Quantitative Management Techniques in Logistics



Quantitative Management Techniques in Logistics

Quantitative methods

Studies employing quantitative methods in the field of ecotourism can be classified into two broad categories. First, there are studies that examine essentially demographic data through the use of descriptive statistics, typically with the objective of characterizing the economic and social environment of a host population or target market. Second, more comprehensive studies examine complex relationships and cause-and-effect associations between variables describing the hosts or markets and their behavior. These latter studies often employ factor-cluster segmentation procedures, or other simpler forms of clustering procedures (Aeero, 1994). They typically include different statistical tests for examining the differences between the groups obtained. The tests observed were chi-squared, t-test, and analysis of variance. In addition, some studies used multiple regressions to model the variables explaining certain behavioral patterns.

Question 1

Descriptive Statistics

Mean

Std. Deviation

N

Sales

1.6878E2

85.38775

26

Reps

50.0769

12.93344

26

Brands

8.5385

2.26682

26

Adspend

5.3731

1.88882

26

Crime Index

9.3846

4.39160

26

Correlations

Sales

Reps

Brands

Adspend

Crime Index

Pearson Correlation

Sales

1.000

.591

-.697

.166

.387

Reps

.591

1.000

-.147

.103

.457

Brands

-.697

-.147

1.000

-.182

-.002

Adspend

.166

.103

-.182

1.000

-.158

Crime Index

.387

.457

-.002

-.158

1.000

Sig. (1-tailed)

Sales

.

.001

.000

.208

.025

Reps

.001

.

.236

.309

.010

Brands

.000

.236

.

.186

.497

Adspend

.208

.309

.186

.

.221

Crime Index

.025

.010

.497

.221

.

N

Sales

26

26

26

26

26

Reps

26

26

26

26

26

Brands

26

26

26

26

26

Adspend

26

26

26

26

26

Crime Index

26

26

26

26

26

Model Summaryc

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1

df2

Sig. F Change

1

.854a

.729

.706

46.30649

.729

31.003

2

23

.000

2

.874b

.763

.718

45.35397

.034

1.488

2

21

.249

a. Predictors: (Constant), Brands, Reps

b. Predictors: (Constant), Brands, Reps, Adspend, Crime Index

c. Dependent Variable: Sales

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

203.957

54.936

3.713

.001

Reps

3.299

.724

.500

4.556

.000

Brands

-23.465

4.131

-.623

-5.680

.000

2

(Constant)

190.524

61.827

3.082

.006

Reps

2.623

.812

.397

3.233

.004

Brands

-23.717

4.108

-.630

-5.773

.000

Adspend

1.997

5.025

.044

.397

.695

Crime Index

4.119

2.388

.212

1.725

.099

a. Dependent Variable: Sales

Question 2

Descriptive Statistics

Mean

Std. Deviation

N

y

7.0771

6.86242

28

x

78.8286

131.23091

28

Correlations

y

x

Pearson Correlation

y

1.000

-.567

x

-.567

1.000

Sig. (1-tailed)

y

.

.001

x

.001

.

N

y

28

28

x

28

28

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1

df2

Sig. F Change

1

.567a

.321

.295

5.76168

.321

12.302

1

26

.002

a. Predictors: (Constant), x

b. Dependent Variable: y

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

408.385

1

408.385

12.302

.002a

Residual

863.121

26

33.197

Total

1271.506

27

a. Predictors: (Constant), x

b. Dependent Variable: y

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

9.413

1.276

7.375

.000

x

-.030

.008

-.567

-3.507

.002

a. Dependent Variable: y

Question 3

 

exponential

logistic

time

model

model

1

1.0

1.0

2

1.3

1.3

3

1.8

1.8

4

2.5

2.3

5

3.3

3.0

6

4.5

3.8

7

6.0

4.8

8

8.2

6.0

9

11.0

7.3

10

14.9

8.8

11

20.1

10.3

12

27.1

11.8

13

36.6

13.2

14

49.4

14.4

15

66.7

15.6

16

90.0

16.5

17

121.5

17.3

18

164.0

17.9

19

221.4

18.4

20

298.9

18.8

21

403.4

19.1

22

544.6

19.3

23

735.1

19.5

24

992.3

19.6

25

1339.4

19.7

26

1808.0

19.8

27

2440.6

19.8

28

3294.5

19.9

29

4447.1

19.9

30

6002.9

19.9

 

exponential

logistic

N

growth rate

growth rate

0

0

0

1

0.3

0.285

2

0.6

0.54

3

0.9

0.765

4

1.2

0.96

5

1.5

1.125

6

1.8

1.26

7

2.1

1.365

8

2.4

1.44

9

2.7

1.485

10

3

1.5

11

3.3

1.485

12

3.6

1.44

13

3.9

1.365

14

4.2

1.26

15

4.5

1.125

16

4.8

0.96

17

5.1

0.765

18

5.4

0.54

19

5.7

0.285

20

6

0

21

6.3

-0.315

22

6.6

-0.66

23

6.9

-1.035

24

7.2

-1.44

Quantitative approaches to the study of language draw numerically-based comparisons between different types of language use: for instance, they may look at the frequency with which certain linguistic forms are used across speakers, groups of speakers, texts or text types. Quantitative approaches use descriptive statistics and inferential statistics for data analysis (Babbie, 1995).

Quantitative approaches are associated particularly with variationist sociolinguistics in the tradition inspired by William LABOV so much so that the terms quantitative sociolinguistics or the quantitative paradigm are found for this tradition. For instance, in his study of the language of New York City (e.g. Labov, 1966, 1972a) Labov was able to identify systematic patterns in the frequency distribution of certain pronunciation features across different social groups and speaking styles (Barron, 1998).

A quantitative approach informs other sociolinguistic traditions, for example quantitative patterns have been identified in the use of interactional features such as interruptions. It is also typical for much work in corpus linguistics (researchers have analysed corpora of several million words to identify systematic patterns of usage across a range of text types; see e.g. ...