Examlex

Solved

The Following MINITAB Output Presents a Multiple Regression Equation y^=b0+b1x1+b2x2+b3x3\hat { y } = b _ { 0 } + b _ { 1 } x _ { 1 } + b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 }

question 31

Multiple Choice

The following MINITAB output presents a multiple regression equation y^=b0+b1x1+b2x2+b3x3\hat { y } = b _ { 0 } + b _ { 1 } x _ { 1 } + b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 } +b4x4+ b _ { 4 } x _ { 4 } .
The regression equation is
Y=1.9568+1.7369X1+1.1099X21.2672X3+1.6080X4\mathrm { Y } = 1.9568 + 1.7369 \mathrm { X } 1 + 1.1099 \mathrm { X } 2 - 1.2672 \mathrm { X } 3 + 1.6080 \mathrm { X } 4
 Predictor  Coef  SE Coef  T  P  Constant 1.95680.82481.12770.345 X1 1.73690.79803.42960.004 X2 1.10990.75003.25290.006 X3 1.26720.75341.87300.076 X4 1.60800.87330.93280.349\begin{array}{lllll}\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\\text { Constant } & 1.9568 & 0.8248 & 1.1277 & 0.345 \\\text { X1 } & 1.7369 & 0.7980 & 3.4296 & 0.004 \\\text { X2 } & 1.1099 & 0.7500 & -3.2529 & 0.006 \\\text { X3 } & -1.2672 & 0.7534 & 1.8730 & 0.076 \\\text { X4 } & 1.6080 & 0.8733 & -0.9328 & 0.349\end{array}
 The following MINITAB output presents a multiple regression equation  \hat { y } = b _ { 0 } + b _ { 1 } x _ { 1 } + b _ { 2 } x _ { 2 } + b _ { 3 } x _ { 3 }   + b _ { 4 } x _ { 4 } . The regression equation is  \mathrm { Y } = 1.9568 + 1.7369 \mathrm { X } 1 + 1.1099 \mathrm { X } 2 - 1.2672 \mathrm { X } 3 + 1.6080 \mathrm { X } 4   \begin{array}{lllll} \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 1.9568 & 0.8248 & 1.1277 & 0.345 \\ \text { X1 } & 1.7369 & 0.7980 & 3.4296 & 0.004 \\ \text { X2 } & 1.1099 & 0.7500 & -3.2529 & 0.006 \\ \text { X3 } & -1.2672 & 0.7534 & 1.8730 & 0.076 \\ \text { X4 } & 1.6080 & 0.8733 & -0.9328 & 0.349 \end{array}      \text { Analysis of Variance }   \begin{array}{lccccc} \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\ \text { Regression } & 4 & 503.9 & 126.0 & 5.0806 & 0.003 \\ \text { Residual Error } & 40 & 990.4 & 24.8 & & \\ \text { Total } & 44 & 1,494.3 & & & \\ \hline \end{array}   Predict the value of  \mathrm { y }  when  x _ { 1 } = 1 , x _ { 2 } = 2 , x _ { 3 } = 3 , x _ { 4 } = 6  A)  9.798 B)  9.8031 C)  10.6228 D)  11.7599

 Analysis of Variance \text { Analysis of Variance }
 Source  DF  SS  MS  F  P  Regression 4503.9126.05.08060.003 Residual Error 40990.424.8 Total 441,494.3\begin{array}{lccccc}\text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\\text { Regression } & 4 & 503.9 & 126.0 & 5.0806 & 0.003 \\\text { Residual Error } & 40 & 990.4 & 24.8 & & \\\text { Total } & 44 & 1,494.3 & & & \\\hline\end{array}

Predict the value of y\mathrm { y } when x1=1,x2=2,x3=3,x4=6x _ { 1 } = 1 , x _ { 2 } = 2 , x _ { 3 } = 3 , x _ { 4 } = 6


Definitions:

Perceptual Sets

Psychological mechanisms that influence how people perceive and interpret sensory information based on their expectations and prior experiences.

Internal Factor

An influence, element, or condition within an organization that affects its operations and outcomes.

Person Perception

The process of forming impressions and making judgments about other people's traits, intentions, and behaviors.

Perceiver Characteristics

Attributes or traits of the individual observing and interpreting others' behaviors, such as biases, attitudes, experiences, and personality, that affect their perceptions.

Related Questions