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Do you need more than 10 observations in linear regression?
Tartinho
This is a difficult question to answer. The pure answer is no. In reality, it depends on the level of randomness in the data. If you plot the data, it will give you an idea of the randomness. Even with 10 data points, 1 or 2 outliers can significantly change the regression equation. I am not aware of a rule of thumb on the minimum number of data points. Obviously, the more the better. Also, calculate the correlation coefficient. Be sure to follow the rules of regression. See the following website: http:/www.duke.edu/~rnau/testing.htm
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∙ 14y ago
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What is the difference between simple and multiple linear regression?
I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.
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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.
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For an ordinary bar graph you need two variables, the dependent variable being numerical. You need at least two observations - unless you want a bar graph that serves no purpose. You could have more than one dependent variables for a stacked or grouped bar graph.
Convert X intervals to z intervals?
The answer depends on the relationship between X and z. This may not be linear, in which case the conversion may need to take account of turning points.
What is the difference between simple and multiple linear regression?
I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.
True of false linear regression question. The need for possible transformation in regressor variables can be aided by observing the leverage, or partial regression plots?
true
Why is it important to look at a scatter plot prior to starting a simple linear regression?
To see if there is a linear relationship between the dependent and independent variables. The relationship may not be linear but of a higher degree polynomial, exponential, logarithmic etc. In that case the variable(s) may need to be transformed before carrying out a regression. It is also important to check that the data are homoscedastic, that is to say, the error (variance) remains the same across the values that the independent variable takes. If not, a transformation may be appropriate before starting a simple linear regression.
When would you use simple linear regression?
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.
What kind of calculator do I need for my Trigonometry class?
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What is regression line?
Regression techniques are used to find the best relationship between two or more variables. Here, best is defined according to some statistical criteria. The regression line is the straight line or curve based on this relationship. The relationship need not be a straight line - it could be a curve. For example, the regression between many common variables in physics will follow the "inverse square law".
Distinguish between correlation and regression?
Correlation is a measure of the degree of agreement in the changes (variances) in two or more variables. In the case of two variables, if one of them increases by the same amount for a unit increase in the other, then the correlation coefficient is +1. If one of them decreases by the same amount for a unit increase in the other, then the correlation coefficient is -1. Lesser agreement results in an intermediate value. Regression involves estimating or quantifying this relationship. It is very important to remember that correlation and regression measure only the linear relationship between variables. A symmetrical relationshup, for example, y = x2 between values of x with equal magnitudes (-a < x < a), has a correlation coefficient of 0, and the regression line will be a horizontal line. Also, a relationship found using correlation or regression need not be causal.
Why do you need to have observations in education?
You need observations in education so you have a clear view of what is ahead
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If you google "Past Life Regression Therapist" you will get all the information you need to find one near you.
Why are observations are important?
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The position and value of any two observations. In terms of coordinates, (x1,y1) and (x2, y2) for any two points provided that x1 is not the same as x2.
When regression is not applicable?
There are many cases where you won't get useful results from regression. The two most common kinds of issues are (1) when your data contain major violations of regression assumptions and (2) when you don't have enough data (or of the right kinds). Core assumptions behind regression include - That there is in fact a relationship between the outcome variable and the predictor variables. - That observations are independent. - That the residuals are normally distributed and independent of the values of variables in the model. - That each predictor variable is not a linear combination of any others and is not extremely correlated with any others. - Additional assumptions depend on the nature of your dependent variable; for example whether it is measured on a continuous scale or is categorical yes/no etc. The form of regression you use (linear, logistic, etc.) must match the type of data. Not having enough data means having very few cases at all or having large amounts of missing values for the variables you want to analyze. If you don't have enough observations, your model either will not be able to run or else the estimates could be so imprecise (with large standard errors) that they aren't useful. A generic rule some people cite is that you need 10-20 cases per variable in the model; there's nothing magic about that number and you might get by just fine with less, but it suggests you could run into trouble if you have much less that that. Missing values can be a big problem as well and in the worst case could skew your results if they are not handled properly.
