What Relationship Does Exist Between?

18. Draw a best-fit line for the Tonga Trench data. A best fit line is a smooth line that shows the trend of the data; the line does not have to pass through the data points.

17 In regression analysis, the coefficient of determination R2 measures the amount of variation in y

4.How do sources 11.1 and 11.2 help explain the success of the Mongols Empire-building efforts?Source 11.1 explains a sense of how political alliances were formed in Mongol society and how khans secured the allegiance of their followers;how Temujin became Chinggis Khan;the personality and leadership style of Ogedei.Source 11.2 revealed how Chinggis Khan viewed wisdom traditions of conquered people and

Stats from E-Digital Research look at the number of followers for the top 20 retailers on Facebook and Twitter and, for the first time, Google+.

Linear regression is an approach for modeling the relationship between a scalar dependent variable Y and explanatory variables (or independent variables) denoted X. Function $f(X,W)=Y$ (shown below) can be learned to predict future values.

Whether it’s administering pain medication or allowing the parent to hold them in between procedures, it is the nurse’s job to determine the actual stressor.

Relationships can either be monogamous or non-monogamous in nature and its definitions and functions can vary. A monogamous relationship is traditionally characterised by two individuals who have dependent children or also as a ‘nuclear family.’ Monogamy is a hetero-normative practice that involves two individuals in a relationship; the couple is not engaged in extramarital intimacy. Nuclear families also fall under this practice. Polyamory is a non-monogamous practice that refers to multiple intimate partners and experiences. However, some scholars argue that if, or when one individual engages in infidelity, Polyamory is a viable option to sustain the relationship. Academic research positions Polyamory as an option that may successfully

This scatter plot graph is a representation of combining income and credit balance. It shows the income increasing as the credit balance increases. As a result of this data it can be inferred that there is a positive relationship between the two variables. Because of the positive relationship between income and credit balance the best fit line or linear regression line fits the data quite well. The speculation can be strongly made that the

contribution of each X variable with the Y variable after all other X variables are included in the model.

"There are several different kinds of relationships between variables. Before drawing a conclusion, you should first understand how one variable changes with the other. This means you need to establish how the variables are related - is the relationship linear or quadratic or inverse or logarithmic or something else" ("Relationship Between Variables ", n.d)

This assignment will answer two interrelated questions. The first part will look at transitions, for example, temporal transitions across time, such as, a child 's growth development or evolving capacities. Also, spatial transitions, between spaces such as moving between specific locations or services. It will look at how transitions can affect the child and how law and policy can try to slow down some transitions. How practitioners need to understand the concept of transitions and the perspective of all involved to see how it can be applied and developed in practice (The Open University, 2016a). The second part will focus on new care setting using case studies for evidence. There are different types of care, kinship care where family

The goal of ordinary least squares (OLS) regression is to best predict one variable?s behavior against another.

To start with, the 1st model used is regression line method. According to this method, the technique fits a trend line to a series of historical data point and the projects the line into the future for medium to long range forecasts

The trendline, known as the line of best fit or the least squares regression line, shows the linear equation which best explains the sums up the data’s trend. The formula on the right is the formula of the line of best fit.

So you first predict the independent variable, then look at the established relationships between that independent variable and the dependent ones to predict what the dependent variables will be. You then develop an equation that summarizes the effects of predictor variables.