good question.
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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.
Provided that the correct model is used, the theoretical probability is correct. The experimental probability tends towards the theoretical value as the number of trials increases.Provided that the correct model is used, the theoretical probability is correct. The experimental probability tends towards the theoretical value as the number of trials increases.Provided that the correct model is used, the theoretical probability is correct. The experimental probability tends towards the theoretical value as the number of trials increases.Provided that the correct model is used, the theoretical probability is correct. The experimental probability tends towards the theoretical value as the number of trials increases.
No.
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You improve your model through a better understanding of the underlying processes. Although more trials will improve the accuracy of experimental probability they will make no difference to the theoretical probability.
Depends on your definition of "linear" For someone taking basic math - algebra, trigonometry, etc - yes. Linear means "on the same line." For a statistician/econometrician? No. "Linear" has nothing to do with lines. A "linear" model means that the terms of the model are additive. The "general linear model" has a probability density as a solution set, not a line...
The profit maximizing point on the graph for this business model is where the marginal revenue equals the marginal cost.
A model in which your mother.
It is a linear model.
The confidence intervals will increase. How much it will increase depends on whether the underlying probability model is additive or multiplicative.
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.
It's a measure of how well a simple linear model accounts for observed variation.
when does it make sense to choose a linear function to model a set of data
In theoretical probability, the probability is determined by an assumed model (for example, the normal distribution). (compare with empirical probability)
In an economic model, the marginal rate of substitution between two goods is calculated by finding the ratio of the marginal utility of one good to the marginal utility of the other good. This ratio represents the rate at which a consumer is willing to trade one good for another while maintaining the same level of satisfaction.
marginal utility model ....i think circular flow model....
A model in which your mother.