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## Standard Error Of Intercept

## Standard Error Of Regression Interpretation

## Stone & Jon Ellis, Department of Chemistry, University of Toronto Last updated: October 25th, 2013 Login or Register Log in with Forums FAQ Search in titles only Search in General only

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See the mathematics-of-ARIMA-models notes for more **discussion of** unit roots.) Many statistical analysis programs report variance inflation factors (VIF's), which are another measure of multicollinearity, in addition to or instead of In a simple regression model, the standard error of the mean depends on the value of X, and it is larger for values of X that are farther from its own You don′t need to memorize all these equations, but there is one important thing to note: the standard errors of the coefficients are directly proportional to the standard error of the asked 1 year ago viewed 1771 times active 1 year ago Blog Stack Overflow Podcast #93 - A Very Spolsky Halloween Special Get the weekly newsletter! Source

An observation whose residual is much greater than 3 times the standard error of the regression is therefore usually called an "outlier." In the "Reports" option in the Statgraphics regression procedure, On the other hand, if the coefficients are really not all zero, then they should soak up more than their share of the variance, in which case the F-ratio should be Please try the request again. Similarly, if X2 increases by 1 unit, other things equal, Y is expected to increase by b2 units. his comment is here

The t distribution resembles the standard normal distribution, but has somewhat fatter tails--i.e., relatively more extreme values. Ideally, you would like your confidence intervals to be as narrow as possible: more precision is preferred to less. For example, if the sample size is increased by a factor of 4, the standard error of the mean goes down by a factor of 2, i.e., our estimate of the But, again, the problem has been manufactured by a poor parameterization: one cannot (and does not want to) estimate a partial effect at x = 0.

Does this mean that, when comparing alternative forecasting models for the same time series, you should always pick the one that yields the narrowest confidence intervals around forecasts? See page 77 **of this article for** the formulas and some caveats about RTO in general. Confidence intervals for the forecasts are also reported. Standard Error Of Estimate Interpretation In addition to ensuring that the in-sample errors are unbiased, the presence of the constant allows the regression line to "seek its own level" and provide the best fit to data

The ANOVA table is also hidden by default in RegressIt output but can be displayed by clicking the "+" symbol next to its title.) As with the exceedance probabilities for the In a simple regression model, the F-ratio is simply the square of the t-statistic of the (single) independent variable, and the exceedance probability for F is the same as that for That is to say, their information value is not really independent with respect to prediction of the dependent variable in the context of a linear model. (Such a situation is often http://people.duke.edu/~rnau/mathreg.htm However, when the dependent and independent variables are all continuously distributed, the assumption of normally distributed errors is often more plausible when those distributions are approximately normal.

As the sample size gets larger, the standard error of the regression merely becomes a more accurate estimate of the standard deviation of the noise. Standard Error Of Regression Coefficient Rather, a 95% confidence interval is an interval calculated by a formula having the property that, in the long run, it will cover the true value 95% of the time in Last edited by Maarten Buis; 20 Aug 2014, 02:21. --------------------------------- Maarten L. Hence, if at least one variable is known to be significant in the model, as judged by its t-statistic, then there is really no need to look at the F-ratio.

Another situation in which the logarithm transformation may be used is in "normalizing" the distribution of one or more of the variables, even if a priori the relationships are not known http://www.chem.utoronto.ca/coursenotes/analsci/stats/ErrRegr.html As with the mean model, variations that were considered inherently unexplainable before are still not going to be explainable with more of the same kind of data under the same model Standard Error Of Intercept Tags: None Nick Cox Tenured Member Join Date: Mar 2014 Posts: 9003 #2 19 Aug 2014, 15:03 Why not look at the data? Standard Error Of The Slope Definition Got it? (Return to top of page.) Interpreting STANDARD ERRORS, t-STATISTICS, AND SIGNIFICANCE LEVELS OF COEFFICIENTS Your regression output not only gives point estimates of the coefficients of the variables in

Despite the advantages of regressing e.g. (response - its mean) on (predictors - their means) that doesn't always produce equations that are easy to compare between different studies, as observed means this contact form This situation often arises when two or more different lags of the same variable are used as independent variables in a time series regression model. (Coefficient estimates for different lags of Comment Post Cancel Maarten Buis Tenured Member Join Date: Mar 2014 Posts: 874 #5 20 Aug 2014, 01:28 I tend to center my variables and look at the constant very often. The standard error of the forecast for Y at a given value of X is the square root of the sum of squares of the standard error of the regression and Standard Error Of Regression Formula

But there is much to be said for shifting the origin to something more convenient so long as it is fairly central within the observed range. Note: the t-statistic is usually not used as a basis for deciding whether or not to include the constant term. The simple regression model reduces to the mean model in the special case where the estimated slope is exactly zero. have a peek here Login or Register by clicking 'Login or Register' at the top-right of this page.

The system returned: (22) Invalid argument The remote host or network may be down. Standard Error Of Slope Excel Generated Tue, 01 Nov 2016 18:01:24 GMT by s_fl369 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.10/ Connection However, like most other diagnostic tests, the VIF-greater-than-10 test is not a hard-and-fast rule, just an arbitrary threshold that indicates the possibility of a problem.

The rule of thumb here is that a VIF larger than 10 is an indicator of potentially significant multicollinearity between that variable and one or more others. (Note that a VIF Return to top of page. Often X is a variable which logically can never go to zero, or even close to it, given the way it is defined. Standard Error Of Slope Calculator In a multiple regression model in which k is the number of independent variables, the n-2 term that appears in the formulas for the standard error of the regression and adjusted

Also, if X and Y are perfectly positively correlated, i.e., if Y is an exact positive linear function of X, then Y*t = X*t for all t, and the formula for In case (i)--i.e., redundancy--the estimated coefficients of the two variables are often large in magnitude, with standard errors that are also large, and they are not economically meaningful. To do this, first click and drag from the cell containing your formula so that you end up with a selection consisting of all the cells in 5 rows and 2 Check This Out The usual default value for the confidence level is 95%, for which the critical t-value is T.INV.2T(0.05, n - 2).

You can use regression software to fit this model and produce all of the standard table and chart output by merely not selecting any independent variables. Hence, it is equivalent to say that your goal is to minimize the standard error of the regression or to maximize adjusted R-squared through your choice of X, other things being However, it can be converted into an equivalent linear model via the logarithm transformation. For the case in which there are two or more independent variables, a so-called multiple regression model, the calculations are not too much harder if you are familiar with how to

The estimated coefficient b1 is the slope of the regression line, i.e., the predicted change in Y per unit of change in X. What's the bottom line? A pair of variables is said to be statistically independent if they are not only linearly independent but also utterly uninformative with respect to each other. X Collapse Posts Latest Activity Search Page of 1 Filter Time All Time Today Last Week Last Month Show All Discussions only Photos only Videos only Links only Polls only Filtered

However, if one or more of the independent variable had relatively extreme values at that point, the outlier may have a large influence on the estimates of the corresponding coefficients: e.g., That is, the absolute change in Y is proportional to the absolute change in X1, with the coefficient b1 representing the constant of proportionality. In many applications (perhaps even the vast majority), zero is not a possible value for a covariate. Once we have our fitted model, the standard error for the intercept means the same thing as any other standard error: It is our estimate of the standard deviation of the

Not the answer you're looking for? There are various formulas for it, but the one that is most intuitive is expressed in terms of the standardized values of the variables. But outliers can spell trouble for models fitted to small data sets: since the sum of squares of the residuals is the basis for estimating parameters and calculating error statistics and In general, the standard error of the coefficient for variable X is equal to the standard error of the regression times a factor that depends only on the values of X

The explained part may be considered to have used up p-1 degrees of freedom (since this is the number of coefficients estimated besides the constant), and the unexplained part has the But the standard deviation is not exactly known; instead, we have only an estimate of it, namely the standard error of the coefficient estimate.