A consumer analyst collected the following data on the screen sizes of popular L

Question

A consumer analyst collected the following data on the screen sizes of popular LCD televisions sold recently at a large retailer:

Does there appear to be a linear relationship between the screen size and the price? (Round your answer to 2 decimal places.)

Use statistical software to determine the regression equation. (Negative amounts should be indicated by a minus sign. Round your answers to the nearest whole number.)

Interpret the value of the slope in the regression equation.

Include the manufacturer in a multiple linear regression analysis using a "dummy" variable. Does it appear that some manufacturers can command a premium price? Hint: You will need to use a set of indicator variables. (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)

Test each of the individual coefficients to see if they are significant. (Round your answers to 2 decimal places. Leave no cells blank - be certain to enter "0" wherever required. Negative amounts should be indicated by a minus sign.)

A consumer analyst collected the following data on the screen sizes of popular LCD televisions sold recently at a large retailer:

Explanation / Answer

(a) Does there appear to be a linear relationship between the screen size and the price?

Ans:Using Excel function The correlation is=CORREL(xvalues,yvalues)

                                                               =0.8735

The value 0.8735 is fairly close to 1.there is storngly relation linear relationship between varaibles

(b) Which variable is the "dependent" variable?

Ans:The price is the dependent variable

c-1.Use statistical software to determine the regression equation

Ans:Using Excel data analysis

    

The regression equation is Y=-506+66X

                                     Price=-506+66(screen)

c-2.Interpret the value of the slope in the regression equation.

Ans:slope=66

     For each inch increase in screen size, the price increase $66 on average

Regression Statistics Multiple R 0.873545808 R Square 0.763082278 Adjusted R Square 0.751800482 Standard Error 306.769675 Observations 23 ANOVA df SS MS F Significance F Regression 1 6365286.674 6365287 67.63837 5.27179E-08 Residual 21 1976260.304 94107.63 Total 22 8341546.978 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -505.8272348 319.2342397 -1.5845 0.128024 -1169.711177 158.0567073 -1169.711177 158.0567073 X Variable 1 65.80759333 8.001648153 8.224255 5.27E-08 49.16725511 82.44793154 49.16725511 82.44793154

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