Description Usage Arguments Details Examples
Multiple Regression Output
1 | model_output(models, data, checkList = NULL, formulas, outliers)
|
models |
A list of |
data |
The dataframe from which the model was built. |
checkList |
a list object created by |
formulas |
Formula list produced by |
outliers |
Outlier option, select the number of observations to examine for outliers. |
Creates plots and text output to summarize models and check
assumptions via objects created by assumptions_check
. Uses full
model with all predictors.
1 2 3 4 5 6 7 8 | freeny_model_formulas <- create_formula_objects("y",
c("lag.quarterly.revenue"), c("price.index"))
freeny_models <- create_model_objects(freeny_model_formulas,
dataset = freeny)
freeny_model <- freeny_models[[length(freeny_models)]]
checks <- assumptions_check(freeny_model)
model_output(freeny_models, freeny, checks, freeny_model_formulas,
outliers = "significant")
|
REGRESSION OUTPUT
Durbin-Watson = 2.357 p value = 0.797
Partial Regression plots (all relationships should be linear):
Plot of studentized residuals: uniform distibution across predicted values requiredCorrelation Matrix for model (correlation >.70 indicates severe multicollinearity)
y lag.quarterly.revenue price.index
y 1.0000 0.9978 -0.9895
lag.quarterly.revenue 0.9978 1.0000 -0.9894
price.index -0.9895 -0.9894 1.0000
Variance inflation factor (<10 desired):
lag.quarterly.revenue price.index
47.5 47.5
Standardized Residuals (observations > 3.00 problematic):
1963.25
3.073
Cook's distance (values >.2 problematic):
1963.25
1.169
Normality of standardized model residuals: Shapiro-Wilk (p-value): 0.1296
Model change statistics
R R^2 Adj R^2 SE Est. Delta R^2 F Change df1 df2 p Fch Sig
Model 1 0.9978 0.9956 0.9955 0.0212 0.9956 8360.3793 1 37 0 ***
Model 2 0.9979 0.9958 0.9956 0.0209 0.0002 2.1304 1 36 0.1531
Model 1 : y ~ lag.quarterly.revenue
Model 2 : y ~ lag.quarterly.revenue + price.index
Model Coefficients
Model term estimate std.error statistic p.value sig
Model 1 (Intercept) 0.04169 0.10138 0.4112 0.6833
Model 1 lag.quarterly.revenue 0.99827 0.01092 91.4351 0.0000 ***
Model 2 (Intercept) 2.18577 1.47236 1.4845 0.1464
Model 2 lag.quarterly.revenue 0.89122 0.07412 12.0240 0.0000 ***
Model 2 price.index -0.25592 0.17534 -1.4596 0.1531
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