0
You use it when the relationship between the two variables of interest is linear. That is, if a constant change in one variable is expected to be accompanied by a constant [possibly different from the first variable] change in the other variable.
Note that I used the phrase "accompanied by" rather than "caused by" or "results in". There is no need for a causal relationship between the variables.
A simple linear regression may also be used after the original data have been transformed in such a way that the relationship between the transformed variables is linear.
Wiki User
∙ 9y ago
Add your answer:
Earn +20 pts
Is the line of best fit the same as linear regression?
Linear Regression is a method to generate a "Line of Best fit" yes you can use it, but it depends on the data as to accuracy, standard deviation, etc. there are other types of regression like polynomial regression.
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.
How can i use the word regression in a sentence?
Her regression is smoking.
What is an example sentence with the word regression?
i know the facts. What is the reason? For your Regression?
What is the normal probability plot of residuals?
When you use linear regression to model the data, there will typically be some amount of error between the predicted value as calculated from your model, and each data point. These differences are called "residuals". If those residuals appear to be essentially random noise (i.e. they resemble a normal (a.k.a. "Gaussian") distribution), then that offers support that your linear model is a good one for the data. However, if your errors are not normally distributed, then they are likely correlated in some way which indicates that your model is not adequately taking into consideration some factor in your data. It could mean that your data is non-linear and that linear regression is not the appropriate modeling technique.
Is the line of best fit the same as linear regression?
Linear Regression is a method to generate a "Line of Best fit" yes you can use it, but it depends on the data as to accuracy, standard deviation, etc. there are other types of regression like polynomial regression.
What do researchers use to represent graphically the correlation between two variables?
A linear regression
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.
What serves as a standard of comparison to evaluate the effect of the dependent variable on the dependent variable?
ControlThe answer will depend on the nature of the effect. IFseveral requirements are met (the effect is linear, the "errors" are independent and have the same variance across the set of values that the independent variable can take (homoscedasticity) then, and only then, a linear regression is a standard. All to often people use regression when the data do not warrant its use.
How can i use the word regression in a sentence?
Her regression is smoking.
How would you find the equation for a linear function when you are given a table of values for the variables?
If the figures in the table are exact and without measurement error then take any two of the points (x1, y1) and (x2, y2) and use these to form the linear relation y - y1 = ((y2 - y1)/(x2 - x1))(x - x1) If, however, you suspect that the values in the table do not exactly follow a linear relationship then use linear regression for which formulae are provided in wikipedia.
What is multiple and partial correlation?
multiple correlation: Suppose you calculate the linear regression of a single dependent variable on more than one independent variable and that you include a mean in the linear model. The multiple correlation is analogous to the statistic that is obtainable from a linear model that includes just one independent variable. It measures the degree to which the linear model given by the linear regression is valuable as a predictor of the independent variable. For calculation details you might wish to see the wikipedia article for this statistic. partial correlation: Let's say you have a dependent variable Y and a collection of independent variables X1, X2, X3. You might for some reason be interested in the partial correlation of Y and X3. Then you would calculate the linear regression of Y on just X1 and X2. Knowing the coefficients of this linear model you would calculate the so-called residuals which would be the parts of Y unaccounted for by the model or, in other words, the differences between the Y's and the values given by b1X1 + b2X2 where b1 and b2 are the model coefficients from the regression. Now you would calculate the correlation between these residuals and the X3 values to obtain the partial correlation of X3 with Y given X1 and X2. Intuitively, we use the first regression and residual calculation to account for the explanatory power of X1 and X2. Having done that we calculate the correlation coefficient to learn whether any more explanatory power is left for X3 to 'mop up'.
How can you find a linear relation between time t and another variable containing Vc in an RC circuit and then use linear regression to find the slope and intercept of these two variables?
-τ(ln (Vo-Vc/Vo)=t Mgk is that all
When can't i use SPSS and why?
The answer depends on the context.You cannot use SPSS if you have no computer. The reason is that SPSS is a computer based analysis package.You cannot use SPSS if you have no data. There must be an input into SPSS.You cannot use SPSS if your assumptions are not supported by the data. For example doing a linear regression for a relationship that is clearly non-linear. Technically, you CAN use SPSS but the reults will be wrong.
If the regression sum of squares is large relative to the error sum of squares is the regression equation useful for making predictions?
If the regression sum of squares is the explained sum of squares. That is, the sum of squares generated by the regression line. Then you would want the regression sum of squares to be as big as possible since, then the regression line would explain the dispersion of the data well. Alternatively, use the R^2 ratio, which is the ratio of the explained sum of squares to the total sum of squares. (which ranges from 0 to 1) and hence a large number (0.9) would be preferred to (0.2).
What is an example sentence with the word regression?
i know the facts. What is the reason? For your Regression?
What is the difference between multivariate regression and multiple regression?
Although not everyone follows this naming convention, multiple regression typically refers to regression models with a single dependent variable and two or more predictor variables. In multivariate regression, by contrast, there are multiple dependent variables, and any number of predictors. Using this naming convention, some people further distinguish "multivariate multiple regression," a term which makes explicit that there are two or more dependent variables as well as two or more independent variables.In short, multiple regression is by far the more familiar form, although logically and computationally the two forms are extremely similar.Multivariate regression is most useful for more special problems such as compound tests of coefficients. For example, you might want to know if SAT scores have the same predictive power for a student's grades in the second semester of college as they do in the first. One option would be to run two separate simple regressions and eyeball the results to see if the coefficients look similar. But if you want a formal probability test of whether the relationship differs, you could run it instead as a multivariate regression analysis. The coefficient estimates will be the same, but you will be able to directly test for their equality or other properties of interest.In practical terms, the way you produce a multivariate analysis using statistical software is always at least a little different from multiple regression. In some packages you can use the same commands for both but with different options; but in a number of packages you use completely different commands to obtain a multivariate analysis.A final note is that the term "multivariate regression" is sometimes confused with nonlinear regression; in other words, the regression flavors besides Ordinary Least Squares (OLS) linear regression. Those forms are more accurately called nonlinear or generalized linear models because there is nothing distinctively "multivariate" about them in the sense described above. Some of them have commonly used multivariate forms, too, but these are often called "multinomial" regressions in the case of models for categorical dependent variables.
