line relationship

(noun)

A direct relationship between superiors and their subordinates in a work setting.

Related Terms

Examples of line relationship in the following topics:

  • Line Structure

    • The line structure model of organization is a direct linear relationship of command and deference between superiors and their subordinates.
    • Colonels and generals have a line relationship; generals give orders to the colonels, and the colonels are directly responsible for carrying them out.
    • An example of a simple hierarchical organizational chart is the line relationship that exists between superiors and subordinates.
    • An example of a "line relationship" (or chain of command in military relationships) in would be between the manager and the two supervisors.
    • An example of a "line relationship" (or chain of command in military relationships) in this chart would be between the manager and the two supervisors.

  • Beginning with straight lines

    • Such plots permit the relationship between the variables to be examined with ease.
    • While the relationship is not perfectly linear, it could be helpful to partially explain the connection between these variables with a straight line.
    • Straight lines should only be used when the data appear to have a linear relationship, such as the case shown in the left panel of Figure 7.6.
    • The right panel of Figure 7.6 shows a case where a curved line would be more useful in understanding the relationship between the two variables.
    • We only consider models based on straight lines in this chapter.

  • Hypothesis Tests with the Pearson Correlation

    • We test the correlation coefficient to determine whether the linear relationship in the sample data effectively models the relationship in the population.
    • We can use the regression line to model the linear relationship between $x$ and $y$ in the population.
    • Therefore we can NOT use the regression line to model a linear relationship between $x$ and $y$ in the population.
    • (We do not know the equation for the line for the population.
    • Our regression line from the sample is our best estimate of this line in the population. )

  • Equations of Lines and Planes

    • A line is a vector which connects two points on a plane and the direction and magnitude of a line determine the plane on which it lies.
    • A line is described by a point on the line and its angle of inclination, or slope.
    • Every line lies in a plane which is determined by both the direction and slope of the line.
    • The components of equations of lines and planes are as follows:
    • Explain the relationship between the lines and three dimensional geometric objects

  • Product Line Breadth

    • The breadth of the product mix consists of all the product lines that the company has to offer to its customers.
    • What products will be offered (i.e., the breadth and depth of the product line)?
    • In this unit, you're going to learn about the relationship between the breadth of the product line and the product mix.
    • An individual product is a particular product within a product line.
    • Describe the relationship between product line breadth and the product marketing mix

  • Homework

    • What does it imply about the significance of the relationship?
    • What does it imply about the significance of the relationship?
    • What does it imply about the significance of the relationship?
    • What does it imply about the significance of the relationship?
    • What does it imply about the significance of the relationship?

  • Implied Line

    • Implied lines are suggested lines that give works of art a sense of motion, and keep the viewer engaged in a composition.
    • The quality of a line refers to the character that is presented by a line in order to animate a surface to varying degrees.
    • Rather than actual visible lines, implied lines are more like visual prompts, or suggested lines.
    • Both set up a diagonal relationship that implies movement to the viewer.
    • Implied lines are found in three-dimensional artworks as well.

  • Assumptions in Testing the Significance of the Correlation Coefficient

    • We are examining the sample to draw a conclusion about whether the linear relationship that we see between x and y in the sample data provides strong enough evidence so that we can conclude that there is a linear relationship between x and y in the population.
    • The regression line equation that we calculate from the sample data gives the best fit line for our particular sample.
    • We want to use this best fit line for the sample as an estimate of the best fit line for the population.
    • There is a linear relationship in the population that models the average value of y for varying values of x.
    • Our regression line from the sample is our best estimate of this line in the population. )

  • Describing linear relationships with correlation

    • If the relationship is strong and positive, the correlation will be near +1.
    • It appears no straight line would fit any of the datasets represented in Figure 7.11.
    • We'll leave it to you to draw the lines.
    • In general, the lines you draw should be close to most points and reflect overall trends in the data.
    • In each case, there is a strong relationship between the variables.

  • Least-Squares Regression

    • Finding the best fit line is based on the assumption that the data are scattered about a straight line.
    • Any other potential line would have a higher SSE than the best fit line.
    • Therefore, this best fit line is called the least squares regression line.
    • Ordinary Least Squares (OLS) regression (or simply "regression") is a useful tool for examining the relationship between two or more interval/ratio variables assuming there is a linear relationship between said variables.
    • If there is a linear relationship between two variables, you can use one variable to predict values of the other variable.