 8.48346510 4.01909923
## [411] 8.87582279 1.66076297 1.83727941 3.62779615 3.98397374
## [416] 1.75794470 4.19450761 2.21756834 6.83002671 5.20422920
## [421] 3.18594785 2.68984587 4.55257857 5.82090354 -2.17966198
## [426] 2.06215628 9.97381522 8.52237882 2.96779865 2.05340285
## [431] 1.99530293 -0.76132902 4.49344462 7.71139749 2.82932277
## [436] 6.57157486 3.11449005 5.84017004 6.05331428 3.39853036
## [441] 2.20115571 5.10777154 7.25716102 2.83590599 6.92410641
## [446] -0.90571310 5.20722633 6.76870375 6.74729311 4.36167099
## [451] 3.67430188 8.99010143 6.01711043 4.67295857 8.51210857
## [456] 9.27833104 3.65618433 7.33901217 5.39891901 2.54275789
## [461] 8.19730758 12.14626995 5.47564725 2.43677613 5.56777143
## [466] 4.05418944 8.65353991 3.62819725 6.92887155 5.45062686
## [471] 0.81997247 3.16276717 4.17955802 3.10766468 2.56237674
## [476] 8.16068874 2.21311540 4.33317649 3.72426867 4.28771998
## [481] 4.30424413 4.45921088 5.88648223 6.02321593 4.73404702
## [486] 7.26786071 7.00321164 2.92558558 8.57306224 4.56292248
## [491] 7.00257475 1.31815774 3.42783691 11.36637866 3.14687745
## [496] 5.48552117 4.01283354 8.82187646 0.49692646 0.99507220#3. Generate y
vandf <- data.frame(xi, ui)
vandf <- mutate(vandf, yi = 1+2*vandf$xi+vandf$ui)
# Regression
vandfreg <- lm(data = vandf, yi~xi)
summary(vandfreg)##
## Call:
## lm(formula = yi ~ xi, data = vandf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.4151 -1.8743 -0.1128 1.8663 9.1690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.35345 0.28202 22.53 <2e-16 ***
## xi 1.94945 0.04711 41.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.846 on 498 degrees of freedom
## Multiple R-squared: 0.7747, Adjusted R-squared: 0.7743
## F-statistic: 1712 on 1 and 498 DF, p-value: < 2.2e-16#4. Fitted value of u
vandfreg$residuals## 1 2 3 4 5
## -2.784734560 -0.694703575 2.132533284 1.935842970 2.969208767
## 6 7 8 9 10
## -0.110958787 2.991032122 1.224422573 1.706884021 1.082366931
## 11 12 13 14 15
## 4.114975658 1.623253894 -1.179248428 3.729798031 5.618284524
## 16 17 18 19 20
## -0.445002118 3.141042542 -1.356631536 -1.184410316 -2.767445559
## 21 22 23 24 25
## 0.117371358 -4.357597892 -5.583268606 0.556174702 -0.669753123
## 26 27 28 29 30
## -1.384868553 1.024063356 -1.463468346 -1.284055860 -0.514935392
## 31 32 33 34 35
## 0.471775772 -1.663004405 -1.075824191 -0.881188818 -3.944706191
## 36 37 38 39 40
## -1.523567067 1.805199284 -2.571217340 0.013930598 0.898636307
## 41 42 43 44 45
## -1.210871787 -3.599657803 3.848908170 -0.931908747 -1.322802549
## 46 47 48 49 50
## -6.072949886 -1.312915905 -0.210558797 0.532910504 -0.096490733
## 51 52 53 54 55
## 2.148117816 -2.215850148 -0.697363336 0.842030409 0.273177723
## 56 57 58 59 60
## 1.062296733 -1.202804288 -1.873472108 -4.325098684 0.153614744
## 61 62 63 64 65
## 3.142225907 0.003563479 2.102275264 2.021219491 0.145090559
## 66 67 68 69 70
## -3.514339177 2.343012474 -1.411910424 1.854460957 -1.314568647
## 71 72 73 74 75
## -0.939487074 5.885664199 -1.219847771 -1.313354513 -2.151969124
## 76 77 78 79 80
## 0.791290587 -3.121210640 -3.004373298 -3.190303354 -3.476577205
## 81 82 83 84 85
## -0.783366221 3.359544100 -5.891672721 -1.727314072 -2.175708102
## 86 87 88 89 90
## -4.792950820 3.171441817 1.425795973 -4.590143179 3.831799869
## 91 92 93 94 95
## -0.483532985 0.721634305 3.845671018 -4.814405609 0.752593552
## 96 97 98 99 100
## 2.360541141 1.285810449 5.387191352 1.986600384 -0.570422295
## 101 102 103 104 105
## 0.640814059 -3.337311652 8.326056444 0.409921337 -0.276851366
## 106 107 108 109 110
## 1.185045621 -5.829207094 0.390023177 -0.114734408 1.188309489
## 111 112 113 114 115
## -4.228975601 3.650375132 2.412544641 -0.125353150 5.781886280
## 116 117 118 119 120
## -2.562922480 -4.538381249 -0.804473210 -0.969498987 2.154165360
## 121 122 123 124 125
## -1.171658790 -0.273444810 -1.537343911 -1.316167389 4.097246497
## 126 127 128 129 130
## -0.084449385 -0.554547869 1.143174558 -2.339292358 -0.684786196
## 131 132 133 134 135
## -2.406129452 2.305486936 0.363293007 -1.289061188 2.194803044
## 136 137 138 139 140
## 1.791165035 -7.256631471 -0.320224767 0.844898127 -2.255338921
## 141 142 143 144 145
## -0.314883026 -2.291115498 -1.327537700 2.538430291 2.283539279
## 146 147 148 149 150
## 2.808662617 1.476660958 -0.637017258 1.321574894 2.752654967
## 151 152 153 154 155
## -1.157715648 1.018412317 0.896534167 -2.098967225 0.006710064
## 156 157 158 159 160
## -2.590612919 -3.048274780 1.352221680 0.375883197 6.178197880
## 161 162 163 164 165
## -3.130240092 3.800732743 1.004551248 -1.396180651 0.763342775
## 166 167 168 169 170
## 3.829371333 -2.171251340 -3.242340163 1.185840511 -0.463584620
## 171 172 173 174 175
## -8.415122448 3.570162750 -1.750549701 -2.344615170 3.594462961
## 176 177 178 179 180
## 0.686116820 -0.402445583 2.155775066 0.707321364 -0.360057551
## 181 182 183 184 185
## -0.570504366 -0.125528533 4.058842511 1.508268551 4.906265782
## 186 187 188 189 190
## 1.699715795 1.106591750 -6.931057291 -0.548403278 2.917193597
## 191 192 193 194 195
## -2.590973735 1.781873541 2.428264896 0.556687470 0.637803423
## 196 197 198 199 200
## -2.874713099 3.849821221 2.091136927 1.400906571 1.112104782
## 201 202 203 204 205
## 6.537362579 -2.951714346 -4.279265459 4.285336455 -2.757107464
## 206 207 208 209 210
## 3.368649601 -1.846555748 4.173161158 1.235755725 -0.316491336
## 211 212 213 214 215
## 0.363380853 -4.252613420 -0.135257857 -4.022687995 1.904972060
## 216 217 218 219 220
## 7.652656010 -1.274244121 2.074063418 9.169019047 -1.934145172
## 221 222 223 224 225
## -2.319949633 1.365468455 1.042453330 -3.712178833 -0.084976057
## 226 227 228 229 230
## 1.647729177 3.089523255 1.780658690 0.394027308 4.933813385
## 231 232 233 234 235
## 0.460465171 4.310554779 -4.113742183 3.429741796 -3.041909106
## 236 237 238 239 240
## -4.690696061 0.258970287 -1.529965217 -3.222070026 -1.923868235
## 241 242 243 244 245
## -2.941451941 -3.165470804 -2.774271629 0.938895729 1.857619141
## 246 247 248 249 250
## -2.007400729 2.725079461 -1.070103950 -1.703686673 5.103762777
## 251 252 253 254 255
## 3.082068040 -0.965152175 3.379347065 5.067451501 -0.184008081
## 256 257 258 259 260
## 5.369493988 1.641654607 3.204172452 3.886521157 1.487465398
## 261 262 263 264 265
## 1.004309857 -0.014088677 -2.661948045 0.188827645 -1.366841660
## 266 267 268 269 270
## 2.843005455 1.378591980 -3.443922361 4.918434243 -1.460814617
## 271 272 273 274 275
## 3.515415677 6.588334069 -5.602162401 -0.475013311 -5.731101582
## 276 277 278 279 280
## 1.407983512 0.355374556 2.216413399 -3.803411950 1.858345585
## 281 282 283 284 285
## 0.092581229 2.997084601 -1.876742250 -3.541915711 -3.398068704
## 286 287 288 289 290
## 7.585706596 0.552884468 -1.869426123 -0.230051617 0.250655190
## 291 292 293 294 295
## -0.433307436 0.864887352 3.952771899 -1.409956594 -1.588521289
## 296 297 298 299 300
## -0.973488872 1.792321718 -6.408597088 5.463797038 -0.746949903
## 301 302 303 304 305
## -4.988527611 -0.420653574 -0.480438796 0.843229250 -4.899169324
## 306 307 308 309 310
## -1.569163141 4.682766334 -2.238020987 -2.784393790 3.618656591
## 311 312 313 314 315
## 1.139127990 1.312929235 -3.252812068 -1.976697953 0.447648046
## 316 317 318 319 320
## 0.115141607 -0.598652417 0.863926618 -4.624948153 -0.844588112
## 321 322 323 324 325
## 1.373385659 0.171963436 7.024237787 -3.028923205 -1.943890484
## 326 327 328 329 330
## -0.985075540 -0.857542851 -5.642514289 -3.547610831 -0.484234991
## 331 332 333 334 335
## -0.519151704 -0.415761485 1.675718353 0.597851438 -1.421992897
## 336 337 338 339 340
## 2.483195269 -4.222024270 3.294324866 -0.511105979 -0.965984908
## 341 342 343 344 345
## 2.457793480 6.326516942 -2.501294505 -3.310690976 1.531471043
## 346 347 348 349 350
## 2.977700761 -2.545534417 0.239872531 5.953195840 0.261894253
## 351 352 353 354 355
## 2.062794108 -4.815624925 -3.576097878 2.332837565 -4.784452292
## 356 357 358 359 360
## -4.936908741 1.811225621 -5.073075457 0.003044742 4.556000742
## 361 362 363 364 365
## -1.737118959 -0.643409345 -0.290968445 1.890120616 -2.870495976
## 366 367 368 369 370
## -3.231555006 1.483415260 2.454042168 0.405488572 -4.297463037
## 371 372 373 374 375
## -0.715090065 3.818093165 1.246894983 -3.667119992 -4.604919727
## 376 377 378 379 380
## -0.158209301 2.364554096 2.515791003 2.632743405 -2.269415400
## 381 382 383 384 385
## 1.080621714 3.961993100 3.449460463 -6.772605172 -0.136475120
## 386 387 388 389 390
## -0.559662677 1.785156314 2.046118393 -2.226338735 0.399280196
## 391 392 393 394 395
## -3.085177184 5.338055720 2.822435081 -1.424685115 -0.836625596
## 396 397 398 399 400
## -1.107749931 2.663327380 1.269813468 0.062877385 -1.582126991
## 401 402 403 404 405
## -2.287093297 2.474756045 1.942031988 3.767340424 1.772231651
## 406 407 408 409 410
## -2.603969890 3.345016951 3.754155617 3.354053646 -1.200190106
## 411 412 413 414 415
## 3.819398895 -3.635547827 -3.074131889 -1.417352347 -1.203069018
## 416 417 418 419 420
## -3.500971335 -0.868120738 -2.781356257 1.784778332 0.238133417
## 421 422 423 424 425
## -1.694818621 -2.335979253 -0.526739695 0.576251068 -7.161797913
## 426 427 428 429 430
## -3.158286908 4.687880895 3.410178236 -1.969128247 -3.058863845
## 431 432 433 434 435
## -3.226197908 -5.862861235 -0.841329708 2.454590200 -2.181356293
## 436 437 438 439 440
## 1.308307458 -1.791440603 0.677034606 0.707384422 -1.722545629
## 441 442 443 444 445
## -2.846445154 0.133527962 2.167527671 -2.430901127 1.678080744
## 446 447 448 449 450
## -6.213656175 -0.054854487 1.707731176 1.675284489 -0.605588360
## 451 452 453 454 455
## -1.539932347 4.100110738 1.156308816 -0.251346522 3.173670304
## 456 457 458 459 460
## 4.112513396 -1.448707123 2.062911001 0.262604524 -2.391814494
## 461 462 463 464 465
## 3.154881996 7.243168739 0.428005260 -2.810148974 0.547051564
## 466 467 468 469 470
## -1.050349282 3.386929909 -1.334499117 1.943961019 0.479432449
## 471 472 473 474 475
## -4.279145542 -1.854157406 -0.919843472 -1.884636716 -2.589569318
## 476 477 478 479 480
## 2.813816201 -2.975497294 -0.595068202 -1.280471112 -0.841812602
## 481 482 483 484 485
## -0.683983794 -0.466712976 0.831284931 1.016352840 -0.124632181
## 486 487 488 489 490
## 2.177389605 1.969048665 -2.141552514 3.663819155 -0.632626339
## 491 492 493 494 495
## 1.916238772 -3.658993717 -1.527116030 6.048168436 -2.021880195
## 496 497 498 499 500
## 0.432991973 -0.873903198 3.703062616 -4.855639178 -4.042288117#6. Repeat
set.seed(9)
nobs <- 500
min <- 0
max <- 10
xi <- runif(nobs, min = 0, max = 10)
summary(xi)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.03091 2.45000 4.93100 5.03200 7.53600 9.98600sd(xi)## [1] 2.900968ui <- rnorm(nobs, mean = 5.032, sd = 2.900968)
irfdf <- data.frame(xi, ui)
irfdf <- mutate(irfdf, yi = 1+2*vandf$xi+vandf$ui)
irfdfreg <- lm(data = irfdf, yi~xi)
summary(irfdfreg)##
## Call:
## lm(formula = yi ~ xi, data = irfdf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.3493 -4.4450 0.1924 4.2267 15.1785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.05642 0.53705 31.760 <2e-16 ***
## xi -0.05727 0.09248 -0.619 0.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.993 on 498 degrees of freedom
## Multiple R-squared: 0.0007695, Adjusted R-squared: -0.001237
## F-statistic: 0.3835 on 1 and 498 DF, p-value: 0.536ui## [1] 4.0497941589 5.9701128689 5.0440790685 8.9924580152 1.4765662431
## [6] 4.1369234700 5.3626145018 9.0545583777 4.8312414054 8.8524084961
## [11] 0.4193871725 4.3393893709 4.3170556660 8.3322821678 7.0091801354
## [16] 3.8651750645 0.0333425570 2.4864097950 4.9404063312 3.7485226446
## [21] 8.4126820974 1.1632409392 3.1895997138 6.4411433578 8.1716881890
## [26] 6.2097042140 2.0128064174 9.7318722407 4.7679946294 -2.0049512228
## [31] 5.3537027711 6.5616663898 7.0392337151 3.9282022240 6.3712345884
## [36] 6.4986242018 7.9909127594 5.7567899613 8.4525124859 8.2397063648
## [41] 6.7975711156 2.8824350386 10.3873487225 8.3194735323 1.4641354582
## [46] 8.6443559907 9.3119315076 4.0782516886 3.9093372662 1.5102352161
## [51] 7.3238971050 5.2847539875 10.4745412342 3.2949279307 -0.3026311201
## [56] 2.9070231031 10.3269109902 6.7261847079 -0.7825266217 3.9274917883
## [61] 9.5725490053 -0.3065862235 -0.6190675170 3.1185611674 9.1882258730
## [66] 3.1157346803 8.6773771010 7.2824302003 7.0556944180 3.6986716893
## [71] 5.2809070272 8.9495812371 6.5852093977 5.3721435951 6.3285687202
## [76] 8.1056990010 13.0460080249 1.7514580972 6.2887386860 6.7650014043
## [81] 4.3191753068 4.9121337629 5.7450702059 4.0616865151 3.5436202343
## [86] 3.3675397663 5.9914100255 4.4313821275 7.1593509284 12.1921320576
## [91] 4.3997877279 5.3985340019 1.7938595940 2.6409001408 8.3984201955
## [96] 7.3210652265 6.3405774725 5.4962871729 1.7248006117 4.1144466404
## [101] 9.1384893533 2.7362083928 7.6901032510 2.9300288723 3.2932136568
## [106] 3.6741820777 7.7871503100 2.9475264383 4.3923590652 7.6686844414
## [111] 6.3748187496 6.3062462385 8.2122381255 2.9788367895 1.6448161150
## [116] 3.1872408649 4.7275785545 6.2331030747 3.8427727872 4.9663397283
## [121] 7.2311388208 4.0506826288 6.8016752602 3.5640481296 2.7789821640
## [126] 12.9569398556 3.9944759179 5.5999233205 5.6183827458 5.2836835429
## [131] 5.3286469805 5.1686257125 5.1456798342 8.3575531556 2.8311985261
## [136] 6.3660017781 4.1126667765 8.0826368324 0.0001841672 10.0715048325
## [141] 4.4818237047 2.3931334122 8.3014377524 3.5772385560 7.5986926681
## [146] 8.6980097608 2.7323882113 4.3246657880 4.0927963193 9.8142185518
## [151] 0.8996582995 2.0451556754 7.1613843010 6.0365524825 2.2386553907
## [156] 0.6801591521 3.8736282537 6.1004313560 -0.6384919805 3.2923386348
## [161] 2.1989536077 6.4528941897 0.8304108982 8.9116365865 5.3321149077
## [166] 2.7570251409 7.0341029778 7.4985228530 4.5839865576 9.1900156486
## [171] 11.0660191198 5.0725609766 4.2032416295 4.5321942521 5.6535361711
## [176] 6.0127720475 1.1270728370 6.5398230426 6.2208414021 4.3420537737
## [181] 2.1290383773 3.7591378364 2.6734863665 5.7032483991 5.8708044211
## [186] 5.9480855760 6.9642768812 3.5683651056 10.5172043819 2.9672897825
## [191] 2.3417898344 4.8758587168 5.3035351602 4.8038623709 6.2463845118
## [196] 2.6468352229 5.9187325952 12.0540485526 8.2551964840 2.0552318758
## [201] 3.9189754823 2.7271688312 4.2316023974 4.5858481638 7.9153771032
## [206] 3.7794326416 3.8610684773 3.4489879921 7.9182655807 8.0045515487
## [211] 0.2121435598 5.6487992289 4.8572032558 7.1993987761 7.1736204421
## [216] 2.0442842546 7.1872598089 6.1795310766 3.6118326065 5.7543436006
## [221] 1.8839149690 4.5092651487 4.2214983994 2.8560401110 6.1938748244
## [226] -3.7900587596 5.4737706655 4.2559336789 6.6768354036 5.0242571925
## [231] 5.7356686799 8.5583010894 6.5056466846 4.3019161286 6.0291390505
## [236] 5.8385827122 8.5530333516 6.4689721311 5.5801545122 3.5749970262
## [241] 5.8798620763 3.4582685814 6.5762320785 4.8234330536 0.2500857352
## [246] 8.1470731654 11.4146396271 3.7246754176 8.6718857019 3.3618123957
## [251] 2.2675727638 11.5727692100 10.1186502608 6.5161752669 2.5897688509
## [256] 4.0385837961 6.8071089060 6.3633037405 5.9902048671 4.1022088928
## [261] 4.6889781013 4.0994838439 7.7847166894 8.1045477310 5.6424836425
## [266] 5.5011566686 6.2761332666 7.1470287146 7.4227603577 7.3396746089
## [271] 10.1092910630 10.3431030903 5.2031134616 6.3405412235 10.5084112172
## [276] -0.3893659812 0.1559512362 8.3948806813 1.1298803717 9.2468110652
## [281] 11.5987893149 3.5009218458 4.2098332317 -1.6442769987 5.2622520696
## [286] 4.2515454193 6.9533321301 7.5869667641 5.8874747999 2.7062404977
## [291] -2.1827066953 6.5229666898 6.8988985880 2.4149363451 4.1825348418
## [296] 8.1592435005 2.9379711006 5.3553200972 2.1193630409 2.5642798243
## [301] 5.6085213519 2.1057434800 -1.6190041212 9.3171202722 5.4686351610
## [306] 4.8421840075 5.6319054989 7.2150708762 3.0578182369 4.7122003776
## [311] 4.5994888351 5.2394815136 -0.1949357522 7.2055796342 5.7389412024
## [316] 4.3541205261 1.6082016137 6.0814950687 11.7603186692 5.6062030238
## [321] 7.4646578243 3.9667683571 7.0187921968 6.1722228987 5.5792079284
## [326] 6.2846617045 6.9761140231 4.1129637272 6.6249195291 6.6778576309
## [331] 1.4946474624 6.0903716488 1.4351276248 5.6741967414 4.4540778129
## [336] 2.7602682152 2.1928421301 9.5384123571 4.9240738292 3.7948151886
## [341] 0.1503495175 9.3648479492 6.3908799041 2.6113734807 3.2531492036
## [346] 5.8658914113 3.4770755112 12.3374801556 0.8052631499 0.9141407227
## [351] 2.3210662365 5.8247745993 5.9788203664 3.1337868415 6.1674550355
## [356] 2.9725796795 5.6188410384 6.8590450973 8.0885815585 -0.4033613273
## [361] 8.4305725927 7.3107070125 6.5398119867 5.1402066252 -0.6273969601
## [366] 6.3521846066 3.5566976956 3.7184881923 5.7559078300 3.2622723914
## [371] 3.5759504137 4.8691509777 10.1518496295 7.7619270134 3.2948117516
## [376] 4.0841158012 7.1878785615 7.6416026593 7.5768445062 3.8154257844
## [381] 6.7978619233 1.0936210955 7.6633721091 6.3507029657 6.1579138792
## [386] 7.2223978682 2.5995128592 7.8401693107 4.2312243669 1.8264452553
## [391] 7.1808488156 5.6175854051 7.1510999736 4.7257673920 2.9810215594
## [396] 10.6815277739 1.3926712534 2.0782467944 6.4639778946 2.9854604860
## [401] 0.8287959645 7.2531047138 6.7506544052 3.3850889412 3.3699574978
## [406] 5.0465307838 4.7440464592 4.8181157148 9.6041545022 -2.3786593489
## [411] 5.7940495388 7.7219604395 7.5519476363 -1.0574022068 10.2363540961
## [416] 1.5172609461 0.7593970018 -1.4874670464 6.2594955336 6.1504522139
## [421] 7.2953078876 5.0932885356 7.3006065079 3.6415337157 1.9789779719
## [426] 6.7964416870 0.4474372065 9.0955454891 8.4177491967 6.1310742206
## [431] 0.3817392609 9.5169232243 6.1799407079 2.4227610001 7.3923030411
## [436] 2.6584371250 1.7643406323 3.1561532326 5.0078161222 2.3966599907
## [441] 6.0188230024 5.4366523472 5.5234474225 6.1756012077 4.0673619879
## [446] 10.7175900561 8.4142697039 6.6903701277 4.5825877941 6.1659901395
## [451] 4.0757011850 4.9202287182 5.3094744709 5.0752976747 8.0163693759
## [456] 1.7669165294 9.8215580550 2.9810077991 8.3341172629 2.0480962116
## [461] 5.8250004004 1.5664126514 4.3882908885 4.1518010914 4.8329332720
## [466] 2.6512796476 7.9695003347 2.1957824552 6.2079051293 3.2516808584
## [471] 10.4579315799 4.5172536595 3.8909113834 7.7685851783 4.2864755829
## [476] 6.9150442461 6.4839199758 6.7885836672 7.5681477482 5.7611417839
## [481] 4.3536565147 4.8589364864 4.9939895431 5.3103683861 6.2621148326
## [486] 6.7066863491 6.3518011606 2.4184422457 2.5634526917 1.1941488059
## [491] 0.9126821034 5.7893055169 1.4190872991 5.5237015169 3.9283952627
## [496] 7.2785841593 9.4682985511 6.8249211305 0.2190807491 12.3479370263irfdfreg$residuals## 1 2 3 4 5
## -6.46144349 -4.70724217 -4.58090204 -4.21149198 9.62775942
## 6 7 8 9 10
## 2.77879011 8.21450765 0.66091913 -1.00928826 -7.36551822
## 11 12 13 14 15
## 6.13695587 0.16023962 2.66929335 5.28945211 2.89584953
## 16 17 18 19 20
## -0.62529720 6.33404128 -6.02878472 3.79028433 -0.92667745
## 21 22 23 24 25
## 0.94479896 1.62291650 -6.28209521 0.15543502 -2.60597242
## 26 27 28 29 30
## 1.21968462 -7.79717772 -2.46465804 0.23704853 -3.76831177
## 31 32 33 34 35
## 7.14857158 5.97140826 5.87601482 6.85878269 -12.05570718
## 36 37 38 39 40
## -3.41399816 3.78027508 1.39271186 -0.69560988 4.97964690
## 41 42 43 44 45
## -2.17480672 -12.79331538 -3.67686339 -0.25761050 -6.79320065
## 46 47 48 49 50
## -4.93296027 6.25599899 -8.42801550 -7.28933843 5.08964294
## 51 52 53 54 55
## -4.43772220 -8.76544059 -2.62082096 -6.15324836 5.74900688
## 56 57 58 59 60
## 0.18504347 3.82353140 -6.31541754 3.25241755 2.16106135
## 61 62 63 64 65
## 11.83623695 -2.42728524 4.64921178 -0.08980863 0.48294770
## 66 67 68 69 70
## 4.84373046 -2.63058167 3.08028905 -3.75890744 -2.27209736
## 71 72 73 74 75
## 6.38061976 14.36095313 -10.40376667 6.13983817 0.68544662
## 76 77 78 79 80
## -2.44345295 -6.83173735 -8.64340662 -3.32990513 -4.71170197
## 81 82 83 84 85
## -1.68924672 11.55706689 2.84481577 -10.63953455 -6.30370385
## 86 87 88 89 90
## -1.87535093 10.61104982 -6.44016626 -10.89099336 -0.05680758
## 91 92 93 94 95
## 5.33502953 -8.88441646 8.60999176 -1.63009628 -3.98972002
## 96 97 98 99 100
## 2.19794294 4.20463610 9.43167669 9.67527278 -3.58109141
## 101 102 103 104 105
## 3.56899072 -0.67264438 11.89714414 -8.75236518 0.41403225
## 106 107 108 109 110
## 7.46900913 -6.22626335 5.17078971 1.53144242 -1.00875243
## 111 112 113 114 115
## -13.71052652 11.09175214 6.70759731 -7.84926150 11.00861445
## 116 117 118 119 120
## -4.31522634 2.11948799 -3.78997108 -2.95257586 -2.10952607
## 121 122 123 124 125
## -10.00037672 -3.36166745 -1.05075476 1.57811209 -4.89538758
## 126 127 128 129 130
## 6.69971344 3.25712021 -1.87551184 -6.67599838 1.23574622
## 131 132 133 134 135
## -8.12759649 6.84772783 -5.53808066 -0.84531046 9.40855582
## 136 137 138 139 140
## 6.37844369 -5.97353030 0.16215343 -6.12182833 1.79791771
## 141 142 143 144 145
## -5.27696015 -3.83550081 -0.04062414 10.91311126 2.63693178
## 146 147 148 149 150
## 4.24245747 3.48046758 -0.60928308 1.43516348 1.33799953
## 151 152 153 154 155
## -7.51876928 0.58151157 3.78339410 -11.00924718 4.20294842
## 156 157 158 159 160
## 0.27991574 4.53173365 -0.34126559 1.54831387 -2.14315230
## 161 162 163 164 165
## 1.85882801 -4.51179327 -3.21809139 -0.20744661 2.30596145
## 166 167 168 169 170
## 2.02007803 2.52801001 -7.65167327 7.87731398 1.72400279
## 171 172 173 174 175
## -10.02323926 0.87485469 -4.62319317 -12.45982303 7.67665454
## 176 177 178 179 180
## -3.67520552 2.87798247 4.10817046 -4.13620207 4.87395111
## 181 182 183 184 185
## -6.45930981 5.81650756 9.78826401 8.44057898 -1.93528899
## 186 187 188 189 190
## 10.89711026 -1.16520899 -16.34928368 4.03684313 -6.92581947
## 191 192 193 194 195
## -11.18441415 10.01062202 -1.16705119 7.09815992 -1.72715420
## 196 197 198 199 200
## 0.92721049 11.49180942 -0.73499625 7.07838262 7.85894664
## 201 202 203 204 205
## -2.51545280 -7.59001396 -5.23662748 12.42929217 -5.09049642
## 206 207 208 209 210
## 4.37506126 -3.48482634 6.13508583 -5.67275268 0.19975697
## 211 212 213 214 215
## 0.53306207 -8.33025150 -9.66438452 -2.58676069 2.27914711
## 216 217 218 219 220
## 14.57729518 -4.72023969 2.92123746 9.71002518 -1.39749110
## 221 222 223 224 225
## 0.77288601 -5.97616170 5.75450327 -1.60445711 2.22359605
## 226 227 228 229 230
## -2.67501608 1.04034793 8.30590758 1.02395006 -0.69013150
## 231 232 233 234 235
## 4.90313367 3.74008816 -0.26966933 -0.42337365 -9.16070991
## 236 237 238 239 240
## -7.19022451 6.21403040 -1.48407230 -7.43771464 -12.12946527
## 241 242 243 244 245
## -3.69061552 4.76449662 3.75184442 -4.04578757 8.09041980
## 246 247 248 249 250
## -6.32267195 -5.66286603 -8.06130502 0.80811822 9.36211604
## 251 252 253 254 255
## -4.26529199 -0.95132598 2.23293272 10.81286162 0.65933128
## 256 257 258 259 260
## 10.05528805 1.26598796 2.65142834 12.91160030 1.50712456
## 261 262 263 264 265
## -2.70918547 -3.94057919 -13.08346509 3.25952147 7.65501826
## 266 267 268 269 270
## -1.70700396 -8.23241723 -2.85177137 -5.62282768 -8.39081257
## 271 272 273 274 275
## 7.37466384 11.45422704 1.59588510 1.03632679 -2.87856392
## 276 277 278 279 280
## 1.94108059 7.16547820 10.73259935 2.54908088 -1.59368860
## 281 282 283 284 285
## 4.33548311 7.60656326 -0.56381993 4.80152004 -1.43259050
## 286 287 288 289 290
## 7.34017967 6.86975138 -1.88771945 0.18475795 0.52147463
## 291 292 293 294 295
## -6.80362857 3.69844652 5.25376313 -5.41004162 7.06460617
## 296 297 298 299 300
## -11.46212326 -2.64674882 0.82128660 7.67410490 6.68497584
## 301 302 303 304 305
## 1.43883634 -2.57413756 3.83232306 -1.18681550 3.40553915
## 306 307 308 309 310
## -3.15911238 6.93504608 -2.94901897 -8.43035434 4.40643514
## 311 312 313 314 315
## 8.39711212 -8.99211956 4.76890407 -0.25982465 -7.57981599
## 316 317 318 319 320
## 6.24602547 -9.75917398 -5.18645948 0.04182506 5.09762776
## 321 322 323 324 325
## 5.27114627 5.03316475 11.95050054 -11.36286279 -4.23201206
## 326 327 328 329 330
## 6.74052113 3.02549152 -10.12105657 -0.01064766 7.73563288
## 331 332 333 334 335
## -8.12197073 -5.10916472 7.86677140 7.32398120 -0.43807956
## 336 337 338 339 340
## 3.34430796 2.46544713 -3.87982669 5.21459665 5.61793636
## 341 342 343 344 345
## 4.62143420 15.17847853 0.97021801 -1.61968578 -6.79488233
## 346 347 348 349 350
## -1.35543505 2.85242771 7.79542771 -0.25858374 -0.63069808
## 351 352 353 354 355
## -2.07833410 -4.53693598 -10.13443739 -3.26488304 -13.33000190
## 356 357 358 359 360
## -8.07924550 -6.68166987 3.91219879 2.11024220 8.46297523
## 361 362 363 364 365
## -0.71091541 1.53367337 -5.42682244 4.37169204 -10.58899479
## 366 367 368 369 370
## -11.27162082 5.80138642 -5.38754714 -5.56211710 -7.01241774
## 371 372 373 374 375
## -3.94507616 7.52941515 -6.80828264 1.37953547 -10.19580270
## 376 377 378 379 380
## -7.88196201 -7.67916066 -2.16707235 4.12866170 1.52231312
## 381 382 383 384 385
## -3.74949657 12.52206807 6.01111729 -6.54544107 1.62597649
## 386 387 388 389 390
## 4.16667505 3.35478923 9.11778620 -0.06811797 4.09303461
## 391 392 393 394 395
## 2.15245298 -2.05539514 4.08550942 -1.05194269 5.96868140
## 396 397 398 399 400
## 5.28567834 1.23258606 7.11210885 8.58560121 -4.33759858
## 401 402 403 404 405
## -6.93726497 -1.37485003 3.58394686 3.23742858 1.50796406
## 406 407 408 409 410
## 3.85783984 -5.31098106 -4.46684133 1.42632196 -6.54617311
## 411 412 413 414 415
## 4.61727113 -11.64371921 3.55154022 0.13519517 -5.25777182
## 416 417 418 419 420
## -10.08478270 -0.32382878 0.46145057 3.41518889 4.50207913
## 421 422 423 424 425
## 6.12804319 0.01438820 -0.48550334 -5.38634928 -3.20570824
## 426 427 428 429 430
## -8.29040624 -2.99517309 2.41506659 3.90240995 -4.29789140
## 431 432 433 434 435
## -8.64767176 -6.79277649 -10.66546356 -4.22577547 0.87487397
## 436 437 438 439 440
## -5.79306861 5.20367200 -2.35424844 -9.32407649 -2.90820711
## 441 442 443 444 445
## -1.42845423 4.24046189 1.99341049 -9.78498925 -4.64754363
## 446 447 448 449 450
## -14.70239991 -6.69725031 2.46840404 2.04556549 3.77655073
## 451 452 453 454 455
## -6.65934575 11.34631269 9.64626535 5.69559205 -6.56210437
## 456 457 458 459 460
## 1.06122423 -2.47409004 -5.61639091 -1.95774839 3.39285177
## 461 462 463 464 465
## 4.93749866 14.14544246 1.52611058 -9.13600002 3.11271004
## 466 467 468 469 470
## -2.11236606 -3.68919882 3.24271581 5.98589794 4.62140068
## 471 472 473 474 475
## -4.66968868 0.95632509 -1.67951166 1.63551031 -5.27166267
## 476 477 478 479 480
## -7.10705625 -7.31772797 5.32146456 1.68128091 -2.66124105
## 481 482 483 484 485
## 3.13227755 5.60018452 1.81439629 4.22208592 8.33217836
## 486 487 488 489 490
## 1.85259083 3.96427147 -1.37946289 10.29423173 -4.86299582
## 491 492 493 494 495
## 1.96503225 0.46760469 3.22593345 -3.13020771 -5.59565401
## 496 497 498 499 500
## 1.69334170 6.59961715 2.26103446 -15.32862099 -2.19962618