# Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.

18 Pages

### SMART FIELD HOMEWORK FOR CHP 7

Course: BUS 603, Spring 2012
School: Rutgers
Rating:

Word Count: 1690

#### Document Preview

1: Task A fashion student was interested in factors that predicted the salaries of catwalk models. She collected data from 231 models. For each model she asked them their salary per day on days when they were working (salary), their age (age), how many years they had worked as a model (years), and then got a panel of experts from modeling agencies to rate the attractiveness of each model as a percentage with 100%...

Register Now

#### Unformatted Document Excerpt

Coursehero >> New Jersey >> Rutgers >> BUS 603

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
1: Task A fashion student was interested in factors that predicted the salaries of catwalk models. She collected data from 231 models. For each model she asked them their salary per day on days when they were working (salary), their age (age), how many years they had worked as a model (years), and then got a panel of experts from modeling agencies to rate the attractiveness of each model as a percentage with 100% being perfectly attractive(beauty). The data are in the file Supermodel.sav. Unfortunately, this fashion student bought some substandard statistics text and so doesnt know how to analyze her data. Can you help her out by conducting a multiple regression to see which variables predict a models salary? How valid is the regression model? Descriptive Statistics Salary per Day (? Attractiveness (%) Number of Years as a Model Age (Years) Mean 11.3385 75.9447 4.5854 18.0679 Std. Deviation 16.02644 6.77303 1.57865 2.42190 N 231 231 231 231 Model Summaryb Model R R Square Adjusted Std. Error Change R Square of the Durbin-Watson Statistics Estimate R Square F Change df1 df2 Sig. F Change 1 .429a .184 .173 14.57213 .184 Change 17.066 3 227 .000 a. Predictors: (Constant), beauty, years, age b. Dependent Variable: salary ANOVAa Model Sum of df Mean Square F Sig. Squares Regression 3 3623.988 Residual 48202.790 227 59074.754 17.066 .000b 212.347 Total 1 10871.964 230 a. Dependent Variable: salary b. Predictors: (Constant), beauty, years, age Adjusted states the shrinkage from the unadjusted value(0.184) pointing the model could not occur well. 2.057 We could use: Adjusted R2 =1-[(231-1/231-3-1)(231-2/231-3-2)(231+1/231)](1-0.184)=0.159 This is meaning these results are indicative that the model may not cross generalize well. The population used was 231 models and three predictors were observed which is suitable in observing medium to large effects. The 18.4% of the variance in salary per day, it is a suitable fit of the all data F (3227)=17.07,p<0.0001). The R2 The tolerance is below 0.2 which also indicates a serious problem in the collinearity of the model. This indicates that the age and years are almost identical meaning they measure almost the same thing. The reasoning for this is because as you age skin becomes wrinkled therefore making it harder to impress others in photo shoots due to large amounts of applied makeup. This is an indicator that the assumption may be unreliable for this model. Model Unstan Standar t Sig. 95.0% Correlat ions dardize dized Confide d Coeffici nce Coeffici ents Collinearity Statistics Interval ents B for B Std. Beta Lower (Consta Bound Partial Part Toleran order VIF ce -60.890 16.497 6.234 1.411 .942 4.418 .000 3.454 9.015 .397 .281 .265 .079 12.653 years -5.561 2.122 -.548 -2.621 .009 -9.743 -1.380 .337 -.171 -.157 .082 12.157 beauty -.196 .152 -.083 -1.289 .199 -.497 .104 .068 -.085 -.077 .867 1.153 nt) 1 a. age -3.691 Zero- Bound Error Upper .000 -93.396 -28.384 Dependent Variable: salary Collinearity Diagnosticsa Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) age years beauty 1 1.000 .00 .00 .00 .00 2 .070 7.479 .01 .00 .08 .02 3 .004 30.758 .30 .02 .01 .94 4 1 3.925 .001 63.344 .69 .98 .91 .04 a. Dependent Variable: salary Case Number Std. Residual Casewise Diagnosticsa Salary per Predicted Day (? 5 4.603 116 3.422 135 4.672 dimension0 155 3.257 191 3.153 198 3.510 a. Dependent Variable: Salary per Day (? Value 95.34 64.79 89.98 74.86 50.66 71.32 Residual 28.2647 67.07340 14.9259 49.86537 21.8946 68.08541 27.4025 47.45824 4.7164 45.93938 20.1729 51.14779 We could find clearly that the salary per day is predicted by the age of the model and it shows the positive relationship between them. Also, we could find that there is a negative relationship between years spending on model and theirs salary. Salary = 0+1age2experience+2attractivene =-60.89+(6.23 age) - (5.56experience) (0.02 attractivenes Task 2: Using the Glastonbury data from this chapter (with the dummy coding in GlastonburyDummy. sav), which you shouldve already analyzed, comment on whether you think the model is reliable and generalizable. Model Summaryb Model R R Square Adjusted R Std. Error Change Square Statistics of the Durbin-Watson Estimate R Square F Change df1 df2 Sig. F Change 1 a .276 .076 .053 a. Predictors: (Constant), Indie_Kid, Crusty, Metaller b. Dependent Variable: change .68818 .076 Change 3.270 3 119 .024 1.893 ANOVAa Model Sum of Df Mean Square F Sig. Squares Regression 3 1.549 Residual 56.358 119 61.004 .024b .474 Total 1 4.646 3.270 122 a. Dependent Variable: change b. Predictors: (Constant), Indie_Kid, Crusty, Metaller Coefficientsa Model Unstand Standar ardized T Sig. dized Confide Coefficie Coefficie nts 95.0% Correlati Collinearity Statistics ons nce nts Interval for B B Std. Beta Lower (Consta nt) 1 Crusty Metaller Indie_Ki d -.554 .090 -.412 .167 .028 -.410 Zero- Bound Error Upper Bound Partial Part Toleranc order VIF e -6.134 .000 -.733 -.375 -.232 -2.464 .015 -.742 -.081 -.203 -.220 -.217 .879 1.138 .160 .017 .177 .860 -.289 .346 .112 .016 .016 .874 1.144 .205 -.185 -2.001 .048 -.816 -.004 -.147 -.180 -.176 .909 1.100 a. Dependent Variable: change Collinearity Diagnosticsa Model Dimension Eigenvalue Condition Index Variance Proportions (Constant) Crusty Metaller Indie_Kid 1 1 1.727 1.000 .14 .08 .08 .05 2 1.000 1.314 .00 .07 .36 .30 3 1.000 1.314 .00 .38 .04 .33 4 .273 2.515 .86 .48 .52 .32 a. Dependent Variable: change Casewise Diagnosticsa Case Number Std. Residual change Predicted Value Residual 31 -2.302 -2.55 -.9658 -1.58417 153 2.317 1.04 -.5543 1.59431 202 -2.653 -2.38 -.5543 -1.82569 346 -2.479 -2.26 -.5543 -1.70569 479 2.215 .97 -.5543 1.52431 a. Dependent Variable: change Normality of the histogram and the normal P-P Plot indicate that there is a normal distribution in the histogram and normal (straight) line from the dashed line in the P-P Plot. In the histogram the assumption of normality has been met. With the P-P Plot, the dashed line does not deviate from the straight line, hence assuming normality. Homoscedasticity mean that the residual levels at each level of the predictor(s) should have the same variance. When they are unequal, they are said to be heteroscedasticity. The scatterplot of ZPred vs. ZResid does show that the height of each predictor is the same, indicating that they have the same variance. This tells us that the model used is realistic, hence proving homoscedasticity. Task 3: A study was carried out to explore the relationship between Aggression and several potential predicting factors in children 666 that had an older sibling. Variables measured were Parenting Style (high score bad parenting practices), Computer Games (high score more time spent playing computer games), Television (high score more time spent watching television), Diet (high score the child has a good diet low in E-numbers), and Sibling Aggression (high score more aggression seen in their older sibling). Past research indicated that parenting style and sibling aggression were good predictors of the level of aggression in the younger child. All other variables were treated in an exploratory fashion. The data are in the file Child Aggression.sav. Analyse them with multiple regression. : A study was carried out to explore the relationship between Aggression and several potential predicting factors in 666 children that had an older sibling. Variables measured were Parenting Style (high score = bad parenting practices), Computer Games (high score = more time spent playing computer games), Television (high score = more time spent watching television), Diet (high score = the child has a good diet low in E-numbers), and Sibling Aggression (high score = more aggression seen in their older sibling). Past research indicated that parenting style and sibling aggression were good predictors of the level of Aggression in the younger child. All other variables were treated in an exploratory fashion. The data are in the file Child Aggression.sav. Analyse them with multiple regression. Based on this subject, we know that parenting style and sibling aggression were good predictors of the level of aggression in the younger child. So we separate these two variables from others to conduct this analysis. I select AnalyzeRegressionLinear to enter parenting style and sibling aggression in the first step (forced entry) and the remaining variables in a second step (stepwise) to get the output shown below: Model Summaryd Model Std. Adjusted R Error of Change R R the Statistic Square Square Estimate s R Square F Sig. F 1 .231a .053 Change Change .050 .31125 .053 df1 18.644 df2 Change 2 663 .000 dimensio 2 n0 .264b .070 .066 .30875 .017 11.787 1 662 .001 3 .286c .082 .076 .30697 .012 8.682 1 661 .003 a. Predictors: (Constant), Sibling Aggression, Parenting Style b. Predictors: (Constant), Sibling Aggression, Parenting Style, Use of Computer Games. Durbin-Watson c. Predictors: (Constant), Sibling Aggression, Parenting Style, Use of Computer Games., Good Diet d. Dependent Variable: Aggression 1.911 Coefficientsa Model 95.0% Unstandard Standardiz ized Confidence ed Interval for Coefficients Coefficients t B Collinearity Statistics Lower -.479 Bound .632 Bound -.029 Tolerance .018 Std. Error -.006 Beta .012 .062 .012 .194 5.057 .000 .038 .086 .970 .093 .038 Sig. .096 2.491 .013 .020 .167 .970 -.007 .012 -.574 .566 -.030 .017 Parenting .054 .012 .170 4.385 .000 .030 .079 .937 Style Sibling .068 .038 .070 1.793 .073 -.006 .142 .933 Aggression Use of .126 .037 .134 3.433 .001 .054 .197 .918 Games. (Constant) -.006 .012 -.497 .619 -.029 .017 Parenting .062 .013 .194 4.925 .000 .037 .087 .897 Style Sibling .086 .038 .088 2.258 .024 .011 .161 .908 Aggression Use of 1 B (Constant) Upper .143 .037 .153 3.891 .000 .071 .216 .893 -.112 .038 -.118 -2.947 .003 -.186 -.037 .870 Parenting Style Sibling 2 Aggression (Constant) VIF Computer 3 Computer Games. Good Diet a. Dependent Variable: Aggression Excluded Variablesd Model 1 Beta In t Sig. Collinearity Statistics .049a 1.091 .276 Tolerance .042 VIF .704 .134a 3.433 .001 .132 .918 1.090 .918 -.092a Time spent Minimum Tolerance 1.421 .704 -2.313 .021 -.090 .894 1.119 .894 .044b .986 .324 .038 .703 1.423 .703 -.114 .870 1.150 .870 .028 .697 1.436 .669 watching television. Use of Computer Games. Good Diet 2 Time spent watching Partial television. Good Diet 3 Correlation -2.947 .003 -.118b Time spent .032c .715 .475 watching television. a. Predictors in the Model: (Constant), Sibling Aggression, Parenting Style b. Predictors in the Model: (Constant), Sibling Aggression, Parenting Style, Use of Computer Games. c. Predictors in the Model: (Constant), Sibling Aggression, Parenting Style, Use of Computer Games., Good Diet d. Dependent Variable: Aggression Residuals Statisticsa Minimum -.4630 Maximum .3279 -1.15286 1.18037 .00000 .30605 666 Std. Predicted -5.011 3.643 .000 1.000 666 Value Std. Residual -3.756 3.845 .000 .997 666 Predicted Value Residual a. Dependent Variable: Aggression Mean Std. Deviation -.0050 .09139 N 666 Casewise Diagnosticsa Case Number Std. Aggression -3.067 Value -.93 157 3.845 1.13 -.0529 1.18037 169 3.182 .85 -.1251 .97673 200 3.026 .75 -.1805 .92899 221 3.205 1.14 .1543 .98372 270 -3.018 -.73 .1936 -.92649 439 -3.092 -.85 .1041 -.94922 440 -3.290 -.95 .0624 -1.00982 463 -3.756 -1.15 .0055 -1.15286 482 3.476 1.07 .0025 1.06707 505 -3.223 -1.12 -.1284 -.98938 539 dimension0 Residual 45 Predicted 3.416 1.18 .1300 1.04877 a. Dependent Variable: Aggression Residual .0106 -.94162 We could analysis from the graphs above. Sibling aggression ( =0.088, b=0.086, t=2.26 and p<0.05). It is a significantly predicted aggression. The represents when the time of spending playing computer games increases, the aggression increases too. This is a positive relationship between them. Parenting style ( b=0.062, =0.088, t=2.26 and p<0.05). It is a significantly predicted aggression. The represents when the sibling aggression increases, the aggression increases too. This is a positive relationship between them. Computer games ( b=0.143, =0.037, t=3.89 and p<0.001). It is a significantly predicted aggression. The represent that the time of spending playing computer games increases, the aggression increase too. This is a positive relationship between them. E-number (b=0.112,t=2.95, =0.118 and p<0.01.). It is a significantly predicted aggression. The represent that when the diet changes, the aggression decreases. This is a negative relationship between them. There is only one factor could not to predict aggression. This is Television (t=0.72, b=0.032 and p> .05). It is not a significantly predict aggression. By the analysis above, we could find that the actually parenting style is the most substantive predictor of aggression. And the computer games factor, good diet and sibling aggression are behind it. The scatterplot of ZPRED and ZRESID do not point the random pattern. There is no independence of errors assumption. And the residuals could be uncorrelated in this model. The output of the statistic states that the errors are reasonably independent. In conclusion, by the errors, we could find there is no violations of the assumptions.
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

University of Phoenix - BUS - 210
Soapy RidesSoapy RidesSoapy Rides is a company that is built up of many different areas of employmentranging from owners/managers down to the manual labor. The employees that will bedoing the manual labor are broken down into a more diverse group. For
University of Phoenix - BUS - 210
SOAPY RIDESDivisional structureBeing an employee at Soapy Rides means you will focus on a certainposition. This enables us to all work together but be dedicated to our ownduties.We have 3 main goals here at Soapy Rides:1st To be motivated2nd Always
University of Phoenix - BUS - 210
HCR 210 DQ 1 week 1Select an accreditation agency and name an area of records management that isaffected by its guidelines. How does accreditation affect the practical activities of recordmanagement in hospitals?How does accreditation affect the pract
University of Phoenix - BUS - 210
HCR 210 DQ 2 week 1Using the Internet for information about medical records standards, what arethree ways you can ensure that you maintain standard measures ofperformance when you work in records management?The three ways you can ensure that you maint
University of Phoenix - BUS - 210
HCR 210 Week 2: CheckPoint: Records Administrators and TechniciansHow the general duties for handling patient records differ between arecords administrator (RHIA) and a records technician (RHIT)?Registered Health Information Administrator is known as R
University of Phoenix - BUS - 210
HCR 210 Week 3 Check Point Record FormatsMany facilities and physician offices maintain patient records in a paper format known as amanual record. A variety of formats are used to maintain manual records, including the sourceoriented records (SOR), pro
University of Phoenix - BUS - 210
HCR 210 Week 3 DQ 2What do you think is the reasoning for not filing incident reports in medical records?Provide examples of three incidents and explain why they could be problematic inpatients files.The purpose of an incident report is not to produce
University of Phoenix - BUS - 210
HCR 210 Week 3 DQ 1How might a novice confuse the following advance directives within the Patient SelfDetermination Act when distinguishing information as administrative or clinical?oooooDo Not Resuscitate OrderDurable Power of Attorney for Health
University of Phoenix - BUS - 210
Motivation and Team Cases StudyTwo Men and a Lot of Trucks:I think sheets motivation came from the success that she had early on in her business. There are a few motivationexamples of this explained in the story. First, by putting a simply drawing in a
University of Phoenix - BUS - 210
MAT/116Week 2 Concept CheckAxia College of University of PhoenixLavora MosesInstructor: Mrs. Jenell SargentDate: December 16, 2011How do you know when an equation has infinitely many solutions?An equation (with one variable) has infinitely many sol
University of Phoenix - BUS - 210
3Associate Level MaterialAppendix CStarting a BusinessStarting your own business can be exciting and daunting at the same time. Businesses use math whenmanaging finances, determining production levels, designing products and packaging, and monitoring
University of Phoenix - BUS - 210
Associate Level MaterialAppendix CAcute Care Patient ReportsFill in the following table with a general description of each type of patient report, whomay have to sign or authenticate it, and the standard time frame that JCAHO or AOArequires for it to
University of Phoenix - BUS - 210
Associate Level MaterialAppendix DCareer Self-Reflection IMaintaining patient files occurs within various types of health care and health caresettings. One goal of this course is to help you contemplate choices for your career.From what youve learned
University of Phoenix - BUS - 210
MAT/116Exercise Week 4 Concept CheckAxia College of University of PhoenixLavora MosesInstructor: Mrs. Jenell SargentDate: January 13, 2012Explain in your own words why the line x = 4 is a vertical line.By looking at the equation you are able to tel
University of Phoenix - BUS - 210
Associate Level MaterialAppendix DLandscape DesignLandscape designers often use coordinate geometry and algebra as they help their clients. In manyregions, landscape design is a growing field. With the increasing popularity of do-it-yourself televisio
University of Phoenix - BUS - 210
Alphabetic FilingLavora MosesAxia College of University of PhoenixInstructor: Kimberly Kirby-BassHCR/210Robert D. AngeloNorman Bailey JonesNorman Bailey-JonesRobert DAngeloRobert DeAngeloJudith Dela CroixJudith delaCroixFranklin M. HaneyFrank
University of Phoenix - BUS - 210
MAT/116Introduction to FunctionsAxia College of University of PhoenixLavora MosesInstructor: Mrs. Jenell SargentDate: January 20, 2012
University of Phoenix - BUS - 210
Associate Level MaterialAppendix ENumeric FilingList the following set of numbers within the table below according to:Straight numeric orderMiddle-digit orderTerminal-digit order535-11-38536-01-38535-01-38534-10-38534-10-36534-01-38600-11-37
University of Phoenix - BUS - 210
Legal TermsLavora MosesAxia College of University of PhoenixInstructor: Kimberly Kirby-BassHCR/2101) Assault- Is when someone threatens or attempts to harm someone else.2) Breach of Confidentiality- Is when information has been disclosed without the
University of Phoenix - BUS - 210
Record OrganizationLavora MosesAxia College of University of PhoenixInstructor: Kimberly Kirby-BassHCR/210I have identified that there are some differences and similarities among small, medium,and large facilities with the organization of patient fi
University of Phoenix - MAT - 116
MAT/116ResponseAxia College of University of PhoenixLavora MosesInstructor: Mrs. Jenell SargentDate: January 27, 2012How can you determine if two lines are perpendicular?If the lines are perpendicular to each other, the product of their slopes equa
University of Phoenix - BUS - 210
Record ControlLavora MosesAxia College of University of PhoenixInstructor: Kimberly Kirby-BassHCR/210What conclusions can you draw about similarities and differences in circulation, trackingand security measures for records handling and storage with
University of Phoenix - BUS - 210
Internet DatabasesLavora MosesAxia College of University of PhoenixInstructor: Kimberly Kirby-BassHCR/210There are several pros and cons with medical information being electronically stored inan internet database, which does not limit the use of doc
University of Phoenix - HTT - 200
WEEK 5 CHECKPOINTThe conventional lodging customer in 2007 is actually divided between leisure and business,becoming 44% business in addition to 56% leisure. The actual report then stopped workingwho, where, just how much, how many times, the reason wh
University of Phoenix - HTT - 200
WEEK 1 CHECKPOINTThe work possibility in the hospitality area I've found fascinating may be the one I've beenperforming for fifteen years, as being a local travel agent. This job continues to interest mepersonally simply because every day differs from
University of Phoenix - HTT - 200
WEEK 2 ASSIGNMENTDuring my assessment of Raleigh, North Carolina plus in response to the queries regarding thecity where I reside, make sure you start to see the answers listed below. I've included how Ibelieve the most important developments affect th
University of Phoenix - HTT - 200
WEEK 2 CHECKPOINTThe key components and trends of diversity tend to be enhanced different population, thevolume of women versus men working, transforming family structure, and modifications inearnings contributors. The consequences the alterations are
University of Phoenix - HTT - 200
WEEK 3 CHECKPOINTa. Mimis Caf theme represents home style cooking food along with family style restaurant witha brand new Orleans inspired environment. Reception menus is of simple products lots ofpeople would prepare food at home. The interior is attr
University of Phoenix - HTT - 200
WEEK 4 ASSIGNMENTWhile evaluating the 8 Factors to consider in a Franchise on the internet site using theSeven Fundamental Questions for any Potential Franchisee in the publication, a number ofresemblances and variations were noticed between the two. E
University of Phoenix - HTT - 200
WEEK 4 CHECKPOINTThere are several benefits and drawbacks to each chain restaurants and independentrestaurants. Selecting from the list provided, I've chose to contrast and compare access tofunds, site selection, and also new service growth.Initially,
Bergen Community College - ECON - 201
Economics 102-02Spring 2012Homework #1Due February 8, 2012Evelyn Tavares1. United States ProductionThe following production possibilities table data represent the amount of apples andoranges grown in the United States in an hour.ABCDEApples2
Bergen Community College - ECON - 201
Economics 102-02Spring 2012Homework #2Due February 27, 2012Evelyn TavaresComplete the following questions from the McConnell &amp; Brue (GREEN) text.Chapter 3 Page 64 Question #9 parts g and h onlyChapter 6 Page 132 Questions #9, 109g - Price up, quan
Bergen Community College - ECON - 201
Economics 102-02Spring 2012Homework #2Due February 27, 2012NO LATE ASSIGNMENTS WILL BE ACCEPTED.Complete the following questions from the McConnell &amp; Brue (GREEN) text.Chapter 3 Page 64 Question #9 parts g and h onlyChapter 6 Page 132 Questions #9,
Bergen Community College - BUSINESS - 101
1. Ranch owner who operated cat-breeding business brought a Federal 1983 (invasion ofprivacy protection for California citizens) action against newspaper and its reporter for allegedFourth Amendment violation arising when the reporter, at the invitation
Bergen Community College - BUSINESS - 101
Chapter 28a.b.c.d.e.f.g.LimitedLimitedOriginalAppellateLimitedLimitedAppellate10. Yes. Once Mostek Corp. fails to deliver the final installment and since a statement saying thatany dispute arising from an order would be submitted to arbitr
Bergen Community College - BUSINESS - 101
Business Law IChapter 3 Assignment AnswersStudent: Evelyn Tavares2. Ann Elkin, who works for Brill Co., has been sent out to conduct two customersevaluations, which have gone much more quickly than Ann anticipated. Hersupervisor does not expect Ann b
Bergen Community College - BUSINESS - 101
Evelyn TavaresAssignment 4 Answers2- The State cannot charge, because the Commerce Clause protects companies from been taxedover every state.7- Its unconstitutional. Because it violates the Privileges and Immunities clause.11- Yes, because according
Bergen Community College - BUSINESS - 101
Business Law - Answers Chapter 5Evelyn Tavares5- No, because Price discrimination only occurs when the prices charged from buyers aredifferent despite the same marginal costs, and in this case it did not happen. This case does notinvolve different pri
Bergen Community College - BUSINESS - 101
Evelyn TavaresBusiness Law Chapter 6 Assignment Answers#'s 2, 5, 10, 132. It could appeal to the judicial branch on the basis of procedural issues, an agency cannot passregulations without studies, comments, and hearings. The Agency limitation of 1 mi
Bergen Community College - BUSINESS - 101
Evelyn TavaresBusiness Law I Chapter 7 Assignment Answers4- I think that once the licenses were obtained in reliance on the purchasers (Sadler) writtenassurance that the goods would not be disposed of contrary to the export license. The U.SRegulations
Bergen Community College - BUSINESS - 101
Evelyn TavaresBusiness Law 101Chapter 8 Assignment Answers3. Yes, there is enough intent and action for a crime. If the police had not arrived in time, Barkerand the others would have finished the action, taking all the merchandise; although I wouldp
Bergen Community College - BUSINESS - 101
3. Yes. Larceny is a taking of personal property from the owner with the intent to deprive theowner of that property. Leaving the store is often proof of intent; but, it is not the only proof. Theuse of the torch and moving the items in this case shows
Bergen Community College - BUSINESS - 101
Business Law IChapter 9 Assignment AnswersEvelyn Tavares2. The case will be dismissed on the ground that it was not liable for defamation under the FirstAmendment because no reasonable person would have interpreted the caption as an allegationthat Ev
Bergen Community College - BUSINESS - 101
2. Although the word pimp may be reasonably capable of a defamatory meaning when read inisolation, we agree with the district courts assessment that the term loses its meaning whenconsidered in the context of this case where it appeared among other phot
Bergen Community College - BUSINESS - 101
Business Law IChapter 12Assignment AnswersEvelyn Tavares6, 9, 10, 14, 156. No, it is not a valid defense. The contract was binding without a seal. However, oral offer andacceptance is enough to give rise to a contract.9. Yes, since he was aware of
Bergen Community College - BUSINESS - 101
6. A contract to build a house is binding without a seal.Generally speaking, a seal is never necessary to make a binding contract. If there is a seal, itspresence may have some effect in certain situations. For example, in many states the sealedcontrac
Bergen Community College - BUSINESS - 101
Business Law IFall/2011Chapter 13 Assignment AnswersEvelyn Tavares3, 4, 6, 8, 9, 10, 113. No. The letter was not revoked by Katherine, because when Paul accepted the offer, he had noidea that Katherine intended to revoke the offer. A letter on mail
Bergen Community College - BUSINESS - 101
3. No. A revocation is not effective until it is communicated to the offeree. In the case of a mailedrevocation, this requires actual receipt by the offeree of the revoking letter. Unlike a letter ofacceptance, the letter of revocations is not effective
Bergen Community College - BUSINESS - 101
Business Law I/Fall 2011Chapter 14 Assignment AnswersEvelyn Tavares1. Definitely yes. Once the seller didnt know the cars history, he shouldnt give the statementattesting the car was not involved in a wreck. The seller was guilty of fraud, even if he
Bergen Community College - BUSINESS - 101
1. Yes. The salesperson making the statement that there were no prior wrecks did not knowwhether that statement was true or false. Nevertheless, the salesperson made the statement asthough it were true and as though he knew it were true. This constitute
Bergen Community College - BUSINESS - 101
Business Law IChapter 15 Assignment AnswersEvelyn Tavares1. No. Although she was probably under moral obligation to reward her neighbor, it does notconstitute consideration, so it is not enforceable.4. Past consideration is no consideration. Stan pre
Bergen Community College - BUSINESS - 101
1. No. The fact that a person is under a moral obligation to pay for a particular service does notconstitute consideration so as to make binding a promise to pay for the service.4. The general rule is that past consideration is no consideration. In this
Bergen Community College - BUSINESS - 101
Business Law - IChapter 16 - Assignment AnswersEvelyn Tavares2. No, U.S. West Direct contract was made on the take-it-or-leave-it basis, therefore an adhesioncontract and there was a lack of mutuality.5. There was some solicitation in the letter. Eve
Bergen Community College - BUSINESS - 101
Chapter 17 Assignment AnswersEvelyn Tavares2. Yes, just because the letter was not a formal written contract and signed by both parties, doesnot make it less valid. The contract was not merely oral, so the written letter should satisfy thestatute of f
Bergen Community College - BUSINESS - 101
Business Law IChapter 18 Assignment AnswersEvelyn Tavares3.No.Therightassignedcalledfortheperformanceofpersonalservices,sotherightcantbeassignedwithouttheconsentofthepersonthatwastoprovidethepersonalservices.5.No.Becausethetransactioninvolvesathirdp
Bergen Community College - BUSINESS - 101
3. No. The right assigned called for the performance of personal services. Such a right cannot beassigned without the consent of the person who is to render the personal services. This isparticularly true in this case, because this type of assignment, i
Bergen Community College - BUSINESS - 101
Business Law I/ Fall 2011Chapter 19 Assignment AnswersEvelyn Tavares4. No. The contract should not be discharged by Samets death. The fact that their designer doesnot mean they can cancel contract. The Metalcrafters still should be able to develop the
Bergen Community College - BUSINESS - 101
Business Law I/ Fall 2011Chapter 20Assignment AnswersEvelyn Tavares2. Yes, she can recover the amount. Because Anthony failed to perform the contract and since itwas on the contract, that he would pay Laura \$ 5,000 as liquidated damages, it is her ri
Bergen Community College - BUSINESS - 101
2. She can recover the \$5,000. The value of a work of art cannot be determined exactly.Moreover, a work of art cannot be resold immediately when the buyer breaks the contract. Asubstantial period of time could elapse before the seller could make a resal
Bergen Community College - BUSINESS - 101
Business Law IFall 2011Chapter 37 - Assignment AnswersEvelyn Tavares4. No. The discharge of the agent by the principal in this case was a violation of their contract.Jones is liable to Stayword. Jones is correct when he states that the principal has
Bergen Community College - BUSINESS - 101
Business Law IChapter 38 Assignment AnswersEvelyn Tavares5. No. Mills will not be held vicarious liability theory. Under the respondeat superior rule, theemployer is liable only for those torts committed within the scope of employment. In this case,S