How do I graph Studentized residuals in SPSS?

How do I graph Studentized residuals in SPSS?

Generating a Residual Plot in SPSS

1. Go to the “Analyze” menu and select “Regression”
2. Under the “Regression” options, select “Linear”
3. In the “Linear Regression” dialogue box, click and drag the explanatory variable (x) into the “Independent” variable box.

What is a studentized residual plot?

A studentized residual is calculated by dividing the residual by an estimate of its standard deviation. The standard deviation for each residual is computed with the observation excluded. Studentized residuals are more effective in detecting outliers and in assessing the equal variance assumption.

How do you plot residuals on a scatter plot?

Residual Scatterplots

1. To produce a scatterplot of the residuals by the predictor Package design, from the menus choose:
2. In the Chart Builder, select the Scatter/Dot gallery and choose Simple Scatter.
3. Select Standardized Residual as the y-axis variable and Package design as the x-axis variable.
4. Click OK.

What is studentized residual in SPSS?

In statistics, a studentized residual is the quotient resulting from the division of a residual by an estimate of its standard deviation. It is a form of a Student’s t-statistic, with the estimate of error varying between points.

What is a PP plot in SPSS?

The P-P plot compares the observed cumulative distribution function (CDF) of the standardized residual to the expected CDF of the normal distribution. Note that we are testing the normality of the residuals and not predictors.

What is Studentized range statistic?

The Studentized Range (q) is the difference between the largest and smallest data point in a sample, measured in terms of sample standard deviations.

What does a high studentized residual mean?

In general, studentized residuals are going to be more effective for detecting outlying Y observations than standardized residuals. If an observation has a studentized residual that is larger than 3 (in absolute value) we can call it an outlier.

How should a residual plot look?

The residual plot shows a fairly random pattern – the first residual is positive, the next two are negative, the fourth is positive, and the last residual is negative. This random pattern indicates that a linear model provides a decent fit to the data.

What does a QQ plot of residuals show?

A Quantile-Quantile plot (QQ-plot) shows the “match” of an observed distribution with a theoretical distribution, almost always the normal distribution. If the observed distribution of the residuals matches the shape of the normal distribution, then the plotted points should follow a 1-1 relationship.

What does a high Studentized residual mean?

What does a good normal Q-Q plot look like?

The normal distribution is symmetric, so it has no skew (the mean is equal to the median). On a Q-Q plot normally distributed data appears as roughly a straight line (although the ends of the Q-Q plot often start to deviate from the straight line).

What is a Studentized residual in statistics?

Studentized Residuals. A studentized residual (sometimes referred to as an “externally studentized residual” or a “deleted t residual”) is: That is, a studentized residual is just a deleted residual divided by its estimated standard deviation (first formula).

How do you delete residuals in regression analysis?

The basic idea is to delete the observations one at a time, each time refitting the regression model on the remaining n –1 observations. Then, we compare the observed response values to their fitted values based on the models with the ith observation deleted. This produces deleted residuals.

How do you solve for deleted residuals?

Unfortunately, there’s not a straightforward answer to that question. Deleted residuals depend on the units of measurement just as the ordinary residuals do. We can solve this problem though by dividing each deleted residual by an estimate of its standard deviation.

What does a large deleted residual indicate about a data point?

That is, a data point having a large deleted residual suggests that the data point is influential. An example. Consider the following plot of n = 4 data points (3 blue and 1 red):