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Part I: Law of Supply. In Microeconomics, the “Law of Supply” says that, if all else...

Part I: Law of Supply. In Microeconomics, the “Law of Supply” says that, if all else remains equal, an increase in price will result in an increase in the quantity supplied. Consider the price and demand data presented below:

x = price

y = Quantity Supplied

$3.00

209

$4.00

259

$4.25

542

$4.75

346

$5.00

379

$5.00

360

$6.50

781

$6.75

672

$8.00

873

$8.00

900

$9.50

969

$10.00

927
  1. Use software to find the correlation coefficient between the two variables price and quantity supplied.
  1. Is there a significant correlation between the two variables? Explain why or why not.
  1. Based on that correlation coefficient, describe the relationship between the two variables. Be sure to use two words from the following pairs: positive/negative and weak/strong.
  1. Use software to determine the equation of the regression line between the two variables where price is the explanatory variable (x) and quantity supplied is the response variable (y).
  1. Using that regression equation, predict the quantity supplied for this item if the price was set at $7.00.
  1. Using that regression equation, predict the quantity supplied for this item if the price was set at $15.00.
  1. Do you think the estimates from #5 and #6 are reliable? Explain why or why not.
  1. Based on this data, what percent of the variation in quantity supplied can be explained by the price?

Part I: Law of Supply. In Microeconomics, the “Law of Supply” says that, if all else remains equal, an increase in price will result in an increase in the quantity supplied. Consider the price and demand data presented below:

x = price

y = Quantity Supplied

$3.00

209

$4.00

259

$4.25

542

$4.75

346

$5.00

379

$5.00

360

$6.50

781

$6.75

672

$8.00

873

$8.00

900

$9.50

969

$10.00

927
  1. Use software to find the correlation coefficient between the two variables price and quantity supplied.
  1. Is there a significant correlation between the two variables? Explain why or why not.
  1. Based on that correlation coefficient, describe the relationship between the two variables. Be sure to use two words from the following pairs: positive/negative and weak/strong.
  1. Use software to determine the equation of the regression line between the two variables where price is the explanatory variable (x) and quantity supplied is the response variable (y).
  1. Using that regression equation, predict the quantity supplied for this item if the price was set at $7.00.
  1. Using that regression equation, predict the quantity supplied for this item if the price was set at $15.00.
  1. Do you think the estimates from #5 and #6 are reliable? Explain why or why not.
  1. Based on this data, what percent of the variation in quantity supplied can be explained by the price?

Part I: Law of Supply. In Microeconomics, the “Law of Supply” says that, if all else remains equal, an increase in price will result in an increase in the quantity supplied. Consider the price and demand data presented below:

x = price

y = Quantity Supplied

$3.00

209

$4.00

259

$4.25

542

$4.75

346

$5.00

379

$5.00

360

$6.50

781

$6.75

672

$8.00

873

$8.00

900

$9.50

969

$10.00

927
  1. Use software to find the correlation coefficient between the two variables price and quantity supplied.
  1. Is there a significant correlation between the two variables? Explain why or why not.
  1. Based on that correlation coefficient, describe the relationship between the two variables. Be sure to use two words from the following pairs: positive/negative and weak/strong.
  1. Use software to determine the equation of the regression line between the two variables where price is the explanatory variable (x) and quantity supplied is the response variable (y).
  1. Using that regression equation, predict the quantity supplied for this item if the price was set at $7.00.
  1. Using that regression equation, predict the quantity supplied for this item if the price was set at $15.00.
  1. Do you think the estimates from #5 and #6 are reliable? Explain why or why not.
  1. Based on this data, what percent of the variation in quantity supplied can be explained by the price?
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Answer #1

Correlations QUANTITY PRICESUPPLIED .938 PRICE Pearson Correlation Sig. (2-tailed) 12 12 QUANTITY SUPPLIED Pearson Correlation 938 Sig. (2-tailed) 12 12 t. Correlation is significant at the 0.01 evel (2-tailed)

Correlation is 0.938

Positive and strong

It is significant at 0.01 level of significance.

Model Summary Adiusted R Std. Error of the Estimate Mode R Square 938- 880 868 103.43275 a. Predictors: (Constant), PRICE ANOVA Sum of Squares 782039.572 106983.345 889022.917 df Mean Square Si Regression Residua Tota 1782039.57273.099 10698.334 a. Predictors: (Constant), PRICE b. Dependent Variable: QUANTITYSUPPLIED Coefficients Standardized Unstandardized Coefficients Coefficients (Constant PRICE 134.330 118.113 91.087 13.815 1.475 8.550 938 a. Dependent Variable: QUANTITY SUPPLIED

y= -134.330 +118.113*Price

X= $7.00

Y(Quantity supplied)= -134.330+118.113*7

= -134.330+826.791

= 692.461

When X= $15

Y(Quantity supplied)= -134.330+118.113*15

Y(Quantity supplied)= 1637.365

Yes it is reliable values.

From above table

R squared that is coefficient of determination= 0.880

Therefore 88% of the variation in quantity supplied can be explained by the price.

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