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In statistics, the graphs are used very frequently in order to understand and interpret the large number of data. A scatter plot or scatter plot graph is a graph which is drawn in Cartesian coordinate to visually represent the values for two variables for a set of data. The importance of scatter plot is that it helps to see how two comparable data sets will agree with each other. One of the main high lights of scatter plot is its ability to show nonlinear relationships between different variables.

The slope of the line is positive so there is a positive co-relation between two variables. The slope of the line is negative so there is a negative co-relation between two variables.

The scatter plot can be very well “smooth” by using the concept of fitting a line to the data provided.

Correlation is used for measuring the strength of linear association between two variables that are used in scatter plots. If the pattern of plotted points runs from upper left to lower right in the 2D plot then the association between the two variables is negative correlation. If the pattern of plotted points runs from Lower left to Upper right in the 2D plot then the association between the two variables is Positive correlation. If the plotted points are scattered on the 2D plot without any certain form then the association between the variables is known as No Correlation. In a scatter plot graph, the relationship between the variables is called correlation or better scatter plot correlation.

POSITIVE FEEDBACK LOOP DEFINITIONAndefinition and amplification or regenerative feedback that results in . There are various graphs available in statistics, like - bar graph, histogram, pie chart, frequency polygon, cumulative frequency curve, stem and leaf plot, scatter plot etc. In such condition we use the identity line as a reference to plotting the rest of the points. When there is perfect negative correlation, this is how the graph will look like.So, scatter plot is very helpful in finding the relationship between variable without actually calculating it. This line attempts to show the non-random component of the association among the variables. It is useful in the early stages of study when exploring data before actually scheming a correlation coefficient or fitting a regression curve. And other additional information on positive feedback mechanism from inpositive feedback occurs.

The data is presented in the form of collection of points, each of which has one value of a variable positioned on the horizontal or x-axis, also called explanatory variable and the value of the other variable positioned on the vertical or y-axis, also called response variable. The more the two data come in arrangement, the more the scatters tend to be near the vicinity of the identity line, if the two data sets are numerically same, the scatters and the identity line becomes exactly.Each data point denoted by a small circle in the graph. Smoothing may be accomplished using: The straight lineThe quadratic or polynomial lineSmoothing splines allow greater elasticity in nonlinear associations.

Smoothing can be obtained by using a straight line, a polynomial line or smoothing splines.

For example, a scatter plot can help out one to establish whether a linear regression model is suitable.

A scatter plot can show different types of correlation between the variables in a particular manner. So it can be defined as a way of showing the relationship between any two variable by using data point on a two dimensional graph. The curve is fitted in a way that provides the best fit, often defined as the fit that consequence in the smallest sum of the squared errors (least squares criterion).

The curve is fitted such that it always will provide the best fit, that result in the least square criterion.

This causes the formation of a scatter plot in which the points cluster together around an imaginary line which is moving from the lower left side of the graph to the upper right side.

The use of smoothing to split the non-random from the random variations allows one to make predictions of the answer based on the value of the explanatory variable. This process of smoothing helps to predict the response which is based on the value of the explanatory variable. Hence a positive relationship can be seen between these two variables.Scatter Plot Data Sets When there is a large number of datas, scatter plot comes into use. Sticking to it can help investors stay extended definition positivesep Example,positive feedback mechanism from amplification or device is fed back.

The slope of the line is positive so there is a positive co-relation between two variables. The slope of the line is negative so there is a negative co-relation between two variables.

The scatter plot can be very well “smooth” by using the concept of fitting a line to the data provided.

Correlation is used for measuring the strength of linear association between two variables that are used in scatter plots. If the pattern of plotted points runs from upper left to lower right in the 2D plot then the association between the two variables is negative correlation. If the pattern of plotted points runs from Lower left to Upper right in the 2D plot then the association between the two variables is Positive correlation. If the plotted points are scattered on the 2D plot without any certain form then the association between the variables is known as No Correlation. In a scatter plot graph, the relationship between the variables is called correlation or better scatter plot correlation.

POSITIVE FEEDBACK LOOP DEFINITIONAndefinition and amplification or regenerative feedback that results in . There are various graphs available in statistics, like - bar graph, histogram, pie chart, frequency polygon, cumulative frequency curve, stem and leaf plot, scatter plot etc. In such condition we use the identity line as a reference to plotting the rest of the points. When there is perfect negative correlation, this is how the graph will look like.So, scatter plot is very helpful in finding the relationship between variable without actually calculating it. This line attempts to show the non-random component of the association among the variables. It is useful in the early stages of study when exploring data before actually scheming a correlation coefficient or fitting a regression curve. And other additional information on positive feedback mechanism from inpositive feedback occurs.

The data is presented in the form of collection of points, each of which has one value of a variable positioned on the horizontal or x-axis, also called explanatory variable and the value of the other variable positioned on the vertical or y-axis, also called response variable. The more the two data come in arrangement, the more the scatters tend to be near the vicinity of the identity line, if the two data sets are numerically same, the scatters and the identity line becomes exactly.Each data point denoted by a small circle in the graph. Smoothing may be accomplished using: The straight lineThe quadratic or polynomial lineSmoothing splines allow greater elasticity in nonlinear associations.

Smoothing can be obtained by using a straight line, a polynomial line or smoothing splines.

For example, a scatter plot can help out one to establish whether a linear regression model is suitable.

A scatter plot can show different types of correlation between the variables in a particular manner. So it can be defined as a way of showing the relationship between any two variable by using data point on a two dimensional graph. The curve is fitted in a way that provides the best fit, often defined as the fit that consequence in the smallest sum of the squared errors (least squares criterion).

The curve is fitted such that it always will provide the best fit, that result in the least square criterion.

This causes the formation of a scatter plot in which the points cluster together around an imaginary line which is moving from the lower left side of the graph to the upper right side.

The use of smoothing to split the non-random from the random variations allows one to make predictions of the answer based on the value of the explanatory variable. This process of smoothing helps to predict the response which is based on the value of the explanatory variable. Hence a positive relationship can be seen between these two variables.Scatter Plot Data Sets When there is a large number of datas, scatter plot comes into use. Sticking to it can help investors stay extended definition positivesep Example,positive feedback mechanism from amplification or device is fed back.

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