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Mba 264

In: Business and Management

Submitted By huykle
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Problem 8.
> library(ISLR)
> attach(College)
> college <- read.csv("//Users/HuyKLe/Desktop/College.csv", header = TRUE, sep = ",")
> head(college[,1:5]) X Private Apps Accept Enroll
1 Abilene Christian University Yes 1660 1232 721
2 Adelphi University Yes 2186 1924 512
3 Adrian College Yes 1428 1097 336
4 Agnes Scott College Yes 417 349 137
5 Alaska Pacific University Yes 193 146 55
6 Albertson College Yes 587 479 158
> rownames <- college[,1]
> college <- college[,-1]
> summary(college) Private Apps Accept Enroll Top10perc Top25perc No :212 Min. : 81 Min. : 72 Min. : 35 Min. : 1.00 Min. : 9.0 Yes:565 1st Qu.: 776 1st Qu.: 604 1st Qu.: 242 1st Qu.:15.00 1st Qu.: 41.0 Median : 1558 Median : 1110 Median : 434 Median :23.00 Median : 54.0 Mean : 3002 Mean : 2019 Mean : 780 Mean :27.56 Mean : 55.8 3rd Qu.: 3624 3rd Qu.: 2424 3rd Qu.: 902 3rd Qu.:35.00 3rd Qu.: 69.0 Max. :48094 Max. :26330 Max. :6392 Max. :96.00 Max. :100.0 F.Undergrad P.Undergrad Outstate Room.Board Books Min. : 139 Min. : 1.0 Min. : 2340 Min. :1780 Min. : 96.0 1st Qu.: 992 1st Qu.: 95.0 1st Qu.: 7320 1st Qu.:3597 1st Qu.: 470.0 Median : 1707 Median : 353.0 Median : 9990 Median :4200 Median : 500.0 Mean : 3700 Mean : 855.3 Mean :10441 Mean :4358 Mean : 549.4 3rd Qu.: 4005 3rd Qu.: 967.0 3rd Qu.:12925 3rd Qu.:5050 3rd Qu.: 600.0 Max. :31643 Max. :21836.0 Max. :21700 Max. :8124 Max. :2340.0 Personal PhD Terminal S.F.Ratio perc.alumni Min. : 250 Min. : 8.00 Min. : 24.0 Min. : 2.50 Min. : 0.00 1st Qu.: 850 1st Qu.: 62.00 1st Qu.: 71.0 1st Qu.:11.50 1st Qu.:13.00 Median :1200 Median : 75.00 Median : 82.0 Median :13.60 Median :21.00 Mean :1341 Mean : 72.66 Mean : 79.7 Mean :14.09 Mean :22.74 3rd Qu.:1700 3rd Qu.: 85.00 3rd Qu.: 92.0 3rd Qu.:16.50 3rd Qu.:31.00 Max. :6800 Max. :103.00 Max. :100.0 Max. :39.80 Max. :64.00 Expend Grad.Rate Min. : 3186 Min. : 10.00 1st Qu.: 6751 1st Qu.: 53.00 Median : 8377 Median : 65.00 Mean : 9660 Mean : 65.46 3rd Qu.:10830 3rd Qu.: 78.00 Max. :56233 Max. :118.00
> pairs(college[,1:10])
>
> boxplot(college$Outstate, college$Private, names = c("Outstate","Private"))
> Elite=rep("No",nrow(college ))
> > Elite[college$Top10perc >50]=" Yes"
Error: unexpected '>' in ">"
> > Elite=as.factor(Elite)
Error: unexpected '>' in ">"
> > college=data.frame(college , Elite)
Error: unexpected '>' in ">"
> Elite=rep("No",nrow(college ))
> Elite[college$Top10perc >50]=" Yes"
> Elite=as.factor(Elite)
> college=data.frame(college , Elite)
> summary(college) Private Apps Accept Enroll Top10perc Top25perc No :212 Min. : 81 Min. : 72 Min. : 35 Min. : 1.00 Min. : 9.0 Yes:565 1st Qu.: 776 1st Qu.: 604 1st Qu.: 242 1st Qu.:15.00 1st Qu.: 41.0 Median : 1558 Median : 1110 Median : 434 Median :23.00 Median : 54.0 Mean : 3002 Mean : 2019 Mean : 780 Mean :27.56 Mean : 55.8 3rd Qu.: 3624 3rd Qu.: 2424 3rd Qu.: 902 3rd Qu.:35.00 3rd Qu.: 69.0 Max. :48094 Max. :26330 Max. :6392 Max. :96.00 Max. :100.0 F.Undergrad P.Undergrad Outstate Room.Board Books Min. : 139 Min. : 1.0 Min. : 2340 Min. :1780 Min. : 96.0 1st Qu.: 992 1st Qu.: 95.0 1st Qu.: 7320 1st Qu.:3597 1st Qu.: 470.0 Median : 1707 Median : 353.0 Median : 9990 Median :4200 Median : 500.0 Mean : 3700 Mean : 855.3 Mean :10441 Mean :4358 Mean : 549.4 3rd Qu.: 4005 3rd Qu.: 967.0 3rd Qu.:12925 3rd Qu.:5050 3rd Qu.: 600.0 Max. :31643 Max. :21836.0 Max. :21700 Max. :8124 Max. :2340.0 Personal PhD Terminal S.F.Ratio perc.alumni Min. : 250 Min. : 8.00 Min. : 24.0 Min. : 2.50 Min. : 0.00 1st Qu.: 850 1st Qu.: 62.00 1st Qu.: 71.0 1st Qu.:11.50 1st Qu.:13.00 Median :1200 Median : 75.00 Median : 82.0 Median :13.60 Median :21.00 Mean :1341 Mean : 72.66 Mean : 79.7 Mean :14.09 Mean :22.74 3rd Qu.:1700 3rd Qu.: 85.00 3rd Qu.: 92.0 3rd Qu.:16.50 3rd Qu.:31.00 Max. :6800 Max. :103.00 Max. :100.0 Max. :39.80 Max. :64.00 Expend Grad.Rate Elite Min. : 3186 Min. : 10.00 Yes: 78 1st Qu.: 6751 1st Qu.: 53.00 No :699 Median : 8377 Median : 65.00 Mean : 9660 Mean : 65.46 3rd Qu.:10830 3rd Qu.: 78.00 Max. :56233 Max. :118.00
> summary(college$Elite) Yes No 78 699
> plot(college$Elite, college$Outstate)
> par(mfrow=c(2,2))
> hist(college$Outstate)
> hist(college$Books)
> hist(college$PhD)
> summary(college$Books) Min. 1st Qu. Median Mean 3rd Qu. Max. 96.0 470.0 500.0 549.4 600.0 2340.0
> summary(college$PhD) Min. 1st Qu. Median Mean 3rd Qu. Max. 8.00 62.00 75.00 72.66 85.00 103.00
> summary(college$Outstate) Min. 1st Qu. Median Mean 3rd Qu. Max. 2340 7320 9990 10440 12920 21700

Problem 9. a) Origin, year, name are qualitative.
Mpg, cylinders, displacement, horsepower, weight, acceleration are quantitative.
> library(ISLR)
> data(Auto)
> summary(Auto) mpg cylinders displacement horsepower weight Min. : 9.00 Min. :3.000 Min. : 68.0 Min. : 46.0 Min. :1613 1st Qu.:17.00 1st Qu.:4.000 1st Qu.:105.0 1st Qu.: 75.0 1st Qu.:2225 Median :22.75 Median :4.000 Median :151.0 Median : 93.5 Median :2804 Mean :23.45 Mean :5.472 Mean :194.4 Mean :104.5 Mean :2978 3rd Qu.:29.00 3rd Qu.:8.000 3rd Qu.:275.8 3rd Qu.:126.0 3rd Qu.:3615 Max. :46.60 Max. :8.000 Max. :455.0 Max. :230.0 Max. :5140 acceleration year origin name Min. : 8.00 Min. :70.00 Min. :1.000 amc matador : 5 1st Qu.:13.78 1st Qu.:73.00 1st Qu.:1.000 ford pinto : 5 Median :15.50 Median :76.00 Median :1.000 toyota corolla : 5 Mean :15.54 Mean :75.98 Mean :1.577 amc gremlin : 4 3rd Qu.:17.02 3rd Qu.:79.00 3rd Qu.:2.000 amc hornet : 4 Max. :24.80 Max. :82.00 Max. :3.000 chevrolet chevette: 4 (Other) :365

b) > range(Auto$mpg)
[1] 9.0 46.6
> range(Auto$cylinders)
[1] 3 8
> range(Auto$displacement)
[1] 68 455
> range(Auto$horsepower)
[1] 46 230
> range(Auto$weight)
[1] 1613 5140
> range(Auto$acceleration)
[1] 8.0 24.8

c) > mean(Auto$mpg)
[1] 23.44592
> mean(Auto$cylinders)
[1] 5.471939
> mean(Auto$displacement)
[1] 194.412
> mean(Auto$horsepower)
[1] 104.4694
> mean(Auto$weight)
[1] 2977.584
> mean(Auto$acceleration)
[1] 15.54133

> sd(Auto$mpg)
[1] 7.805007
> sd(Auto$cylinders)
[1] 1.705783
> sd(Auto$displacement)
[1] 104.644
> sd(Auto$horsepower)
[1] 38.49116
> sd(Auto$weight)
[1] 849.4026
> sd(Auto$acceleration)
[1] 2.758864
d) > range(Auto1$mpg)
[1] 9.0 46.6
> range(Auto1$cylinders)
[1] 3 8
> range(Auto1$displacement)
[1] 68 455
> range(Auto1$horsepower)
[1] 46 230
> range(Auto1$weight)
[1] 1613 5140
> range(Auto1$acceleration)
[1] 8.0 24.8

> mean(Auto1$mpg)
[1] 23.44592
> mean(Auto1$cylinders)
[1] 5.471939
> mean(Auto1$displacement)
[1] 194.412
> mean(Auto1$horsepower)
[1] 104.4694
> mean(Auto1$weight)
[1] 2977.584
> mean(Auto1$acceleration)
[1] 15.54133

> sd(Auto1$mpg)
[1] 7.805007
> sd(Auto1$cylinders)
[1] 1.705783
> sd(Auto1$displacement)
[1] 104.644
> sd(Auto1$horsepower)
[1] 38.49116
> sd(Auto1$weight)
[1] 849.4026
> sd(Auto1$acceleration)
[1] 2.758864

e) > plot(Auto$cylinders, Auto$mpg)
> plot(Auto$cylinders, Auto$horsepower)
> plot(Auto$cylinders, Auto$weight)
> plot(Auto$cylinders, Auto$Displacement)
> hist(mpg)
Error in hist(mpg) : object 'mpg' not found
> hist(Auto$mpg))
Error: unexpected ')' in "hist(Auto$mpg))"
> hist(Auto$mpg)
> hist(Auto$cylinders)
> hist(Auto$horsepower)
> hist(Auto$weight)
> hist(Auto$displacement)

f) Looking at the plot of mpg vs cylinders, I found that as the cylinders increase, mpg will decrease.

Problem 10.

a) > library(MASS)
> attach(Boston)
> ?Boston

The Boston data frame has 506 rows and 14 columns.
The rows and columns represent crim per capita crime rate by town. zn proportion of residential land zoned for lots over 25,000 sq.ft. indus proportion of non-retail business acres per town. chas Charles River dummy variable (= 1 if tract bounds river; 0 otherwise). nox nitrogen oxides concentration (parts per 10 million). rm average number of rooms per dwelling. age proportion of owner-occupied units built prior to 1940. dis weighted mean of distances to five Boston employment centres. rad index of accessibility to radial highways. tax full-value property-tax rate per \$10,000. ptratio pupil-teacher ratio by town. black 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town. lstat lower status of the population (percent). medv median value of owner-occupied homes in \$1000s.

b) pairs(~crim+zn,Boston)
> pairs(~crim+zn+indus+chas+nox+rm+dis+rad+tax+black+lstat+medv,Boston)

c) Per capita crime rate is associated with the median value of owner-occupied homes. Per capita crime rate increases => median value of owner-occupied homes decreases.

d) > range(Boston$crim)
[1] 0.00632 88.97620
> range(Boston$tax)
[1] 187 711
> range(Boston$ptratio)
[1] 12.6 22.0
The ranges are widespread which indicate some of the areas have a higher crime rate, higher tax and pupil-to-teacher ratio, but no areas have a particular higher rate compared to others.

e) > sum(Boston$chas)
[1] 35

35 of the suburbs in the data set are bound by the Charles river.

f) > median(Boston$ptratio)
[1] 19.05
The pupil-to-teacher ratio is 19.05

g) > sum(Boston$rm > 7)
[1] 64
> sum(Boston$rm > 8)
[1] 13
Suburbs that average more than 7 room per dwelling: 64
Suburbs that average more than 8 room per dwelling: 13

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...University of International Business & Economics School of Business MBA Fall Course MARKETING MANAGEMENT Syllabus 营销管理教学大纲 授课时间 2011年9月-2012年1月 教 师 熊 伟 学 生 Full-time MBA 2011 E-mail 发布课件 mkt_mba_full@126.com 提交作业 mkt_mba_xw@126.com Tel 10-64494372 1. Outline and Objectives Marketing Management is a core subject in the business program, and it has been designed for those students majoring in Marketing, as well as those taking this course as their formal, academic venture into this discipline. Participants in the program would be expected to recognize that marketing is a total system of business action and should be seen as the key element in the running of any business. This course could enable students to better appreciate the role of marketing in individual firms as well as in the wider community by exposing them to theories and basic concepts. Upon completion of this course, it is expected that students should: be able to identify and explain the important concepts in marketing; understand how marketing integrates with other areas of business, especially in a dynamic business environment; be able to apply these concepts in case situations, particularly in a Chinese context and, therefore, to enhance competence in the analysis of real world’s marketing; develop the ability to confidently and meaningfully analyze marketing problems; be equipped with...

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