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18.05 – Discuss the Pearson product-moment correlation coefficient.
52. The NFL office discovered data covering attendance at professional football games in the late 1940s and early 1950s.
The game with the highest attendance was between the St. Louis Cardinals and the New York Giants. The office also
found considerable information that someone had collected on each game day such as the level of GDP, the Dow,
number of persons employed, number of new businesses formed during the week preceding the game, and the
population. A student intern took the information and built a regression model to predict game attendance for the
upcoming season. The model should
accurately predict game attendance.
NOT predict game attendance accurately because the variable levels of today (i.e., population, Dow, etc.) are
out of range of those used to build the regression model.
predict game attendance accurately because the variable levels of today (i.e., population, Dow, etc.) are out
of range of those used to build the regression model.
predict game attendance accurately because the variable levels (i.e., population, Dow, etc.) are within range
of those used to build the regression model.
None of these are correct.
The model should not predict game attendance accurately because the variable
levels of today (i.e., population, Dow, etc.) are out of range of those used to build
the regression model. See 18–5: Regression Analysis.
18.06 – Discuss a technique for examining the influence of one or more predictor
variables on an outcome variable.
53. If the F-value in ANOVA produces a significantly high p-value (a.k.a. “Sig.” in SPSS) of 0.11 or more, then it is
appropriate to proceed with a post-hoc test (e.g., Duncan).
18.06 – Discuss a technique for examining the influence of one or more predictor