# <1> Dfbeta

Measures the influence of each observation on the coefficient of a particular independent variable (for example, x1). This is in Standard Errors terms.

An observation is influential if it has a significant effect on the coefficient.

A case is an influential outlier

if

lDfBetal > 2 / SQRT(N)

Where N is the sample size

Note: STATA estimates standardized DfBetas.

### Command

. reg price headroom gear_ratio foreign mpg

. predict DF_mpg, dfbeta(mpg)

**To flag the cutoff** cialis existe generico

. gen cutoffdfbeta = abs(DF_mpg) > 2/sqrt(e(N)) & e(sample)

.

# <2> DfFit

Indicator of leverage and high residuals.

Measures how much an observation influences the regression model as a whole.

How much the predicted values change as a result of including and excluding a particular observation. buy sildenafil online no prescription

High influence cheap cialis

if

lDfFITl > 2 SQRT(k/N)

where k is the number of parameters (including the intercept)

and N is the sample size

### Command

. reg price headroom gear_ratio foreign mpg

. predict DFits if e(sample), dfits

**To flag the cutoff**

. gen cutoffdfit = abs(DFits) > 2*sqrt((e(df_m)+1)/e(N)) & e(sample)

.

# <3> Covariance Ratio

Measures the impact of an observation on the Standard Errors

High influence

if

lCOVRATIO – 1l >= 3 * k / N

where k is the number of parameters (including the intercept)

and N is the sample size

### Command

. reg price headroom gear_ratio foreign mpg

. predict covratio if e(sample), covratio

**To flag the cutoff**

. gen cutoffcov = abs(covratio) >= 3*(e(df_m)+1)/e(N) & e(sample)

.

# <4> Cook’s Distance

Measures how much an observation influences the overall model or predicted values.

It is a summary measure of leverage and high residuals

High influence

if

D > 4 / N

where N is the sample size.

D > 1 indicates big outlier problem

### Command

. reg price headroom gear_ratio foreign mpg

. predict D, cooksd

**To flag the cutoff (>1)**

. gen cutoffD1 = D > 1

**To flag the cutoff (>4/N)**

. gen cutoffD2 = D > 4/e(N) & e(sample)

.

# <5> Leverage

Measures how much an observation influences regression coefficient

High influence

if

leverage h > 2 * k / N

where k is the number of parameters (including the intercept)

and N is the sample size

A rule-of-thumb: Leverage goes from 0 to 1.

A value closer to 1 or over 0.5 may indicate problems.

### Command

. reg price headroom gear_ratio foreign mpg

. predict lev, leverage

**To flag the cutoff (>0.5)**

. gen cutofflev = lev > 0.5

**To flag the cutoff (>2 * k/N)**

. gen cutofflev2 = lev > 2*(e(df_m)+1)/e(N) & e(sample)

.

# <6> Mahalanobis Distance

It is rescaled measure of leverage.

M = leverage * (N-1)

where N is sample size

Higher levels indicate higher distance from average values.

The M-distance follows a Chi-square distribution with K-1 df and alpha = 0.001 (where k is the number of independent variables).

Any value over this Chi-square value may indicate problems.