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Perform Statistical Inference for Multiple Regression S=10.6075RSq=84.4%RSq(adjj)=83.3%S = 10.6075 \quad \mathrm { R } - \mathrm { Sq } = 84.4 \% \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } \mathrm { j } ) = 83.3 \%

question 6

Short Answer

Perform statistical inference for multiple regression.
-What affects flat panel LCD TV sales? Flat panel LCD TV's are sold through a
Variety of outlets. Sales figures (number of units) for the popular Sony Bravia were
Obtained for last quarter from a sample of 30 different stores. Also collected were data
On the selling price and amount spent on advertising the Sony Bravia (as a percentage of
Total advertising expenditure in the previous quarter) at each store. Output is shown
Below. The correct null and alternative hypotheses for testing the regression coefficient
Of Price is  Dependent variable is  Sales  Predictor  Coef  SE Coef  T  P  Constant 90.1925.083.600.001 Price 0.030550.010053.040.005 Advertising 3.09260.36808.400.000\begin{array} { l r r r r } \text { Dependent variable is } & \text { Sales } & & \\ & & & & \\ \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 90.19 & 25.08 & 3.60 & 0.001 \\ \text { Price } & - 0.03055 & 0.01005 & - 3.04 & 0.005 \\ \text { Advertising } & 3.0926 & 0.3680 & 8.40 & 0.000 \end{array}
S=10.6075RSq=84.4%RSq(adjj)=83.3%S = 10.6075 \quad \mathrm { R } - \mathrm { Sq } = 84.4 \% \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } \mathrm { j } ) = 83.3 \%
A. H0::βP0H _ { 0 : } : \beta _ { \mathrm { P } } \neq 0 vs. HA:βP=0\mathrm { H } _ { \mathrm { A } : } \beta _ { \mathrm { P } } = 0
B. H0:βP0\mathrm { H } _ { 0 : } \beta _ { \mathrm { P } } \geq 0 vs. HA:βP<0\mathrm { H } _ { \mathrm { A } } : \beta _ { \mathrm { P } } < 0
C. H0:βP0\mathrm { H } _ { 0 } : \beta _ { \mathrm { P } } \leq 0 vs. HA:βP>0\mathrm { H } _ { \mathrm { A } } : \beta _ { \mathrm { P } } > 0
D. H0:βP=0\mathrm { H } _ { 0 : } \beta _ { \mathrm { P } } = 0 vs. HA:βP0\mathrm { H } _ { \mathrm { A } : } \beta _ { \mathrm { P } } \neq 0
E. H0\mathrm { H } _ { 0 } : The regression is not significant vs. HA\mathrm { H } _ { \mathrm { A } } : The regression is significant.


Definitions:

Location Difficulties

Challenges associated with the geographical positioning of a business that could impact its success or operations.

Economic Condition

The state of an economy at a given time, including factors like inflation, unemployment, and growth rates.

City

A large human settlement generally characterized by significant population density, economic activities, and a developed infrastructure.

State

Refers to a politically organized body of people under a single government, or a specific condition or mode of being.

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