**Table from 2 to 20 Chart**

There are many data analysis charts that you could use as examples in different domains, but it is very important to explore this kind of chart that can help us to understand the relationship between two variables, or to explore if there exists a trend or a correlation between two or more variables

“An important factor when selecting data analysis charts for your analysis is to select one that shows clear relationships among the variables. The best choice will be one with all three types of correlations: linear, quadratic, and nonlinear (if the variables being analyzed consist of several independent variables). In general, any type of “cluster” or “barplot” would do the trick. However, if the relationships are not clearly obvious, don’t get so hung up on finding them. Instead, focus on what might be possible in these relationships: that they may support some conclusions about the relationship between the variables. It’s common to find correlation coefficients of 0.7 to 1.0, which means the correlation coefficients are roughly similar in direction to the average correlation coefficient. Some research has shown that this difference usually is insignificant in determining an association between independent variables, so using a cluster-based model is recommended.”

Clustering is the process by which the data that we have is distributed into groups based on similarity and other characteristics. Clustering is useful not only for identifying different segments of the data but also for explaining relationships among those segments. Each group contains observations that share common characteristics that link those observations together. An obvious method for clustering data is to use the k-means algorithm, where each data point is assigned to a centroid, the coordinates of which point from a set that describes the majority of the data points.

Examples of clustering (a) random vs. ordered grouping. (b) clusters vs. single distribution; (c) data points divided into multiple clusters or classes, which can easily be identified with clustering.

Example showing how to visualize the different data points. The data shown at (a) is plotted on the same axis as the data shown at (b), and both graphs represent a single data point, while the second graph is a mixture of data points representing separate classes. (c) the clusters at K are represented based on the first data point in each cluster. For this reason, K is specified in the code above.

In this experiment, we are going to create a barplot to evaluate the relationship between variables. We can make a barplot or scatterplot if we choose to do a regression analysis, but in this case, we will go with a barplot. A bar plot is a great way to examine what kind of relationship a given variable has with another variable. When we use variables such as age, gender, and salary, there is no need for a line that is created by creating the formula above to plot out the dependent variable on the x-axis. If the relationship has more than two independent variables, the line becomes a triangle. The area under the graph is the total amount. But this graph does not give us information about whether the variable is strongly correlated with the other two or the opposite, nor what each variable contributes to the relationship. We can create an artificial line that would connect the values on the axes based on a pair of independent variables. Here we want to compare our data with the results of the last chart.

Barplot with four independent variables. As X1, X2, … Xn are represented by the black line, Y1 is represented by a gray line, and z is the value of interest that we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line that connects all the values on the axis if the relationship is strong.

Scatterplot with two independent variables. As X1, X2, … Xn are represented by the black line, Y1 is represented by a gray line and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong.

Barplot with five independent variables. As X1, X2, … Xn are represented by the black line, Y1 is represented by the gray line and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong.

Scatterplot with seven independent variables. As x1, x2, x3, x4, x5, and x6 are represented by black lines, y1 is represented by gray lines, and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong. (c) the scatterplot in (d) is used as an example of combining different explanatory variables.

Barplot with eight independent variables. As x1, x2, x3, x4, x5, and x6 are represented by black lines, y1 is represented by gray lines, and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong.

Barplot with nine independent variables. As x1, x2, x3, x4, x5, and x6 are represented by black lines, y1 is represented by gray lines, and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong.

Barplot with ten independent variables. As x1, x2, x3, x4, x5, and x6 are represented by black lines, y1 is represented by gray lines, and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong. (c) the scatterplot in (b) is used as an example of combining different explanatory variables.

Barplot with sixteen independent variables. As x1, x2, x3, x4, x5, and x6 are represented by black lines, y1 is represented by gray lines, and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong. (c) the scatterplot in (a) is used as an example of combining different explanatory variables.

Barplot with twenty independent variables. As x1, x2, x3, x4, x5, and x6 are represented by black lines, y1 is represented by gray lines, and z is the value of interest we are trying to determine. When two independent variables are highly correlated, it will create a horizontal line, if the relationship is strong. Note that there is another name for a barplot, namely log barplot. Log bar plots are commonly used in scientific computing. (c) the scatterplot in (d) is used as an example of combining different explanatory variables.

**Table from 2 to 20**

2 × 0 = 1

2 × 1 = 2

2 × 2 = 4

2 × 3 = 6

2 × 4 = 8

2 × 5 = 10

2 × 6 = 12

2 × 7 = 14

2 × 8 = 16

2 × 9 = 18

2 × 10 = 20

3 × 1 = 3

3 × 2 = 6

3 × 3 = 9

3 × 4 = 12

3 × 5 = 15

3 × 6 = 18

3 × 7 = 21

3 × 8 = 24

3 × 9 = 27

3 × 10 = 30

4 × 1 = 4

4 × 2 = 8

4 × 3 = 12

4 × 4 = 16

4 × 5 = 20

4 × 6 = 24

4 × 7 = 28

4 × 8 = 32

4 × 9 = 36

4 × 10 = 40

5 × 1 = 5

5 × 2 = 10

5 × 3 = 15

5 × 4 = 20

5 × 5 = 25

5 × 6 = 30

5 × 7 = 35

5 × 8 = 40

5 × 9 = 45

5 × 10 = 50

6 × 1 = 6

6 × 2 = 12

6 × 3 = 18

6 × 4 = 24

6 × 5 = 30

6 × 6 = 36

6 × 7 = 42

6 × 8 = 48

6 × 9 = 54

6 × 10 = 60

7 × 0 = 0

7 × 1 = 7

7 × 2 = 14

7 × 3 = 21

7 × 4 = 28

7 × 5 = 35

7 × 6 = 42

7 × 7 = 49

7 × 8 = 56

7 × 9 = 63

7 × 10 = 70

8 × 1 = 8

8 × 2 = 16

8 × 3 = 24

8 × 4 = 32

8 × 5 = 40

8 × 6 = 48

8 × 7 = 56

8 × 8 = 64

8 × 9 = 72

8 × 10 = 80

9 × 1 = 9

9 × 2 = 18

9 × 3 = 27

9 × 4 = 36

9 × 5 = 45

9 × 6 = 54

9 × 7 = 63

9 × 8 = 72

9 × 9 = 81

9 × 10 = 90

10 × 1 = 10

10 × 2 = 20

10 × 3 = 30

10 × 4 = 40

10 × 5 = 50

10 × 6 = 60

10 × 7 = 70

10 × 8 = 80

10 × 9 = 90

10 × 10 = 100

11 × 0 = 0

11 × 1 = 11

11 × 2 = 22

11 × 3 = 33

11 × 4 = 44

11 × 5 = 55

11 × 6 = 66

11 × 7 = 77

11 × 8 = 88

11 × 9 = 99

11 × 10 = 110

12 × 1 = 12

12 × 2 = 24

12 × 3 = 36

12 × 4 = 48

12 × 5 = 60

12 × 6 = 72

12 × 7 = 84

12 × 8 = 96

12 × 9 = 108

12 × 10 = 120

13 × 1 = 13

13 × 2 = 26

13 × 3 = 39

13 × 4 = 52

13 × 5 = 65

13 × 6 = 78

13 × 7 = 91

13 × 8 = 104

13 × 9 = 117

13 × 10 = 130

14 × 1 = 14

14 × 2 = 28

14 × 3 = 42

14 × 4 = 56

14 × 5 = 70

14 × 6 = 84

14 × 7 = 98

14 × 8 = 112

14 × 9 = 126

14 × 10 = 140

15 × 1 = 15

15 × 2 = 30

15 × 3 = 45

15 × 4 = 60

15 × 5 = 75

15 × 6 = 90

15 × 7 = 105

15 × 8 = 120

15 × 9 = 135

15 × 10 = 150

16 × 1 = 16

16 × 2 = 32

16 × 3 = 48

16 × 4 = 64

16 × 5 = 80

16 × 6 = 96

16 × 7 = 112

16 × 8 = 128

16 × 9 = 144

16 × 10 = 160

16 × 11 = 176

17 × 1 = 17

17 × 2 = 34

17 × 3 = 51

17 × 4 = 68

17 × 5 = 85

17 × 6 = 102

17 × 7 = 119

17 × 8 = 136

17 × 9 = 153

17 × 10 = 170

18 × 1 = 18

18 × 2 = 36

18 × 3 = 54

18 × 4 = 72

18 × 5 = 90

18 × 6 = 108

18 × 7 = 126

18 × 8 = 144

18 × 9 = 162

18 × 10 = 180

19 × 1 = 19

19 × 2 = 38

19 × 3 = 57

19 × 4 = 76

19 × 5 = 95

19 × 6 = 114

19 × 7 = 133

19 × 8 = 152

19 × 9 = 171

19 × 10 = 190

20 × 1 = 20

20 × 2 = 40

20 × 3 = 60

20 × 4 = 80

20 × 5 = 100

20 × 6 = 120

20 × 7 = 140

20 × 8 = 160

20 × 9 = 180

20 × 10 = 200