
Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge
Edition 6ISBN: 130527010X
Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge
Edition 6ISBN: 130527010X(i) Let
0 and
1 be the intercept and slope from the regression of yi on xi, using n observations. Let c1 and c2, with c2 ? 0, be constants. Let
0 and
1 be the intercept and slope from the regression of c1yi on c2xi. Show that
1 = (c1/c2)
0 and
0 = c1
0, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain
1, plug the scaled versions of x and y into (2.19). Then, use (2.17) for
0, being sure to plug in the scaled x and y and the correct slope.]
(ii) Now, let
0 and
1 be from the regression of (c1 + yi) on (c2 + xi) (with no restriction on c1 or c2). Show that
l =
1 and
0 =
0 + c1 - c2
1.
(iii) Now, let
0 and
1 be the OLS estimates from the regression log(yi) on xi, where we must assume yi. > 0 for all i. For c1 > 0, let
0 and
1 be the intercept and slope from the regression of log(c1yi) on xi. Show that
.
(iv) Now, assuming that x. > 0 for all i, let
0 and
1 be the intercept and slope from the regression of y. on log(c2 xi). How do
0 and
1 compare with the intercept and slope from the regression of yi on log(xi)?
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Other![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/a0a0edfc_a262_4510_a83c_183b73ab713b_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/b7c318dc_f4e3_41d0_8360_a88c6f9ab96d_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/654a353a_e0c6_4e27_b581_923432a601ac_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/a7f99e06_cc98_4cc2_9b6c_4341e2b99bb1_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/f77a5ce8_6d8e_48bc_9008_4973dd10ceca_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/583e369e_bfac_4c04_ba32_6d981597072e_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/c1b21518_7df9_44ac_b9b0_07d0091fd784_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/a24cd91e_2df5_41be_9a89_a692177211ec_SMCC2709_00.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/fcf2cebb_e7fe_4c61_9c38_619ff8c2a503_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/30f06d4c_32da_46b5_95ef_15861bc1fed3_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/730c376e_7f5e_4541_874d_060cb300bc1a_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/02ea6561_9ee7_4518_b7b2_ed924f52b534_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/07116b74_3fbd_4b8f_9170_ebd04b8181e5_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/f473a9ba_b96d_4efc_b029_784274ed3f2c_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/d784f667_ed97_473e_80ba_48c19c1949b9_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/57d2df25_4542_4430_8f4f_9bba5b30df30_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/62422c1b_06ce_49f9_a6c9_cadedc376f2e_SMCC2709_00.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/5cac99f5_f230_41f5_a370_8abcfc784899_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/8e6f7591_2826_45f9_80de_18619ba68f95_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/ce191b65_d4c5_4e51_b559_b2137df7a7f4_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/5b468a8c_1ced_45f2_80a8_62c3cede2d67_SMCC2709_11.jpg)
.![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/f5cdb182_8432_4041_b5ec_b12814bf0caa_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/6b932142_912f_400c_9514_3236fc39dc5f_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/83707580_f38f_4fec_9688_478f307b105e_SMCC2709_11.jpg)
![<blockquote> (i) Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>) <span class=sub>0</span> and <span class=sub>0</span> = c<span class=sub>1</span> <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that <span class=sub>l</span> = <span class=sub>1</span> and <span class=sub>0</span> = <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span> <span class=sub>1</span>. (iii) Now, let <span class=sub>0</span> and <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that . (iv) Now, assuming that x. > 0 for all i, let <span class=sub>0</span> and <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do <span class=sub>0</span> and <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>](https://d2lvgg3v3hfg70.cloudfront.net/SMCC2709/3f807d33_79d5_4b31_ac41_63071e2366a3_SMCC2709_11.jpg)

