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A subsection of the question above. You have to demonstrate an understanding of what the typical goals of a logistic regression are (classification, prediction, etc.) and bring up a few examples and use cases.
saketh varma
To evaluate a logistic regression model, you can start by analyzing coefficient values to determine the significance and direction of each predictor variable. Next, you can examine the goodness-of-fit measures like deviance or chi-square tests to assess how well the model fits the data. Finally, you can apply validation techniques like cross-validation or holdout sample testing to evaluate the model's performance on new data.
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What is the minimum required sample size for running logistic regression?
In fitting a logistic regression, as in applying any statistic method, the required sample size depends on the level of dispersion in the population and the quality of the statistics that you are prepared to accept. Usually there will be some information available somewhere (in the literature, say, or from colleagues) suggesting what level of variability to expect in data that is collected. This can be used to simulate some data sets and the logistic regression results that would arise from them. By varying the sizes of the data sets you can make a judgement. Once you have collected your first sample and fit the actual logistic regression to it you will have a much better idea how much data is actually required for useful estimates.
How would you use the word logistic in a sentence?
Danny found the logistic problem of planning his paper route to be daunting.
What is the value of r in the linear regression equation 67.50-4.90x?
Beautiful! Our patience has finally paid off. We knew it would happen some day, and now it has. We're asked to evaluate a variable in an equation in which that variable doesn't even appear.
Why is the independent variable the only thing that changes?
It is not. If it were, there would be no regression or correlation.
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'.
What is the minimum required sample size for running logistic regression?
In fitting a logistic regression, as in applying any statistic method, the required sample size depends on the level of dispersion in the population and the quality of the statistics that you are prepared to accept. Usually there will be some information available somewhere (in the literature, say, or from colleagues) suggesting what level of variability to expect in data that is collected. This can be used to simulate some data sets and the logistic regression results that would arise from them. By varying the sizes of the data sets you can make a judgement. Once you have collected your first sample and fit the actual logistic regression to it you will have a much better idea how much data is actually required for useful estimates.
What would An s shaped population growth curve best describes what?
logistic growth
If you were to plot the height of everyone in your class on graph the values would probable form a hill-shaped curve called?
bell curve
How would you use the word logistic in a sentence?
Danny found the logistic problem of planning his paper route to be daunting.
What regression method would be used when there is more than one independent variable?
multivariate regression
What is the value of r in the linear regression equation 67.50-4.90x?
Beautiful! Our patience has finally paid off. We knew it would happen some day, and now it has. We're asked to evaluate a variable in an equation in which that variable doesn't even appear.
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).
Which Section would you go to obtain communications equipment or transportation?
Logistic Sections
When would expect to see a species go through a logistic growth pattern?
A logistic growth pattern occurs when the population reaches its carrying capacity, at which point the population growth is zero.
Internet regression would most clearly match the view of?
Pshychoanalysis.
Why is the independent variable the only thing that changes?
It is not. If it were, there would be no regression or correlation.
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'.
