Topic 7: Empirical Issues Flashcards

1
Q

How would you find the SST for a model with SSR given?

A

R^2 = 1- SSR/SST

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2
Q

The change in y caused by a change in x in a model with a quadratic x is?

A

The partial derivative of the model WRT x.

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3
Q

When another variable is added to a model, the R squared only goes up or down?

A

It only can go up. However, the adjusted R squared can go up or down.

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4
Q

What is adjusted R Squared?

A

A goodness of fit measure in MLR analysis that penalizes additional explanatory variables by using a degrees of freedom adjustment in estimating the error variance.

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5
Q

What is over-controlling?

A

Including explanatory variables that should not be held fixed when studying the ceteris paribus effect of one or more other explanatory variables, like when an explanatory variable is a pathway through which the variable of interest affects the dependent variable.

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6
Q

What is a prediction interval?

A

A confidence interval for an unknown outcome (prediction, fitted estimate) on a dependent variable in a MLR model. Is always wider than CI.

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7
Q

What is the predicted effect from a one unit increase in x1, holding x2 constant in a model with a quadratic (and multiple x1 terms and holding other terms constant)?

A

It is the partial derivative of the model with respect to x1.

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8
Q

How would you find an estimated increase in y given beta1x2 + beta2x2, where x2=50?

A

You would run a regression, find the estimated coefficients for beta1 and beta2. Plug all numbers into the equation (including 50) to get the change in y.

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9
Q

How does data scaling affect the independent and dependent variables?

A

Changing the scaling of the y variable changes every else the same about, but changing the x variable, only changes that one. If your x is now divided by 100 your beta coefficient for that will be the inverse, times 100 to make the y value and all others the same.

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10
Q

What are beta coefficients?

A

Also known as standardized coefficients, you calculate the z score by subtracting the mean from the estimate and dividing by standard error, we know know how many “standard units” (deviations) that value is.

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11
Q

What is useful about standardized coefficients?

A

If they are all standardized in a regression, you can tell which is the most important by which is the biggest.

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12
Q

What is the maximum value of a quadratic function?

A

x* = | (beta^1) \ (2beta^2) |

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