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©e¥ô ÁÙ¬O ®¦´f¦æ¬° 10/1/05 7:20:34 am
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1. Consider the market for curbside recycling of household plastics, glass, and paper in the 1960s. Since virtually no curbside recycling took place at this time, business economists conceptualize this market graphically as a non-equilibrium situation:

Describe the above situation pictured in the graph noting the equilibrium quantity of recycling.
Recently, we have witnessed the emergence of curbside recycling programs in most major cities in the United States. Using the above graph, explain what changes have occurred in the recycling market and describe the new equilibrium quantity of recycling.
2. A crucial world commodity is crude oil, which is traded the world over and priced in U.S. dollars. The world demand and supply curves for oil are:
QD = 5 -30P + 4GNP ¡V 200r
QS = 25 + 10P
where Q is the quantity of oil in millions of barrels, P is the dollar price of a barrel of oil, GNP is world income measured in billions, and r is the effective world interest rate in percent.
a) Find the equilibrium world price of oil if the world interest rate is 10 percent and the level of world income is 153 billion.
b) Calculate the world shortage of oil if the price of oil was to be held at $10 a barrel.
3. A management-consulting firm has estimated the following demand function for your product, tractor trailers: QY = 36 ¡V 1PY + 3At¡V1 ¡V 1.2PX + 0.1Y
where: QY = number of trailers sold annually.
PY = price of trailers in thousands.
At¡V1 = advertising expenditures last quarter in thousands.
PX = average price of trailers in thousands.
Y = average income of tractor-trailer rigs in thousands.
The adjusted R-squared was 0.94, and the standard error of the regression was 2 for sample size of 40.
a) Explain how trailer sales vary with changes in advertising and trailer rig income.
b) This year, you will price your trailers at $13,000 each; advertising expenditures last year were $4,000; the expected average price of tractors this year is $15,000; and the predicted average income earned per rig is $100,000. Calculate the number of units and sales revenue predicted by our model. Also, determine an approximate 95-percent confidence interval for number of units and sales revenue.
4. A home builders association is interested in knowing how private housing starts (PHS) are influenced by mortgage interest rates (MR) and level of income (INC). Using quarterly data for 10 years, the following multiple regression model was estimated: PHS = ƒÑ + ƒÒ1MR + ƒÒ2INC. The following estimation results were obtained:
Variable Coefficient Standard Error T-statistic p-value
CONST -325.679 314.562 -1.03534 .307
MR -29.7924 4.97884 -5.98381 .000
INC .110325 0.03363 3.28081 .003
No. of Observations = 40 R2 = .4986 (adj) = .4715
Sum of Sq. Resid. = 231958 Std. Error of Reg. = 79.1779
Durbin-Watson = 1.84280
F(2, 37) = 18.3945 Significance = .000003
a) Evaluate the quality of the model.
b) In evaluating the model results, concerns were raised that private housing starts data appear to be highly seasonal and that there may be a diminishing returns impact to the effect that income has on private housing starts. The model was re-estimated accounting for data seasonality by introducing quarterly seasonal dummy variables:
Quarter 2: Q2 = 1 for second quarters, and 0 otherwise.
Quarter 3: Q3 = 1 for third quarters, and 0 otherwise.
Quarter 4: Q4 = 1 for fourth quarters, and 0 otherwise.
In addition, to evaluate a non-linear effect of income, a squared income term (INC2) was added to the model. The new model yielded the following:
Variable Coefficient Standard Error T-statistic p-value
CONST -12906.0 3693.42 -3.49432 .002
MR -35.2073 2.89548 -12.1594 .000
INC 2.61378 .738987 3.53697 .002
INC2 -.0001246 .0000367 -3.39077 .003
Q2 163.559 17.9517 9.11107 .000
Q3 137.293 17.9654 7.64207 .000
Q4 63.7067 18.0388 3.53165 .002
No. of Observations = 40 R2 = .8854 (adj) = .8646
Sum of Sq. Resid. = 53005.5 Std. Error of Reg. = 40.0778
Durbin-Watson = 1.31884
F(6, 33) = 42.5000 Significance = .000000
Does the addition of seasonal dummy variables and INC2 improve the model fit? Explain.
5. Marshall¡¦s Sprocket Store has determined its price is explained by P = 8 ¡V 0.04Q and its average cost by AC = 40 ¡V 2Q + 0.1Q2. For a quantity range of 0-20, plot total revenue and marginal revenue against quantity and plot average cost and marginal cost against quantity. What quantity represents maximum revenue? What quantity represents minimum cost?
http://intranet.stgregorys.edu/people/faculty/mshorney/FL05/DirStudies/BU3123_Exam_Fl05.doc
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http://www.blogtw.com/blog.php?id=229773
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©Î³\¤£¤Ö¤H³£´¿¸g¦³¹Lºô¶¾D¨ì¸j¬[ªº¸gÅç¡A¤]´N¬O³y³X¬Y¨Çºô¶¤§«á¡A¦Û¤v©Ò³]©wªºº¶´N³Q¨ú¥N¡A¬Æ¦Ü¨C¦¸¤@¶}ÂsÄý¾¹¡A´N³Q±a¨ì³o¨Ç´c·Nªººô¯¸¡AµM¦Ó³o¼Ëªº¦æ¬°¡A¸g¹L¥x¥_¦aÀ˸p½Õ¬d«á¡A¤w¸g»{©wºc¦¨§«®`¹q¸£¨Ï¥Î¸o¡CÀˤè½Õ¬d«ü¥X¡A¬R¬ìºô¸ô¤½¥qt³d¤H¥ÐÄR¶²¡A¬°¤F¼W¥[¬R¬ì¤½¥q©ÒÄÝ´M¹Ú¶éºô¸ô²á¤Ñ«ÇªºÂsÄý¦¸¼Æ¡A©ó¬O´N©e°U¤uµ{®v¼B©¾¹F¼g´c·Nµ{¦¡¡Aµ¥¨ì¨Ï¥ÎªÌ³s½u¨ì´M¹Ú¶éºô¯¸¥H«á¡A´Nn¨D¨Ï¥ÎªÌ¦w¸Ë¡A¦pªG¤£¦w¸Ë¡Aµ{¦¡ÁÙ·|¤@ª½¥X²{¸ß°Ýµøµ¡¡A¢¨Ïºô¤Í¦w¸Ë¡C¥[¤WÀˤèµo²{¡A¬R¬ìºô¸ô¦b¨S¦³§iª¾¨Ï¥ÎªÌªº±¡ªp¤U¡A¥un¨Ï¥ÎªÌ¸Ë¤W´c·Nµ{¦¡¡A¹q¸£ùتº¨t²ÎÀÉ®×´N¾D¨ì×§ï¡A±j¢¨Ï¥ÎªÌ³s½u¨ì´M¹Ú¶éºô¯¸¡A¤zÂZ¨Ï¥ÎªÌ¥¿±`¨Ï¥Î¹q¸£¡AÀˤè¦]¦¹»{©w¥ÐÄR¶²©M¼B©¾¹Fªº¦æ¬°¡A¤w¸gºc¦¨¦Dªkªº§«®`¹q¸£¨Ï¥Î¸o¡A©Ò¥H¡A¨Ìªk±N¨â¤H´£°_¤½¶D¡C
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http://www.blogtw.com/blog.php?id=227403
(108) Ó°jÅT
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¥Ø«e¥¡¦æ«¶K²{²v¡A¾á«O©ñ´Ú¿Ä³q§Q²v©Mµu´Á¿Ä³q§Q²v¦U¬°2%¡B2.375%¤Î4.25%¡A¦Ó«e¦¸¤É®§®É¡A¥¡¦æ¤´ªí¥Ü¡uª«»ù¤Wº¦À£¤O¤´¤j¡v¡A¥[¤W¤µ¦~8¤ëªº®ø¶OªÌª«»ù«ü¼Æ¤j´T¤W¤É¦Ü3.56%ªº¤ô·Ç¡A¦Ó¤µ¦~«e8Ó¤ë®ø¶OªÌª«»ù«ü¼Æ¤]¬ð¯}2%¡A¨Ó¨ì2.13%¡A¶W¹L¥Dp³B¹w¦ôªº2%¡C
¦æ®w¥DºÞ«ü¥X¡A¬ü°ê³Ì·s¤½§Gªº8¤ë¥Í²£ªÌª«»ù«ü¼Æ¦~¼W²v°ª¹F5.1%¡A³Ð¤U14¦~¨Óªº·s°ª¡A¥«³õ¤]±À´ú¬ü°ê±N«ùÄò¤É®§¡A±N¾ÉP¬ü¤¸(·s»D)¤Î·s¥x¹ô§Q®tÂX¤j¡A¦A¥[¤W¡A¥Ø«e¤@¦~´Á©w¦s§Q²v¦©°£®ø¶OªÌª«»ù«ü¼Æ¡A¹ê½èt§Q²v±¡ªp¨Ã¨S¦³¦]¥¡¦æ«ùÄò¤É®§¦Ó§ïµ½¡A¦]¦¹¡A¥¡¦æ«Ü¥i¯à¦b¦¹®É¤É®§¡A¥«³õ¤@¯ë¹w¦ô¥¡¦æ±N¥Ø«e«¶K²{²v¥Ñ2%¤W½Õ¦Ü2.25%¡C
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http://www.blogtw.com/blog.php?id=225517
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