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
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.
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.
go to stat mode then then select (A+BX) mode and enter the data and press AC on cal. then shift+1 and go to the stat and select REG and there you can see options like A,B and r u can select any of these to get what u need .if you want the answer for r select that option. thnx.
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.
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.
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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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.
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.
You should get the HP 33S Scientific Calculator because it has 32KB of memory, keystroke programming, linear regression, binary calculation and conversion, trigonometric, inverse-trigonometric and hyperbolic functions
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".
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.
You need observations in education so you have a clear view of what is ahead
If you google "Past Life Regression Therapist" you will get all the information you need to find one near you.
observations are very important because is something u need
a+b=16 is a linear equation in two variables. The variables are a and b. To solve a linear equation in 2 variables we need more information. We need either another equation, or a value to put in for a or b.Since we have neither, there is no more we can do.
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.