0
a random pattern
Wiki User
∙ 11y ago
Add your answer:
Earn +20 pts
What is a good sentence with the word regression?
The plant began its regression when the rain stopped falling.
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.
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.
Requirements of regression analysis in statistics?
You may get more ideas from wikipedia under regression analysis. You can do a regression analysis with as little as 2 x,y points- but is it meaningful? Requirements for valid or meaningful relationships can be subjective. However, in my opinion, if meaningful relationships are to be created using regression analysis, the following are important: a) The independent variable should have values that are independent (no relation exists between them). b) There should be a good rational or experimental basis for identifying the independent variables and the resultant dependent variable. c) Sufficient data should be collected in a controlled environment to identify the relationship. d) The validity of the relationship should easy to identify both visually and by numbers (see "goodness of fit" tests).
What is the advantages and disadvantages of multiple regression analysis?
Advantages: The estimates of the unknown parameters obtained from linear least squares regression are the optimal. Estimates from a broad class of possible parameter estimates under the usual assumptions are used for process modeling. It uses data very efficiently. Good results can be obtained with relatively small data sets. The theory associated with linear regression is well-understood and allows for construction of different types of easily-interpretable statistical intervals for predictions, calibrations, and optimizations. Disadvantages: Outputs of regression can lie outside of the range [0,1]. It has limitations in the shapes that linear models can assume over long ranges The extrapolation properties will be possibly poor It is very sensitive to outliers It often gives optimal estimates of the unknown parameters.
Difference between negative health and positive health?
Negative health is a bad thing, it shows that there is regression. Positive health on the other hand, is a good thing. It shows improvement.
What is a good sentence with the word regression?
The plant began its regression when the rain stopped falling.
How is bohr model a good model?
its a good model because it shows you the atom written out. it also helps to show you the valence electron number
What is a good idea for a scatter plot?
I like to use age and height in a scatter plot using male and female separate then together. It shows two lines of regression.
What is the relationship between correlation coefficient and linear regreassion?
A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.
Is Taylor swift a good role model for kids?
Taylor Swift is a great role model. you can not find a better role model.
Is past life regression safe if you do it alone?
Good question! I'd ask a doctor first.
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 Lance Armstrong a good role model for kids?
He is an excellent role model for kids because he shows that no matter how sick you have been, if you try hard during your recovery, you can still do great things.
Why is the globe a good model of the earth?
The globe is a good model of the Earth because, it shows the polar and equatorial diameter. It shows all the continents and their latitudes and longitudes as well. And, it is tilted like the Earth on its axis. Physical globes can also have raised topography to somewhat indicate the elevations of the land surfaces around the world.
What is the significance of smaller Root mean square error?
When we use linear regression to predict values, we input a given x value and we use the equation of the correlation line to predict the y values. Sometimes we want to know how spread out the y values are. We look at the difference between the predicted and the actual y values. These differences are called residual and they are either positive if the y value is more than the estimated y value or negative if it is less. So for example if the observed value is 10 and the predicted one is 15, the residual is 15-10=5. Now we can find the residual for each y value in our data set and square it. Then we can take the average of those squares. Last, we take the square root of the average of the squared residuals and this is the RMS or root mean square error. The units are the same as the y values. If the RMS error is big, then the y values are not too close to the predicted ones on the y value and the our line does not provide as good of a model to predict values. If it is small, the y values are well predicted by the regression line. For a horizontal line, the RMS error is the same as the standard deviation. r is the regression coefficient and it measures how closely clustered the points are relative to the standard deviaton. The RMS error measures the spread in the original y units.
What is the definition for 'residual value'?
Residual value is the future value of a good after depreciation of its initial value. For example you bought a car for $20,000. After two years and 60,000 of mileage it will value of $10,000.
