Is this multiple regression model any good at all?
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Test hypotheses: HO: all beta values = 0 vs. HA: at least 1 beta value does
not = 0
-then, use a t-test
Sum of Squared Errors (SSE)
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want a smaller SSE; a larger SSE means that the residuals are more variable
and that our predictions will be correspondingly less precise
,Durbin-Watson statistic
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-can detect first-order autocorrelation from the residuals of a regression
analysis
-it estimates the autocorrelation by summing r-squared comparing the sum
with its expected value under the null hypothesis of no autocorrelation
-under 2: positive autocorrelation
Interpreting Residuals
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-Negative residual: the regression equation provided an overestimate of
the data.
-Positive residual: the regression equation provided an underestimation of
the data.
Interpreting more than 1 dummy variable
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, Interpret the coefficient of Location1: After accounting for the predictor
LivingArea, on average, the house sales price for Location1 is ___ dollars
higher/lower than the house sales price for Location4.
OR
Why is there no coefficient for Medium? Medium was selected for the
baseline, so no indicator variable is needed.
Interpret the coefficient for Small. On average, the Total Delay Per Person
in small cities is 3.6 hours higher than that in medium cities, after allowing
for the effects of highway and arterial speeds.
Four components of a time series
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1. Trend component (T)
2. Seasonal component (S)
3. Cyclical component (C)
4. Irregular component (I)
Why include a categorical variable?
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to better predict the dependent variable and make the residual smaller
F-Test Interpretation
Give this one a try later!
Give this one a try later!
Test hypotheses: HO: all beta values = 0 vs. HA: at least 1 beta value does
not = 0
-then, use a t-test
Sum of Squared Errors (SSE)
Give this one a try later!
want a smaller SSE; a larger SSE means that the residuals are more variable
and that our predictions will be correspondingly less precise
,Durbin-Watson statistic
Give this one a try later!
-can detect first-order autocorrelation from the residuals of a regression
analysis
-it estimates the autocorrelation by summing r-squared comparing the sum
with its expected value under the null hypothesis of no autocorrelation
-under 2: positive autocorrelation
Interpreting Residuals
Give this one a try later!
-Negative residual: the regression equation provided an overestimate of
the data.
-Positive residual: the regression equation provided an underestimation of
the data.
Interpreting more than 1 dummy variable
Give this one a try later!
, Interpret the coefficient of Location1: After accounting for the predictor
LivingArea, on average, the house sales price for Location1 is ___ dollars
higher/lower than the house sales price for Location4.
OR
Why is there no coefficient for Medium? Medium was selected for the
baseline, so no indicator variable is needed.
Interpret the coefficient for Small. On average, the Total Delay Per Person
in small cities is 3.6 hours higher than that in medium cities, after allowing
for the effects of highway and arterial speeds.
Four components of a time series
Give this one a try later!
1. Trend component (T)
2. Seasonal component (S)
3. Cyclical component (C)
4. Irregular component (I)
Why include a categorical variable?
Give this one a try later!
to better predict the dependent variable and make the residual smaller
F-Test Interpretation
Give this one a try later!
