In linear regression, the F-statistic is the test statistic for the analysis of variance (ANOVA) approach to test the significance of the model or the components in the model.

The F-statistic in the linear model output display is the test statistic for
testing the statistical significance of the model. The F-statistic values in the
`anova`

display are for assessing the significance of the
terms or components in the model.

After obtaining a fitted model, say, `mdl`

, using
`fitlm`

or `stepwiselm`

, you can:

Find the

`F-statistic vs. constant model`

in the output display or by usingdisp(mdl)

Display the ANOVA for the model using

anova(mdl,'summary')

Obtain the F-statistic values for the components, except for the constant term using

For details, see theanova(mdl)

`anova`

method of the`LinearModel`

class.

This example shows how to assess the fit of the model and the significance of the regression coefficients using the F-statistic.

Load the sample data.

load hospital tbl = table(hospital.Age,hospital.Weight,hospital.Smoker,hospital.BloodPressure(:,1), ... 'VariableNames',{'Age','Weight','Smoker','BloodPressure'}); tbl.Smoker = categorical(tbl.Smoker);

Fit a linear regression model.

`mdl = fitlm(tbl,'BloodPressure ~ Age*Weight + Smoker + Weight^2')`

mdl = Linear regression model: BloodPressure ~ 1 + Smoker + Age*Weight + Weight^2 Estimated Coefficients: Estimate SE tStat pValue __________ _________ ________ __________ (Intercept) 168.02 27.694 6.067 2.7149e-08 Age 0.079569 0.39861 0.19962 0.84221 Weight -0.69041 0.3435 -2.0099 0.047305 Smoker_true 9.8027 1.0256 9.5584 1.5969e-15 Age:Weight 0.00021796 0.0025258 0.086294 0.93142 Weight^2 0.0021877 0.0011037 1.9822 0.050375 Number of observations: 100, Error degrees of freedom: 94 Root Mean Squared Error: 4.73 R-squared: 0.528, Adjusted R-Squared: 0.503 F-statistic vs. constant model: 21, p-value = 4.81e-14

The F-statistic of the linear fit versus the constant model is 21, with a *p*-value of 4.81e-14. The model is significant at the 5% significance level. The R-squared value of 0.528 means the model explains about 53% of the variability in the response. There might be other predictor (explanatory) variables that are not included in the current model.

Display the ANOVA table for the fitted model.

`anova(mdl,'summary')`

`ans=`*5×5 table*
SumSq DF MeanSq F pValue
______ __ ______ ______ __________
Total 4461.2 99 45.062
Model 2354.5 5 470.9 21.012 4.8099e-14
. Linear 2263.3 3 754.42 33.663 7.2417e-15
. Nonlinear 91.248 2 45.624 2.0358 0.1363
Residual 2106.6 94 22.411

This display separates the variability in the model into linear and nonlinear terms. Since there are two non-linear terms (`Weight^2`

and the interaction between `Weight`

and `Age`

), the nonlinear degrees of freedom in the `DF`

column is 2. There are three linear terms in the model (one `Smoker`

indicator variable, `Weight`

, and `Age`

). The corresponding F-statistics in the `F`

column are for testing the significance of the linear and nonlinear terms as separate groups.

When there are replicated observations, the residual term is also separated into two parts; first is the error due to the lack of fit, and second is the pure error independent from the model, obtained from the replicated observations. In that case, the F-statistic is for testing the lack of fit, that is, whether the fit is adequate or not. But, in this example, there are no replicated observations.

Display the ANOVA table for the model terms.

anova(mdl)

`ans=`*6×5 table*
SumSq DF MeanSq F pValue
________ __ ________ _________ __________
Age 62.991 1 62.991 2.8107 0.096959
Weight 0.064104 1 0.064104 0.0028604 0.95746
Smoker 2047.5 1 2047.5 91.363 1.5969e-15
Age:Weight 0.16689 1 0.16689 0.0074466 0.93142
Weight^2 88.057 1 88.057 3.9292 0.050375
Error 2106.6 94 22.411

This display decomposes the ANOVA table into the model terms. The corresponding F-statistics in the `F`

column assess the statistical significance of each term. For example, the F-test for `Smoker`

tests whether the coefficient of the indicator variable for `Smoker`

is different from zero. That is, the F-test determines whether being a smoker has a significant effect on `BloodPressure`

. The degrees of freedom for each model term is the numerator degrees of freedom for the corresponding F-test. All the terms have one degree of freedom. In the case of a categorical variable, the degrees of freedom is the number of indicator variables. `Smoker`

has only one indicator variable, so it also has one degree of freedom.

In linear regression, the *t*-statistic is useful for making
inferences about the regression coefficients. The hypothesis test on coefficient
*i* tests the null hypothesis that it is equal to zero –
meaning the corresponding term is not significant – versus the alternate
hypothesis that the coefficient is different from zero.

For a hypotheses test on coefficient *i*, with

H_{0} :*
β*_{i} = 0

H_{1} :
*β*_{i} ≠
0,

the *t*-statistic is:

$$t=\frac{{b}_{i}}{SE({b}_{i})},$$

where
*SE*(*b*_{i})
is the standard error of the estimated coefficient
*b*_{i}.

After obtaining a fitted model, say, `mdl`

, using
`fitlm`

or `stepwiselm`

, you can:

Find the coefficient estimates, the standard errors of the estimates (

`SE`

), and the*t*-statistic values of hypothesis tests for the corresponding coefficients (`tStat`

) in the output display.Call for the display using

display(mdl)

This example shows how to test for the significance of the regression coefficients using t-statistic.

Load the sample data and fit the linear regression model.

```
load hald
mdl = fitlm(ingredients,heat)
```

mdl = Linear regression model: y ~ 1 + x1 + x2 + x3 + x4 Estimated Coefficients: Estimate SE tStat pValue ________ _______ ________ ________ (Intercept) 62.405 70.071 0.8906 0.39913 x1 1.5511 0.74477 2.0827 0.070822 x2 0.51017 0.72379 0.70486 0.5009 x3 0.10191 0.75471 0.13503 0.89592 x4 -0.14406 0.70905 -0.20317 0.84407 Number of observations: 13, Error degrees of freedom: 8 Root Mean Squared Error: 2.45 R-squared: 0.982, Adjusted R-Squared: 0.974 F-statistic vs. constant model: 111, p-value = 4.76e-07

You can see that for each coefficient, `tStat = Estimate/SE`

. The $$p$$-values for the hypotheses tests are in the `pValue`

column. Each $$t$$-statistic tests for the significance of each term given other terms in the model. According to these results, none of the coefficients seem significant at the 5% significance level, although the R-squared value for the model is really high at 0.97. This often indicates possible multicollinearity among the predictor variables.

Use stepwise regression to decide which variables to include in the model.

```
load hald
mdl = stepwiselm(ingredients,heat)
```

1. Adding x4, FStat = 22.7985, pValue = 0.000576232 2. Adding x1, FStat = 108.2239, pValue = 1.105281e-06

mdl = Linear regression model: y ~ 1 + x1 + x4 Estimated Coefficients: Estimate SE tStat pValue ________ ________ _______ __________ (Intercept) 103.1 2.124 48.54 3.3243e-13 x1 1.44 0.13842 10.403 1.1053e-06 x4 -0.61395 0.048645 -12.621 1.8149e-07 Number of observations: 13, Error degrees of freedom: 10 Root Mean Squared Error: 2.73 R-squared: 0.972, Adjusted R-Squared: 0.967 F-statistic vs. constant model: 177, p-value = 1.58e-08

In this example, `stepwiselm`

starts with the constant model (default) and uses forward selection to incrementally add `x4`

and `x1`

. Each predictor variable in the final model is significant given the other one is in the model. The algorithm stops when adding none of the other predictor variables significantly improves in the model. For details on stepwise regression, see `stepwiselm`

.

`LinearModel`

| `anova`

| `coefCI`

| `coefTest`

| `fitlm`

| `stepwiselm`