# Non linear relationship between x and y axis

### Correlation and Linear Regression

The first of each pair (as plotted on the horizontal axis) gives a set of single It does not make sense to view y as a linear function of x plus. With nonlinearity, the effect of X on Y depends on the value of X; in effect, X and the relationship between the variables is therefore nonlinear, we can .. in magnitude as X increases, with the curve approaching the X axis as. The correlation between two variables can be positive (i.e., higher levels of one It is important to note that there may be a non-linear association between two continuous variable is plotted along the X-axis and the other along the Y-axis.

These at least originally, when they were developed for pencil-and-paper use emphasize simple, easy-to-compute, robust summaries of data. One of the very simplest kinds of summaries is based on positions within a set of numbers, such as the middle value, which describes a "typical" value.

Middles are easy to estimate reliably from graphics. Scatterplots exhibit pairs of numbers. The first of each pair as plotted on the horizontal axis gives a set of single numbers, which we could summarize separately.

In this particular scatterplot, the y-values appear to lie within two almost completely separate groups: This impression is confirmed by drawing a histogram of the y-values, which is sharply bimodal, but that would be a lot of work at this stage. I invite sceptics to squint at the scatterplot. When I do--using a large-radius, gamma-corrected Gaussian blur that is, a standard rapid image processing result of the dots in the scatterplot I see this: The two groups--upper and lower--are pretty apparent.

The upper group is much lighter than the lower because it contains many fewer dots. Accordingly, let's summarize the groups of y-values separately. I will do that by drawing horizontal lines at the medians of the two groups. In order to emphasize the impression of the data and to show we're not doing any kind of computation, I have a removed all decorations like axes and gridlines and b blurred the points.

Little information about the patterns in the data is lost by thus "squinting" at the graphic: Similarly, I have attempted to mark the medians of the x-values with vertical line segments.

In the upper group red lines you can check--by counting the blobs--that these lines do actually separate the group into two equal halves, both horizontally and vertically. In the lower group blue lines I have only visually estimated the positions without actually doing any counting.

Regression The points of intersection are the centers of the two groups. One excellent summary of the relationship among the x and y values would be to report these central positions. One would then want to supplement this summary by a description of how much the data are spread in each group--to the left and right, above and below--around their centers. For brevity, I won't do that here, but note that roughly the lengths of the line segments I have drawn reflect the overall spreads of each group.

Finally, I drew a dashed line connecting the two centers.

This is a reasonable regression line. Is it a good description of the data?

## Aligning two X axis's, that have a non-linear relationship

Is it even evidence of linearity? That's scarcely relevant because the linear description is so poor. Nevertheless, because that is the question before us, let's address it. Each point represents the observed x, y pair, in this case, BMI and the corresponding total cholesterol measured in each participant. Note that the independent variable BMI is on the horizontal axis and the dependent variable Total Serum Cholesterol on the vertical axis.

BMI and Total Cholesterol The graph shows that there is a positive or direct association between BMI and total cholesterol; participants with lower BMI are more likely to have lower total cholesterol levels and participants with higher BMI are more likely to have higher total cholesterol levels.

For either of these relationships we could use simple linear regression analysis to estimate the equation of the line that best describes the association between the independent variable and the dependent variable. The simple linear regression equation is as follows: The Y-intercept and slope are estimated from the sample data, and they are the values that minimize the sum of the squared differences between the observed and the predicted values of the outcome, i.

These differences between observed and predicted values of the outcome are called residuals.

The estimates of the Y-intercept and slope minimize the sum of the squared residuals, and are called the least squares estimates. That would mean that variability in Y could be completely explained by differences in X. However, if the differences between observed and predicted values are not 0, then we are unable to entirely account for differences in Y based on X, then there are residual errors in the prediction.

The residual error could result from inaccurate measurements of X or Y, or there could be other variables besides X that affect the value of Y.

### Teacher resources - Linear and non-linear relations page 1

Based on the observed data, the best estimate of a linear relationship will be obtained from an equation for the line that minimizes the differences between observed and predicted values of the outcome. The Y-intercept of this line is the value of the dependent variable Y when the independent variable X is zero.

The slope of the line is the change in the dependent variable Y relative to a one unit change in the independent variable X.