Analysis of the Est.Qui data, using Est as the response variable and Qui as the single predictor.

Prepared by BrailleR

Variable summaries

The response variable is Est and the predictor variable is Qui.

The following objects are masked _by_ .GlobalEnv:

    Est, Qui

	Minimum	Lower Quartile	Median	Mean	Upper Quartile	Maximum	Standard Deviation	Missing Values
Est	2	4.75	6.0	6.1250	7.500	10	2.587746	0
Qui	1	4.50	6.5	6.0625	8.125	10	2.981341	0

Scatter Plot

The following objects are masked _by_ .GlobalEnv:

    Est, Qui

    1 2 3 4 Sum
4   0 1 0 2   3
3   0 1 1 0   2
2   1 0 0 0   1
1   1 1 0 0   2
Sum 2 3 1 2   8

Linear regression

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The term Qui is not significant to 10%.


Call:
lm(formula = Est ~ Qui, data = Est.Qui)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5711 -1.2548  0.1896  1.6587  2.5143 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   2.9643     1.8906   1.568    0.168
Qui           0.5213     0.2833   1.840    0.115

Residual standard error: 2.235 on 6 degrees of freedom
Multiple R-squared:  0.3608,    Adjusted R-squared:  0.2542 
F-statistic: 3.386 on 1 and 6 DF,  p-value: 0.1153

plot of chunk FittedLinePlot

Residual analysis

A separate html page showing the residual analysis and model validity checking for Est.Qui.lm is at Est.Qui.lm.Validity.html

One-way Analysis of Variance

Analysis of Variance Table

Response: Est
          Df Sum Sq Mean Sq F value Pr(>F)
Qui        1 16.911  16.911  3.3863 0.1153
Residuals  6 29.964   4.994