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What does f statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
Is the random error in a regression equation the predicted error?
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Is a t-test a descriptive statistic?
A t-test is a inferential statistic. Other inferential statistics are confidence interval, margin of error, and ANOVA. An inferential statistic infers something about a population. A descriptive statistic describes a population. Descriptive statistics include percentages, means, variance, and regression.
What are some of the advantages and disadvantages of making forecasts using regression methods?
+ Linear regression is a simple statistical process and so is easy to carry out. + Some non-linear relationships can be converted to linear relationships using simple transformations. - The error structure may not be suitable for regression (independent, identically distributed). - The regression model used may not be appropriate or an important variable may have been omitted. - The residual error may be too large.
What is normal error term?
Q statistic
What does f statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
What does a large F-statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
Is the random error in a regression equation the predicted error?
pig benis
Is a t-test a descriptive statistic?
A t-test is a inferential statistic. Other inferential statistics are confidence interval, margin of error, and ANOVA. An inferential statistic infers something about a population. A descriptive statistic describes a population. Descriptive statistics include percentages, means, variance, and regression.
If amount of error along regression line is similar is this homoscedasticity?
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What is the role of the stochastic error term in regression analysis?
Regression analysis is based on the assumption that the dependent variable is distributed according some function of the independent variables together with independent identically distributed random errors. If the error terms were not stochastic then some of the properties of the regression analysis are not valid.
What does a high t statistic mean?
Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated. Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated.
The random error in a regression equation?
includes both positive and negative terms.
What are the sources of error in regression model?
Random error, measurement error, mis-specification of model (overspecification or underspecification), non-normality, plus many more.
What are some of the advantages and disadvantages of making forecasts using regression methods?
+ Linear regression is a simple statistical process and so is easy to carry out. + Some non-linear relationships can be converted to linear relationships using simple transformations. - The error structure may not be suitable for regression (independent, identically distributed). - The regression model used may not be appropriate or an important variable may have been omitted. - The residual error may be too large.
Why are your predictions inaccurate using a linear regression model?
There are many possible reasons. Here are some of the more common ones: The underlying relationship is not be linear. The regression has very poor predictive power (coefficient of regression close to zero). The errors are not independent, identical, normally distributed. Outliers distorting regression. Calculation error.
How does a linear regression allow us to better estimate trends costs and other factors in complex situations?
You question is how linear regression improves estimates of trends. Generally trends are used to estimate future costs, but they may also be used to compare one product to another. I think first you must define what linear regression is, and what the alternative forecast methods exists. Linear regression does not necessary lead to improved estimates, but it has advantages over other estimation procesures. Linear regression is a mathematical procedure that calculates a "best fit" line through the data. It is called a best fit line because the parameters of the line will minimizes the sum of the squared errors (SSE). The error is the difference between the calculated dependent variable value (usually y values) and actual their value. One can spot data trends and simply draw a line through them, and consider this a good fit of the data. If you are interested in forecasting, there are many methods available. One can use more complex forecasting methods, including time series analysis (ARIMA methods, weighted linear regression, or multivariant regression or stochastic modeling for forecasting. The advantages to linear regression are that a) it will provide a single slope or trend, b) the fit of the data should be unbiased, c) the fit minimizes error and d) it will be consistent. If in your example, the errors from regression from fitting the cost data can be considered random deviations from the trend, then the fitted line will be unbiased. Linear regression is consistent because anyone who calculates the trend from the same dataset will have the same value. Linear regression will be precise but that does not mean that they will be accurate. I hope this answers your question. If not, perhaps you can ask an additional question with more specifics.
