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Fall2005_Quiz11

Course: ECON 2020, Fall 2005
School: Utah
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________________________ Name: Class: ___________________ Date: __________ Quiz 11 Multiple Choice Identify the letter of the choice that best completes the statement or answers the question. ____ ____ ____ ____ 1. If the short-run Phillips curve were stable, which of the following would be unusual? a. an increase in government spending and a fall in unemployment b. an increase in inflation and a decrease in...

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________________________ Name: Class: ___________________ Date: __________ Quiz 11 Multiple Choice Identify the letter of the choice that best completes the statement or answers the question. ____ ____ ____ ____ 1. If the short-run Phillips curve were stable, which of the following would be unusual? a. an increase in government spending and a fall in unemployment b. an increase in inflation and a decrease in output c. a decrease in the inflation rate and a rise in the unemployment rate d. a decrease in output and an increase in unemployment 2. In the short-run, when aggregate demand increases, prices a. will fall and unemployment will rise. b. and unemployment fall. c. and unemployment rise. d. will rise and unemployment will fall. 3. Friedman and Phelps argued a. that in the long run, monetary growth did not influence those factors that determine the economy's unemployment rate. b. the Phillips curve could be exploited in the long run by using monetary, but not fiscal policy. c. that the short-run Phillips curve was flat. d. that there was either a short-run or long-run tradeoff between inflation and unemployment. 4. The position of the long-run Phillips curve and the long-run aggregate supply curve both depend on a. the natural rate of unemployment and monetary growth. b. the natural rate of unemployment, but not monetary growth. c. monetary growth, but not the natural rate of unemployment. d. neither monetary growth nor the natural rate of unemployment. 1 ID: A Name: ________________________ ID: A Use the graph below to answer the following questions. Figure 35-2 ____ ____ 5. Refer to Figure 35-2. If the economy starts at c and the money supply growth rate increases, in the short run the economy a. moves to b. b. moves to d. c. moves to e. d. None of the above is correct. 6. Refer to Figure 35-2. If the economy starts at c and the money supply growth rate decreases, in the short run the economy a. moves to b. b. stays at c. c. moves to e. d. None of the above is correct. 2 Name: ________________________ ID: A Use the two graphs in the diagram to answer the following questions. Figure 35-3 ____ 7. Refer to Figure 35-3. Starting from c and 3, in the short run, an unexpected decrease in money supply growth moves the economy to a. a and 1. b. b and 2. c. back to c and 3. d. d and 4. ____ 8. Which of the following would cause the price level to rise and output to fall in the short an run? a. increase in the money supply b. a decrease in the money supply c. an adverse supply shock d. a favorable supply shock ____ 9. Suppose an economy with high inflation decides to decrease the money supply growth rate. a. Initially unemployment rises. Eventually the short-run Phillips curve shifts right. b. Initially unemployment rises. Eventually the short-run Phillips curve shifts left. c. Initially unemployment falls. Eventually the short-run Phillips curve shifts right. d. Initially unemployment falls. Eventually the short-run Phillips curve shifts left. ____ 10. Proponents of rational expectations argued that the sacrifice ratio a. would be high because it was rational for people not to immediately change their expectations. b. would be high because people might adjust their expectations quickly if they found anti-inflation policy credible. c. could be low because it was rational for people not to immediately change their expectations. d. could be low because people might adjust their expectations quickly if they found anti-inflation policy credible. 3 Name: ________________________ ID: A Use the pair of diagrams below to answer the following questions. Figure 35-1 ____ 11. Refer to Figure 35-1. If the economy starts at c and 1, then in the short run, an increase in the money supply growth rate moves the economy to a. a and 1. b. b and 2. c. c and 3. d. None of the above is correct. ____ 12. Refer to Figure 35-1. If the economy starts at c and 1, then in the short run, an increase in government expenditures moves the economy to a. b and 2. b. b and 3. c. d and 3. d. None of the above is correct. ____ 13. Refer to Figure 35-1. If the economy starts at c and 1, then in the short run, a decrease in taxes moves the economy to a. d and 2. b. d and 3. c. back to c and 1. d. None of the above is correct. ____ 14. Refer to Figure 35-1. If the economy starts at c and 1, then in the short run, a decrease in aggregate demand moves the economy to a. a and 2. b. d and 3. c. e and 3. d. None of the above is correct. ____ 15. Refer to Figure 35-1. If the economy starts at c and 1, then in the short run, a decrease in the money supply growth rate moves the economy to a. e and 1. b. d and 2. c. d and 3. d. None of the above is correct. 4 This document was created with Win2PDF available at http://www.win2pdf.com. The unregistered version of Win2PDF is for evaluation or non-commercial use only.
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Utah - ECON - 2020
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Quiz 12004. 3. 241. (20%)100 consumers were asked whether they would like to purchase adomestic or a foreign automobile. Their responses are given below.PreferenceDomesticForeignFrequency6040(a) Develop a 95% confidence interval for the proport
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Quiz 22004. 5. 251. (20%)The number of units sold by 3 salespersons over a 3-month period areshown. Use = 0.05 and test for the independence of salesperson andtype of product. What is your conclusion?ProductSalespersonABCTroutman14124Kempto
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Review 12004.4.7Chapter 8: Construct a (1 ) 100% confidence intervals in large and smallsample cases. Determine sample size based on the desired margin of errorChapter 9 Interval estimate, hypothesis tests, and p-value of thepopulation mean and po
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Review 12004.4.7Chapter 8:Construct a (1 ) 100% confidence intervals in large andsmall sample cases.Determine sample size based on the desired margin of errorChapter 9Interval estimate, hypothesis tests, and p-value of thepopulation mean and popu
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Review 22004.6.9Chapter 12Least square estimate and fitted equationTesting hypothesis (F statistic and t statistic) and confidenceintervalr2predictionresidual plotsExample:Given are 5 observations for two variables x and y.xi235yi252520
UT Dallas - BA - 3360
TA Review 12004.4.71. A university planner wants to determine the proportion of springsemester students who will attend summer school. She surveys 32 currentstudents discovering that 12 will return for summer school.(a) Construct a 90% confidence int
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TA Review 12004.4.71. A university planner wants to determine the proportion of springsemester students who will attend summer school. She surveys 32 currentstudents discovering that 12 will return for summer school.(a) Construct a 90% confidence int
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TA Review 22004.6.91. Shown below is a portion of a computer output for regression analysisrelating y (dependent variable) and x (independent variable).ANOVAdfSSRegression1882Residual204000CoefficientsStandard ErrorIntercept5.003.56x6.3
UT Dallas - BA - 3360
TA Review 22004.6.91. Shown below is a portion of a computer output for regression analysisrelating y (dependent variable) and x (independent variable).ANOVAdfSSRegressionResidual120Coefficients8824000Standard ErrorIntercept5.003.56x6.3
UT Dallas - BA - 3360
Chapter 1 Data and StatisticsMotivation: the following kinds of statements in newspaper and magazine appear veryfrequently, Sales of new homes are accruing at a rate of 70300 homes per year. The unemployment rate has dropped to 4.0%. The Dow Jones In
UT Dallas - BA - 3360
Chapter 1 Data and StatisticsMotivation: the following kinds of statements in newspaper and magazine appear veryfrequently,Sales of new homes are accruing at a rate of 70300 homes per year.The unemployment rate has dropped to 4.0%.The Dow Jones Indus
UT Dallas - BA - 3360
1.1 Data(I) Basis components of a data set:Usually, a data set consists the following components:Element: the entities on which data are collected.Variable: a characteristic of interest for the element.Observation: the set of measurements collected f
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1.1 Data(I) Basis components of a data set:Usually, a data set consists the following components:Element: the entities on which data are collected.Variable: a characteristic of interest for the element.Observation: the set of measurements collected f
UT Dallas - BA - 3360
1.2 Data Source:There are two sources for data collection, one is existing sources and the other isstatistical studies.(I) Existing Sources:There are two existing sources:Company: some of the data commonly available from the internal informationsour
UT Dallas - BA - 3360
1.2 Data Source:There are two sources for data collection, one is existing sources and the other isstatistical studies.(I) Existing Sources:There are two existing sources:Company: some of the data commonly available from the internal informationsour
UT Dallas - BA - 3360
1.3 Descriptive Statistics:There are two classes of descriptive statistics, one class includes table and graph andthe other class includes numerical measures and index numbers.(I) Tabular and Graphical Approaches:Example 1(continue):Tabular approach
UT Dallas - BA - 3360
1.3 Descriptive Statistics:There are two classes of descriptive statistics, one class includes table and graph andthe other class includes numerical measures and index numbers.(I) Tabular and Graphical Approaches:Example 1(continue):Tabular approach
UT Dallas - BA - 3360
1.4 Statistical Inference:Descriptive statistics introduced in section 1.4 can provide important and intuitiveinformation about the data of interest. However, these statistical measures are mainlyexploratory. For more detailed, rigorous and accurate re
UT Dallas - BA - 3360
1.4 Statistical Inference:Descriptive statistics introduced in section 1.4 can provide important and intuitiveinformation about the data of interest. However, these statistical measures are mainlyexploratory. For more detailed, rigorous and accurate re
UT Dallas - BA - 3360
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UT Dallas - BA - 3360
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UT Dallas - BA - 3360
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UT Dallas - BA - 3360
2.1 Summarizing Qualitative Data:For qualitative data, we can use frequency distribution and relative frequency. Wenow introduce frequency distribution, relative frequency and percent frequency.Frequency distribution: tabular summary of data indicating
UT Dallas - BA - 3360
2.2 Summarizing Quantitative Data:1. Determine the classes:For quantitative data, we need to define the classes first. There are 3 steps to definethe classes for a frequency distribution:Step 1: Determine the number of nonoverlapping classes, usually
UT Dallas - BA - 3360
2.2 Summarizing Quantitative Data:1. Determine the classes:For quantitative data, we need to define the classes first. There are 3 steps to definethe classes for a frequency distribution:Step 1: Determine the number of nonoverlapping classes, usually
UT Dallas - BA - 3360
2.3 Exploratory Data Analysis:Stem-and-leaf display is a useful exploratory data analysis tool which can provide anidea of the shape of the distribution of a set of quantitative data.Example 1:Suppose the following data are the midterm scores of 10 st
UT Dallas - BA - 3360
2.3 Exploratory Data Analysis:Stem-and-leaf display is a useful exploratory data analysis tool which can provide anidea of the shape of the distribution of a set of quantitative data.Example 1:Suppose the following data are the midterm scores of 10 st
UT Dallas - BA - 3360
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UT Dallas - BA - 3360
Chapter 2 Descriptive Statistics: Table and GraphThe logical flow of this chapter:Summarizing qualitative data using tables and graphs (2.1)Summarizing quantitative data using tables and graphs (2.2)Exploratory data analysis using simple arithmetic an
UT Dallas - BA - 3360
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UT Dallas - BA - 3360
3.1 Measure of Location:Example 1:Suppose the following data are the scores of 10 students in a quiz,1, 3, 5, 7, 9, 2, 4, 6, 8, 10.Some measures need to be used to provide information about the performance of the10 students in this quiz.(I) Mean:n
UT Dallas - BA - 3360
3.2 Measure of Dispersion:Example 2:Suppose there are two factories producing the batteries. From each factory, 10batteries are drawn to test for the lifetime (in hours). These lifetimes are:Factory 1: 10.1, 9.9, 10.1, 9.9, 9.9, 10.1, 9.9, 10.1, 9.9,
UT Dallas - BA - 3360
3.2 Measure of Dispersion:Example 2:Suppose there are two factories producing the batteries. From each factory, 10batteries are drawn to test for the lifetime (in hours). These lifetimes are:Factory 1: 10.1, 9.9, 10.1, 9.9, 9.9, 10.1, 9.9, 10.1, 9.9,
UT Dallas - BA - 3360
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3.3 Exploratory Data Analysis:(I) Five-Number Summary:The five number summary can provide important information about both the locationand the dispersion of the data. They areSmallest valueFirst quartileMedianThird quartileLargest valueExample (c
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3.4 Measures of Relative Location:z-score is the quantity which can be used to measure the relative location of the data.Z-score, referred to as the standardized value for observation i, is defined asxi x.zi =sNote:z i is the number of standard de
UT Dallas - BA - 3360
3.4 Measures of Relative Location:z-score is the quantity which can be used to measure the relative location of the data.Z-score, referred to as the standardized value for observation i, is defined aszi =Note:z i is the number of standard deviation x
UT Dallas - BA - 3360
3.5 The Weighted Mean and Grouped Data:Weighted Mean:nwx1xw = i =niiwi=1.iNote: when data values vary in importance, the analyst must choosethe weight that best reflects the importance of each data value in thedetermination of the mean.Ex
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3.5 The Weighted Mean and Grouped Data:Weighted Mean:nxw =i =1ni =1wixiwi.Note: when data values vary in importance, the analyst must choosethe weight that best reflects the importance of each data value in thedetermination of the mean.Exampl
UT Dallas - BA - 3360
Chapter 3 Descriptive Statistics: NumericalMethodsSupposey1 , y 2 , , y Nthe sample drawn fromare all the elements in the population andx1 , x 2 , , x narey1 , y 2 , , y N , where N is referred to as the populationsize and n is the sample size. I
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Chapter 3 Descriptive Statistics: NumericalMethodsSupposey1, y2 ,K, yNare all the elements in the population andare the sample drawn fromy1, y2 ,K, yN ,x1 , x2 ,K, xnwhere N is referred to as thepopulation size and n is the sample size. In this c
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4.1 Crosstabulations and Scatter Diagrams:The crosstabulation (table) and the scatter diagram (graph) can help us understand therelationship between two variables.1. CrosstabulationsExample:Objective: explore the association of the quality and the pr
UT Dallas - BA - 3360
4.1 Crosstabulations and Scatter Diagrams:The crosstabulation (table) and the scatter diagram (graph) can help us understand therelationship between two variables.1. CrosstabulationsExample:Objective: explore the association of the quality and the pr
UT Dallas - BA - 3360
4.2 Numerical Measures of Association:There are several numerical measures of association. We first introduce the covarianceof two variables.(I)Covariance:Suppose we have two populations,population 1:y1 , y 2 , , y Nand population 2:w1 , w2 , , w
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4.2 Numerical Measures of Association:There are several numerical measures of association. We first introduce the covarianceof two variables.(I)Covariance:Suppose we have two populations,population 1:y1, y2,K yN,and population 2:w1 , w2 ,K, wN .
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5.1. Experiments, Counting Rules, and ProbabilitiesExperiment: any process that generates well-defined outcomes.Example:ExperimentToss a coinRoll a dicePlay a football gameRain tomorrowOutcomesHead, Tail1, 2, 3, 4, 5, 6Win, Lose, TieRain, No r
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5.1. Experiments, Counting Rules, and ProbabilitiesExperiment: any process that generates well-defined outcomes.Example:ExperimentOutcomesToss a coinRoll a dicePlay a football gameRain tomorrowHead, Tail1, 2, 3, 4, 5, 6Win, Lose, TieRain, No r
UT Dallas - BA - 3360
5.2. Events and Their ProbabilityModern probability theory: a probability value that expresses our degree of belief thatthe experimental outcome will occur is specified.Basic requirement for assigning probabilities:1. Let ei denote the ith experimenta
UT Dallas - BA - 3360
5.2. Events and Their ProbabilityModern probability theory: a probability value that expresses our degree of belief thatthe experimental outcome will occur is specified.Basic requirement for assigning probabilities:1. Let ei denote the ith experimenta
UT Dallas - BA - 3360
5.3. Some Basic Relationships of ProbabilityA c : the complement of A, the event containing all sample points that are not in A.A B : the union of A and B, the event containing all sample points belonging to Aor B or Both.A B :the intersection of A a
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5.3. Some Basic Relationships of ProbabilityA c : the complement of A, the event containing all sample points that are not in A.AB: the union of A and B, the event containing all sample points belonging to Aor B or Both.AB:the intersection of A and B
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5.4. Conditional ProbabilityA|B: event A given the condition that event B hasoccurred.Example:cfw_2| E1 : point 2 occurs given that the point is known to be even.P( A | B) : the conditional probability of A given B (as the event B hasoccurred, the c
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5.4. Conditional ProbabilityA|B: event A given the condition that event B hasoccurred.Example:cfw_2| E1 : point 2 occurs given that the point is known to be even.P(A| B) : the conditional probability of A given B (as the event B hasoccurred, the cha
UT Dallas - BA - 3360
5.5. Bayes TheoremExample 1:B: test (positive)A: no AIDSB c : test (negative)A c : AIDSFrom past experience and records, we knowP( A) = 0.99, P( B | A) = 0.03, P( B | A c ) = 0.98.That is, we know the probability of a patient having no AIDS, the c
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5.5. Bayes TheoremExample 1:B: test (positive)A: no AIDSAcB c : test (negative): AIDSFrom past experience and records, we knowP( A) = 0.99, P(B | A) = 0.03, P(B | Ac ) = 0.98.That is, we know the probability of a patient having no AIDS, the condi
UT Dallas - BA - 3360
6.1. Random VariableExample A:Suppose we gamble in a casino and the possible result is as follows.OutcomeToken (X)Win3Lose-4TieMoney (Y)30-4000In this example, the sample space is S = cfw_Win, Lose, Tie , containing 3 outcomes. X isthe qua
UT Dallas - BA - 3360
6.1. Random VariableExample A:Suppose we gamble in a casino and the possible result is as follows.OutcomeToken (X)Win3Lose-4TieMoney (Y)30-4000In this example, the sample space is S = cfw_Win, Lose, Tie , containing 3 outcomes. X isthe qua