PORTFOLIO DATASET

Portfolio Dataset

Portfolio Dataset

Following are the list of (a) categorical variables (b) ordinal variables (c) quantitative variables (d) interval and ratio scale.

Categorical Variables

Ordinal variables

Quantitative variables

Interval

Ratio Scale

Hair

(Curly, straight, silky, etc...)

Pesticide Levels (High, medium and low)

Siblings

annual income

Height (133.56cms, 121.54cms, etc…)

Ball Colour (Red, Green, Blue, etc…)

Injury Scale (0, 1, 2, …)

AGE WHEN FIRST MARRIED

Weights (0-2gms, 2-10gms, etc…)

Dotdata (1.2,

1.3,

2.99, etc…)

Gender (Male and Female)

Page information (poor, ok, good, etc…)

Age

Field of study (Engineering, Medical, etc…)

Speed

College attended

Area(Acres, square miles, square feet)

Political Affiliation

Weight(Pounds, tons, ounces, grams)

Status of disease infection

Height(Inches, feet, centimetres)

Crops (Wheat, Barley, etc…)

Irrigation Methods (Furrow, dry land, etc…)

The above graph is between the alcohol brand and percent of alcohol that brand uses. It can be seen that Red Hook IPA has the highest alcohol percent i.e. 6.5, whereas O'Doul's uses a very less amount of alcohol in their brand i.e. 0.4. While the average alcohol that a brand use is approximately 4.8 which is quite moderate.

Alcohol Percentage

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

0.1%

1

1.4

1.4

1.4

1.1%

1

1.4

1.4

2.8

1.2%

15

21.1

21.1

23.9

1.3%

9

12.7

12.7

36.6

1.4%

16

22.5

22.5

59.2

1.5%

14

19.7

19.7

78.9

1.6%

4

5.6

5.6

84.5

1.7%

9

12.7

12.7

97.2

1.8%

1

1.4

1.4

98.6

1.9%

1

1.4

1.4

100.0

Total

71

100.0

100.0

The graph above is the combination between Alcohol brand and the calories they use in their brand; it shows that Sam Adams Cream Stout uses the maximum number of calories i.e. 195 and on the contrary O'Doul's alcohol Brand use only 70 calories to make their alcohol. On average it can be said that the alcohol companies use 139.3 calories to make their product.

The chart above shows the combination between Brand and Carbohydrates; on average a company use approximately 11 carbohydrates to make their alcohol. The highest numbers of carbohydrates were found in Sam Adams Cream Stout i.e. 23.9, while Blatz Beer and Michelob Ultra use only 2.6 carbohydrates.

The Pearson Coefficient

Correlations

AlcoholPercent

Calories

Carbohydrates

AlcoholPercent

Pearson Correlation

1

.738**

.251*

Sig. (1-tailed)

.000

.017

N

71

71

71

Calories

Pearson Correlation

.738**

1

.812**

Sig. (1-tailed)

.000

.000

N

71

71

71

Carbohydrates

Pearson Correlation

.251*

.812**

1

Sig. (1-tailed)

.017

.000

N

71

71

71

The above table shows the correlation between Alcohol Percent, Calories and Carbohydrates. The magnitude of Pearson correlation among alcohol and calories i.e. 0.738 suggest that there is a positive and strong relationship between the two elements; the significant value says that the results are significant and both the variables have strong impact on each other. The relationship between Carbohydrates and Alcohol percent is positive and strong; the magnitude of 0.251 suggests that relationship is strong enough which is backed by the significant values as it is less 0.05 level of significance. The highest relationship in terms of Pearson coefficient has shown by Carbohydrates and Calories i.e. 0.812, it shows that there is a positive and very strong relationship between these two elements.

The scatter plots between calories and Alcohol Percent shows a linear trend and there is a straight line which posses all the normality assumptions, it can be said that the data is normally distributed. Whenever the calories are increased alcohol percent will be increased. A scatter plot represents the actual data values ??with respect to the values ??predicted by the model. It also features a line that illustrates the perfect prediction, in which the expected value exactly matches the actual value.

Data

Frequency

Cumulative Frequency

Relative Frequency

Relative Cumulative Frequency

98

1

1

0.0204082

0.02040816

87

1

2

0.0408163

0.06122449

85

1

3

0.0612245

0.12244898

82

1

4

0.0816327

0.20408163

78

1

5

0.1020408

0.30612245

77

1

6

0.122449

0.42857143

67

2

8

0.1632653

0.59183673

65

2

10

0.2040816

0.79591837

54

1

11

0.2244898

1.02040816

50

1

12

0.244898

1.26530612

45

5

17

0.3469388

1.6122449

40

1

18

0.3673469

1.97959184

36

3

21

0.4285714

2.40816327

34

2

23

0.4693878

2.87755102

32

1

24

0.4897959

3.36734694

30

2

26

0.5306122

3.89795918

29

1

27

0.5510204

4.44897959

28

2

29

0.5918367

5.04081633

25

2

31

0.6326531

5.67346939

23

1

32

0.6530612

6.32653061

20

1

33

0.6734694

7

14

1

34

0.6938776

7.69387755

11

1

35

0.7142857

8.40816327

10

1

36

0.7346939

9.14285714

9

1

37

0.755102

9.89795918

8

1

38

0.7755102

10.6734694

6

4

42

0.8571429

11.5306122

5

3

45

0.9183673

12.4489796

4

1

46

0.9387755

13.3877551

3

2

48

0.9795918

14.3673469

1

1

49

1

15.3673469

Mean

Median

Mode

Variance

Range

Standard deviation

37.2903

30

45

866.2129

97

29.4315

Regression Analysis

Regression is a method of data analysis of the economic reality that serves to highlight the relationships between ...