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# Managerial Economics ECON 3125

UNCC

GPA 3.91

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This 7 page Class Notes was uploaded by Ora Rath on Sunday October 25, 2015. The Class Notes belongs to ECON 3125 at University of North Carolina - Charlotte taught by Caroline Swartz in Fall. Since its upload, it has received 125 views. For similar materials see /class/229020/econ-3125-university-of-north-carolina-charlotte in Economcs at University of North Carolina - Charlotte.

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Date Created: 10/25/15

Econ 3125 Name Put your name on this test Put your name and 800 number on the answer sheet last name first and bubble in the letters Your exam will be returned to you after grading This exam is divided in to two parts 20 calculatorfree questions 3 points each and 9 problems 5 points each Answer all of the questions in each section The exam adds up to 105 points N 00 C Swartz Exam that do not need a 3 points each Manaerial e conomics can be defined as E Malling limb 2 ml 39 b The economic analysis of internal management processes c The process of deciding objectives alternatives and performing sensitivity analysis d Analysis of predictions about which firms will be profitable Sensitivity analysis can best be defined as a Analyzing the impact ofthe firm s decisions on assumptions b Analyzing tradeoffs among multiple objectives Examinin the reliabilit of ke data used to make a decision 391 quotl r 39ch ji The economics lesson learned from analyzing at the secondary market for massively multiplayer online role playing games M MORPG was a Commodities are sold at a single price worldwide b Large industries do not always support high profits Off r aczll t b Questions are framed in ways that biases the answers c Different question versions are targeted to different segments of respondents d The wrong group of respondents is surveyed Cross sectional data refers to 6 r be quot1i llll il 3 quot11139 1an 5 g b Population data only c Data without dummy variables d Data without heteroskedasticity Page 1 of 6 1 22 Sept 11 Econ 3125 0 Regression analysis is a Used only with controlled market data b A tool similar to reression toward the mean quot v quoti39 1513 d None ofthe above The total sum of squared errors is the sum of the squared deviations from the a Regression line b Trend line quot idle i ll d The correlation coefficient 00 Random fluctuations occur because of a The interaction of business cycles and seasonal variations lrreular trends that offset each other 50 The mathematical rule used in calculating regression coefficients is Maximize the standard error of the regression 7 lall r lll39Tl 2 riirulrl c Maximize tstatistics d Maximize the R2 t 0 US Census began in 1790 and has been completed every 10 years since The data were collected from ever erson in the US on April 1 2010 The 2010 data are b Experimental data Both timeseries and crosssectional data d Timeseries data a reression euation t t 395 0 a 3 3 o c o b Variables are selected to maximize the R2 The main objective is to determine whether there is a valid correlation between two variables Forecasts of one or more independent variables are estimated statistically x I Timeseries patterns can be decomposed into a Past fluctuations and future fluctuations b Chanes caused b economic variables d Patterns of error terms C Swartz Page 2 of 6 Exam 1 22 Sept 11 Econ 3125 13 Business c cles are 4 SW 39lili li 39u ilrimri 7 L alluring b Irregular even s which rarely occur anymore c Abrupt departures from the current economic trend d Seasonal variations in observed data 14 If an economic time series is growing by approximately a constant dollar amount each period the most accurate forecast model would be a A quadratic equation b A constant rate of growth equation An exonential rowth equation 15 Dummy variables can be used to a Determine whether key variables have been omitted from the regression b Test the goodness of fit for an equation 0 Substitute for missin data for a ke variable N t it lli WNEs i l lip 16 Suppose you are analyzing the time series data for the price of regular gasoline sold at retail Your first observation is August 1960 and your second observation is September 1960 How would you construct a lagged variable for gas price Calculate the ratio of the August 1960 value to the September 1960 value and use that ratio as the lagged value for September 1960 Divide the August 1960 value by the September 1960 value and use that for the lagged value for August 1960 Subtract the September 1960 value from the August 1960 value and use that as the lagged value for Setember1960 2 E 8 17 Suppose that the goodness of fit of an equation is nearly perfect What is the value ofthe R2 statistic in this case a Close to either positive or negative one b Close to zero Close to neative one 18 The coefficient39s tstatistic is calculated as ll b the coefficient divided by its P value c the standard error of the coefficient divided by the standard error of the regression d the R2 divided by the coefficient C Swartz Page 3 of 6 Exam 1 22 Sept 11 Econ 3125 Wigwam llJ i jrcii39 jig lit uation and lies bet een 0 and 1 c Measures the unexplained variation in the independent variable d Measures the explained variation in the independent variable 20 Heteroskedasticit occurs whe a w gt w 39 b The regression coefficient are all statistically insignificant The random errors are correlated over time d Two or more explanatory variables are highly correlated 0 Problems 5 points each Answer the next three questions based on these regression results The Gastonia City Bank is planning its loans for the next several years and is using a model of loan demand developed from past experience Fred Smith is responsible for developing the mortgage loan component of total loan demand Fred estimates the following equation using 14 years of data Q 500 02 P 02 D 03 Y 150 H R2 0 844 adjusted R2 0765 294 154 125 375 306 Here Q mortgage loan demand in millions of dollars P the prime interest rate D the discount rate Y is per capita income thousands of dollars and H is an index of average city housing prices thousands of dollars The standard error of the regression is 220 and tstatistics are shown in parentheses 21 The Fstatistic for this regression is b between 0 and 2 c between 2 and 2 d greater than 2 or less than 2 22 Why would it be better to report the adjusted R2 than the R2 for this regression a The adjusted R2 would be higher than the R2 so the results would look stronger Ad39usted R2 is better for multile reressions and R2 is better for simle reressions 7i vaillll init will 7 3 ll 3v d The R2 is better in this case because the R2 is greater than the adjusted R2 C Swartz Page 4 of 6 22 Sept 11 Exam 1 Econ 3125 23 Which statistical problems might occur in this regression l Multicollinearity between P and D and between Y and H II Serial correlation of errors in the time series data Ill The Durbin Watson error from heteroskedasticity a only b ll only c land lll onl d m The next three problems are based on the regression data below Weight 262 15 Height 087 HrsExercise 252 Male 03 51 21 307 The standard error ofthe regression is 256 Weight is the individual39s weight in pounds Height is the individual39s height in inches HrsExercise is the hours spend exercise each week Male is a dummy variable 1 ifthe person is male and 0 otherwise 24 Forecast the weight of a woman who is 70 inches tall and who does not exercise 25 26 a b 1292 pounds 1342 ounds 0 d 1322 pounds Compared to the woman who is 70 inches tall and does not exercise how much more would we expect a man to weigh if he were also 70 inches tall and exercises 5 hours per week a 1650 pounds 1885 pounds Suppose a man39s weight is forecast as 2013 pounds using the above equation What is the lower limit ofthe 95 confidence interval for the forecast 39 5 5 1i pl lkilliil W 1477 pounds 1539 pounds 1443 pounds C Swartz Exam Page 5 of 6 1 22 Sept 11 Econ 3125 The next three problems are based on the regression data below A regressions was estimated to explain changes in the level of consumer credit such as credit card debt between January 2000 and January 2011 The dependent variable is Q quantity of consumer credit in billions of dollars The time variable 1 for January 2000 time 2 for February 2000 time 133 for January 2011 Raression Statistics Multiple R 099 R Square 099 Adjusted R Square 097 Standard Error 29179 Observations 133 f 39 39 tStat Intercept 160770 27892 time 953 9995 27 The equation from this regression is Q 099 097 time c Q 27892 9995 time d Q 9995 952622 time 28 Evaluate the statistical significance of the coefficients a Neither coefficient is statistically significant b Only Intercept is statistically significant Onl time is statisticall sinificant I 0 What is the predicted value of consumer debt in billions of dollars for January 2010 using these regression results a 279895 billion c 283707 billion d 284660 billion By signing below I confirm that l have neither given nor received help during this exam Sign here C Swartz Page 6 of 6 Exam 1 22 Sept 11

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