Dear all,
Usually economics papers report standard errors in the parentheses of regression tables. However, in some cases, regression tables exported from stata report t-statistics by default, rather than standard errors.
I think that qualitatively speaking both the two statistics should be ok for regression tables (although standard errors are easier to be interpreted). But I am not sure if it is acceptable to report t-statistics in a regression table.
Hi Teng
Your question is not clear, and you combined two issues:
A. First issue:
The standard error(SE) is very similar to standard deviation. Both are measures of spread. The higher the number, the more spread out your data is. To put it simply, the two terms are essentially equal — but there is one important difference. While the standard error uses statistics (sample data) standard deviations use parameters (population data).
1. The standard error is the estimated standard deviation or measure of variability in the sampling distribution of a statistic. A low standard error means there is relatively less spread in the sampling distribution. The standard error indicates the likely accuracy of the sample mean as compared with the population mean. The standard error decreases as the sample size increases and approaches the size of the population. Sigma (σ) denotes the standard error; a subscript indicates the statistic. For example, the standard error of the mean is represented by σM. To find the standard error of the mean, divide the standard deviation by the square root of the sample size: , where σ is the standard deviation of the original sampling distribution and N is the sample size.
2. In order to quantify the standard error, a good sample has to be drawn from the target population. The key to a good sample is that it has to be typical of the population from which it is drawn.
3. These are the few factor need to be controlled, in order to get good sample and to quantify the errors and variation:
- Proper planning of the study;
- Use of appropriate statistical design and methods for study;
- Appropriate sampling methods;
- Randomization
- Reduce investigator bias;
- Use appropriate and large sample size with consideration of power for the calculation;
- Use of Standardized procedures and
- Appropriate statistical Techniques.
B. Second issue:
1. Linear regression model/analysis is a technique used to predict the value of one quantitative variable by using its relationship with one or more additional quantitative variables. For example, if we know the relationship between height and weight in adult. males, we can use regression analysis to predict weight given a particular value for height.
The relationship between height and weight is familiar to us; generally, the taller a person is, the more he weighs. Another example of a familiar relationship is that of crop yield and the amount of fertilizer applied to the land; the more fertilizer applied to the land, the greater the yield-to a point. If too much fertilizer is applied, the crop will be killed off by the fertilizer chemicals-the land will be "burned." An important relationship in business is the relationship between the allocation of dollars to advertising effort and the level of sales of a product; the more money expended in advertising, the greater the level of sales (in general).
2. The simple linear regression model is a mathematical way of stating the statistical relationship that exists between two variables. The two principle elements of a statistical relationship are:
(1) the tendency of the dependent variable Y to vary in a systematic way with the independent variable X, and
(2) the scattering of points about the "curve" that represents the relationship between X and Y.
These two elements of a statistical relationship are represented in a simple linear regression model by assuming that:
(i) there is a probability distribution of Y for each value of X, and
(ii) the means of these probability distributions fall perfectly on a line.
Regards,
Zuhair
- Badges
- Science topic
- Similar topics
- Mathematics
- Statistics
- Regression