Checking validity for the model “peso.altura.lm” by way of standardised residuals, leverages, and Cook's distances
Prepared by BrailleR
Basic summary measures
Counts
12 values in all, made up of
12 unique values,
12 observed, and
0 missing values.
Measures of location
Data |
all |
5% trimmed |
10% trimmed |
Mean |
0.0323199 |
0.0323199 |
0.0323199 |
Quantiles
|
Quantile |
Value |
0% |
Minimum |
-1.3726 |
25% |
Lower Quartile |
-0.8200 |
50% |
Median |
-0.2133 |
75% |
Upper Quartile |
0.5739 |
100% |
Maximum |
2.3101 |
Measures of spread
Measure |
IQR |
Standard deviation |
Variance |
Value |
1.3939319 |
1.1063873 |
1.2240928 |
Basic univariate graphs
Histogram
![The histogram](data:image/png;base64,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)
This is a histogram, with the title: Histogram of Residuals
"Residuals" is marked on the x-axis.
Tick marks for the x-axis are at: -2, -1, 0, 1, 2, and 3
There are a total of 12 elements for this variable.
Tick marks for the y-axis are at: 0, 1, 2, 3, 4, and 5
It has 5 bins with equal widths, starting at -2 and ending at 3 .
The mids and counts for the bins are:
mid = -1.5 count = 2
mid = -0.5 count = 5
mid = 0.5 count = 2
mid = 1.5 count = 2
mid = 2.5 count = 1
Boxplot
![The boxplot](data:image/png;base64,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)
This graph has a boxplot printed horizontally
with the title:
"" appears on the x-axis.
"" appears on the y-axis.
Tick marks for the x-axis are at: -1, 0, 1, and 2
This variable has 12 values.
There are no outliers marked for this variable
The whiskers extend to -1.372594 and 2.310133 from the ends of the box,
which are at -0.8422101 and 0.7534516
The median, -0.2133455 is 39 % from the left end of the box to the right end.
The right whisker is 2.94 times the length of the left whisker.
Assessing normality
Formal tests for normality
|
Statistic |
P Value |
Shapiro-Wilk |
0.9377 |
0.4683 |
Anderson-Darling |
0.3299 |
0.4589 |
Cramer-von Mises |
0.0549 |
0.4115 |
Lilliefors (Kolmogorov-Smirnov) |
0.1673 |
0.4665 |
Pearson chi-square |
3.0000 |
0.3916 |
Shapiro-Francia |
0.9416 |
0.4385 |
Normality plot
![The normality plot](data:image/png;base64,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)
Formal tests of moments
|
Statistic |
Z |
P Value |
D'Agostino skewness |
0.6938 |
1.279 |
0.201 |
Anscombe-Glynn kurtosis |
2.5686 |
0.256 |
0.798 |
Regression diagnostic plots
Standardised residuals
![Standardised residuals plotted against fitted values](data:image/png;base64,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)
1 2 3 4 Sum
4 2 1 0 0 3
3 0 0 1 1 2
2 0 2 0 1 3
1 3 1 0 0 4
Sum 5 4 1 2 12
![Standardised residuals plotted against order](data:image/png;base64,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)
1 2 3 4 Sum
4 1 1 0 1 3
3 1 0 1 0 2
2 0 1 2 0 3
1 1 1 0 2 4
Sum 3 3 3 3 12
![standardised residuals plotted against lagged residuals](data:image/png;base64,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)
1 2 3 4 Sum
4 2 0 0 0 2
3 0 1 0 1 2
2 1 1 1 1 4
1 0 2 0 1 3
Sum 3 4 1 3 11
The lag 1 autocorrelation of the standardised residuals is -0.3628521.
Influence
![Standardised residuals plotted against leverages](data:image/png;base64,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)
1 2 3 4 Sum
4 3 0 0 0 3
3 1 0 0 1 2
2 2 1 0 0 3
1 3 0 1 0 4
Sum 9 1 1 1 12
1 points have excessive leverage.
0 points have Cook's distances greater than one.
Outliers and influential observations
|
peso |
altura |
Fit |
St.residual |
Leverage |
Cooks.distance |
2 |
92 |
196 |
91.67509 |
0.0737318 |
0.4368231 |
0.0023412 |
6 |
78 |
169 |
67.99539 |
2.3101333 |
0.1340349 |
0.2880791 |