Consider a multiple regression model for predicting the total number of runs scored by a Major LeagueBaseball (MLB) team during a season. Using data on number of walks (x1), singles (x2), doubles (x3), triples (x4), and home runs (x5) for each of the 30 teams during the 2017 MLB season, a first-order model for total number of runs scored (y) was fit. The selected results are shown in the SAS printout below. Parameter Estimates Parameter Standard Variable DF Estimate t Value Pr > |t| Error Intercept 1 -580.76485 96.97966 -5.99 <.0001 WALKS 1 0.44168 0.07910 5.58 <.0001 SINGLES 1 0.65536 0.08143 8.05 <.0001 DOUBLES 1 0.70413 0.16510 4.26 0.0003 TRIPLES 1 0.79719 0.50228 1.59 0.1256 HOMERUNS 1 1.44574 0.17178 8.42 <.0001 Find B, and interpret it to the context. Estimate is Interpretation: 2. [1 Construct a 95% confidence interval for ß4.

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3. [1 int] Do you think TRIPLES can be dropped from the model if all four other variables are already
there? Briefly explain.
We
(will, will not) drop TRIPLES because
is the most important in predicting the total
4.
Among these five predictors,
number of runs scored because
Transcribed Image Text:3. [1 int] Do you think TRIPLES can be dropped from the model if all four other variables are already there? Briefly explain. We (will, will not) drop TRIPLES because is the most important in predicting the total 4. Among these five predictors, number of runs scored because
Consider a multiple regression model for predicting the total number of runs scored by a Major
LeagueBaseball (MLB) team during a season. Using data on number of walks (x1), singles (x2), doubles (x3),
triples (x4), and home runs (x5) for each of the 30 teams during the 2017 MLB season, a first-order model for
total number of runs scored (y) was fit. The selected results are shown in the SAS printout below.
Parameter Estimates
Parameter
Standard
Variable
DF
Estimate
Error
t Value Pr > |t|
Intercept
-580.76485
96.97966
-5.99
<.0001
WALKS
1
0.44168
0.07910
5.58
<.0001
SINGLES
1
0.65536
0.08143
8.05
<.0001
DOUBLES
1
0.70413
0.16510
4.26
0.0003
TRIPLES
1
0.79719
0.50228
1.59
0.1256
HOMERUNS
1
1.44574
0.17178
8.42
<.0001
] Find B, and interpret it to the context.
Estimate is
Interpretation:
2. [1.
Construct a 95% confidence interval for B4.
Estimate
Transcribed Image Text:Consider a multiple regression model for predicting the total number of runs scored by a Major LeagueBaseball (MLB) team during a season. Using data on number of walks (x1), singles (x2), doubles (x3), triples (x4), and home runs (x5) for each of the 30 teams during the 2017 MLB season, a first-order model for total number of runs scored (y) was fit. The selected results are shown in the SAS printout below. Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > |t| Intercept -580.76485 96.97966 -5.99 <.0001 WALKS 1 0.44168 0.07910 5.58 <.0001 SINGLES 1 0.65536 0.08143 8.05 <.0001 DOUBLES 1 0.70413 0.16510 4.26 0.0003 TRIPLES 1 0.79719 0.50228 1.59 0.1256 HOMERUNS 1 1.44574 0.17178 8.42 <.0001 ] Find B, and interpret it to the context. Estimate is Interpretation: 2. [1. Construct a 95% confidence interval for B4. Estimate
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