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Analytics W5
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- 1For multiple regression models, ____________ are an indispensable tool for detecting whether the linear model is a good fit.residual plots
- 2when independent variables are added to a regression, what happens to R2?R2 never decreases
- 3when comparing regression models with different numbers of independent variables, does one look at R2 or Adj R2?Adjusted R2
- 4What are the steps to analyzing multiple regression relationships?1. analyzing Adjusted R2
2. check residual plots: test whether the relationship between the dependent variable and independents variables is linear and significant
3. analyze the p-values of the independent variables to determine whether there is a significant relationship between the variables in the model. - 5if the underlying multiple relationship is non- linear amongst the multiple independent variables we are analyzing, what will we see? (name 2 things)1. heteroskedasticity
2. nonlinearity. - 6if the underlying multiple relationship is linear, what will we see on the residual plots?the residuals will follow a normal distribution with a mean of zero and fixed variance.
- 7What is Multicollinearity?Multicollinearity occurs when two independent variables are so highly correlated that it is difficult for the regression model to separate the effect each variable has on the dependent variable.
- 8if a variable that was significant becomes insignificant when we add it to a regression model, what does this imply?It implies that a relationship exists
between two or more of the independent variables. - 9When two or more independent variables are highly correlated, we say they're________collinear
- 10One way to detect multicollinearity is :checking to see if any variable's P value increases when a new independent variable is added.
- 11multicollinearity is a problem that should be addressed under what circumstances?If we're trying to understand the net effects
of the independent variables - 12Name 2 ways to reduce multicolliarity1. increase the sample size.
2.removing one of the collinear independent variables. - 13by introducing a lagged variable, we lose ______1 data point
- 14Adding a lagged variable is costly in two ways:The loss of a data point decreases our sample size, which reduces the precision of our estimates of the regression coefficients.
In addition, if the lagged variable does not increase the model’s explanatory power, the addition of the variable decreases Adjusted R2. - 15—
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- Professional (Business Analytics) Harvard CoRE Fichas sobre Analytics W5, creado por Georgia Tan el 16/08/2015.
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