hw2soln - PAD? 8130 Spring 2012 Fertig UGA Homework 2...

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Unformatted text preview: PAD? 8130 Spring 2012 Fertig UGA Homework 2 Solutions 1. T he following tabie gives data on quit rates per 100 employees and unemployment rates in 13 large states. State Unemployment Rate . . . - - . - -' 3.2 I. . u - - . - a. Plot the data in a scatter diagram with a fitted line. You can input data into Stata by using the “input” command. Use help to read up on it. Quit rate A NNH—‘H‘mmw woomL-fi-Mw NHMMN £00quth 3 4 5 6 7 8 urate a quitrate mm Fitted values b. Assume that the quit rate (Y) is iineariy reiated to the unempioymcnt rate (X) as YWHMW.QEMMammwyMMmmwgwmemmgwmwmmlkum OLS estimates of the intercept and stops coefficients using Stata as well. QR Y U R STATE (x .—-—. -.._, H U.) l—‘l—‘ 43-h.) tht—x Midi-AH OELDU‘Iqfi U'l - m .l m N U) MN \iU‘l HM Dot—t .2 9 I\J N 1 #— I .——' “Y4 "sf-"x A: Y( X(aneanx 113 173 073 Q63 —007 ~LO7 —187 «L47 -L77 ~L77 053 L73 053 i)4neanY -Q62 -072 -052 —052 ~OA2 —&02 Q68 Q38 &58 Q78 918 —012 Ilgllllgllll —-0.565 -2 159 -1.388 -O.282 -7.524 SUNG ,4.— 028621174 ' 3366257974 ‘ J [a F) m 00 // / _ Source I SS df MS //’ Number of obs w 13 ——————————— ——+———————————~~mu«»_m—wn~m~————— Jr” F( 1, 11) 2 20.71 Model I 2.15341299 1 2.15341299/' Prob > F = 0.0008 Residual | 1.14351004 11 .1039554§8 R—squared = 0.6532 ——————————— ——+————————————~~uunnm~—~:7£L———— Adj R~squared = 0.6216 Total | 3.29692303 12 «204743586 Root MSE = .32242 quitrate i Coeg,///E;:;/;;r. t P>|ti [95% Conf. Interval] _ _ _ _ _ _ ‘ u u _ _ .....+....... .................._.___‘ ._.__._..._.________._..__.__._..__.__.,_,.,.,______._.____,_____........._._........._.____.____ urate —.2862117 .062885 —4.55 0.001 ~.4246208 —.l478027 ucous 3.366258 .3310838 10.17 0.000 2.637547 4.094969 2. Suppose the classic linear regression model applies to y=bx+e. a. What is the OLS estimator for the slope coefficient when there is no constant term? Show your work. 6‘7" (git Wt) h «4-; * 3L1 ‘ b Ml; 5:: Ki 2’“ l b. The slope coefficient in the regression of x on y is just the inverse of the slope from the regression of y on x (no constant term in both cases). Is this statement true, false, or uncertain? Explain your answer. Ll x 4’ (gt-we; Switcher/l) xv ,i i _. x g l9 7: will <th up wit We t 1 ,. A fix" U L. “AW/f 0%,. ‘ ., .\ (I \ “ i t $3.. XL Z t “it l 0. Run a regression of the above form in State using I’SID data. Note that to run a ng$mnWMmmacmwmmimmni$mmymnwmnouwflmommnfimmmmmWi When you switch x and y in the regression, is the slepe coefficient the inverse of the original slope coefficient? Show your work. | + .. .. _,...:T_‘. .23.; 2;? _ _ _ _ _ _ _ _ _ _ _ . _ _ m _ u fl u u n m m n _ _ n , m w _ _ _ _ _ _ _ _ _ _ _ _ _ _ . u m M w w _ ._ .. L<:f457.057 F . reg famine educhd, noconstant Source | SS df MS ___________ -_+___-__________________________ Model E 4.3294e+13 1 4.3294e+13 Residual i 8.9042e+l3 8275 1.0760e+10 ___________ __+______________________________ Total 1 1.3234e+l4 8276 l.5990e+10 l Oe+05 famine Coef Std. Err. t educhd 86.03155 63.43 reg/éauchd faminc, noconstant //K Source | SS df MS / mmmmmmmmmmm H~+ ____________________________ -— // Model E 475619.487 1 475619.487 //’ Residual E 978196.513 8275 118.211059 ___________ __+______________________________ ' Total 1 1453816 8276 175.666506 educhd | Coef Std Err. t famine .......... "KKKHE s :: amoig :2 come Number of obs = 8276 F( 1, 8275) = 4023.48 Prob > F = 0.0000 R—squared = 0.3272 Adj R—squared = 0.3271 Root MSE = p>§t| [95% Conf Interval] 0 000 5288.414 5625.701 Number of obs = 8276 F( 1, 8275) = 4023.48 Prob > F = 0.0000 R~squared = 0.3272 Adj R—squared = 0.3271 Root MSE = 10.872 P>§tl [95% Conf. Interval] 0.000 .0000581 .0000618 ...
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This note was uploaded on 03/28/2012 for the course PADP 8130 taught by Professor Fertig during the Spring '12 term at LSU.

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hw2soln - PAD? 8130 Spring 2012 Fertig UGA Homework 2...

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