Question No 3.24

(Y) Demand for Bass Drums 3 6 7 5(X) Stone Temple Pilots' TV Appearance 3 4 7 6

A)

(X) (Y) XY X ^2 Y^23 3 9 9 94 6 24 16 367 7 49 49 496 5 30 36 258 10 80 64 1005 7 35 25 49

33 38 227 199 268

Regression

X~ Bar =∑x/n 33/6 5.5Y~ Bar =∑x/n 38/6 6.33333

B = 1.02857(∑x^2-nx~^2)

y~ - bx 0.67619048

Y = a+b(x) 9.85000

Co-Relation

n∑xy-∑x∑y/root[n∑x^2-(∑x)^2][n∑y^2-(∑y)^2 0.8230

82% chances that TV appearances will increases demand for the based drum

~ is represent (bar)

(∑xy -nx~y~)

a =

X is Given 9

r =

10 78 5

82%

82% chances that TV appearances will increases demand for the based drum

0 1 2 3 4 5 6 70

2

4

6

8

10

12

Regreesion B/T TV Appearance and Bass drum Sales

(Y)

Stone Temple Pilots Tv Appearances (X)

Demand for Bass Drums (Y)

0 1 2 3 4 5 6 70

2

4

6

8

10

12

Regreesion B/T TV Appearance and Bass drum Sales

(Y)

Stone Temple Pilots Tv Appearances (X)

Demand for Bass Drums (Y)

Question No 3.30

Brian Buckley has developed the following forecasting model:

Y =36+4.3(x)

A) Forecast Demand for the Aztec when the temperature is 70(F)

Y =36+4.3(x)

Y = 337

B) Forecast Demand for the Aztec when the temperature is 80(F)

Y=36+4.3(x)

Y = 380

B) Forecast Demand for the Aztec when the temperature is 90(F)

Y=36+4.3(x)Y=36+4.3(90)

Y = 423

where y Demand for Aztec air Conditioners

X The outside temperature (F)

Y=36+4.3(70)

Y=36+4.3(80)

Brian Buckley has developed the following forecasting model:

Interpretation

This calculation or forcasting tell us that if the outside temperature inscreasing the demand for AZTEC Air conditioner is also Increasing

0

50

100

150

200

250

300

350

400

450

Y =

Y =

Y =

This calculation or forcasting tell us that if the outside temperature inscreasing the demand for AZTEC Air conditioner is also Increasing

0

50

100

150

200

250

300

350

400

450

Y =

Y =

Y =

Question No 3.34

(Y) Nos. Of coffee sold 760 510 980 250(X) PRICES 2.7 3.5 2 4.2

A)

(X) (Y) XY X ^22.7 760 2052 7.293.5 510 1785 12.252 980 1960 4

4.2 250 1050 17.643.1 320 992 9.61

4.05 480 1944 16.419.55 3300 9783 67.2

Regression

X~ Bar =∑x/n 19.55/6 3.2583333333Y~ Bar =∑x/n 3300/6 550.00000

B = -277.62797(∑x^2-nx~^2)

y~ - bx 1454.60446247

Y = a+b(x) 9.85000

Co-Relation

n∑xy-∑x∑y/root[n∑x^2-(∑x)^2][n∑y^2-(∑y)^2-0.01354 CO-Relation is negative

~ is represent (bar)

(∑xy -nx~y~)

a =

X is Given 1.80

r =

320 4803.1 4.05

Y^257760026010096040062500

102400230400

2193400

0 1 2 3 4 5 6 70

200

400

600

800

1000

1200

Regreesion B/T Prices and Nos Of Coffee Sold

(Y)

Prices (X)

Nos. Of Coffee SOld (Y)

0 1 2 3 4 5 6 70

200

400

600

800

1000

1200

Regreesion B/T Prices and Nos Of Coffee Sold

(Y)

Prices (X)

Nos. Of Coffee SOld (Y)

Question No 3.35

John Howard Alabama Real Estate Developers

Y = a+b(x)a ===== 13473b ===== 37.65

Selling price of house the is 1,860 sqaure feet

Y =13473+37.65(x)Y=36+4.3(1860)

Y = 83502

The house of 1860 Square was sold for 95000$ why this is not as per model?:

Answer :-The house price is $ 83502 but it was sold $95000 as per predicted price model which is not accurateRegression 63% Calculated Manually

X is 1860

The house of 1860 Square was sold for 95000$ why this is not as per model?:

The house price is $ 83502 but it was sold $95000 as per predicted price model which is