Question

A certain manufacturing firm produces a product that is packaged under two brand names, for marketing purposes. These two bra

(2 marks) Find a basic estimate (without auxiliary information) of the total potential sales. Estimate the variance of your e

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Answer #1

Answer:

Given data

A certain manufacturing firm produces a product that is packaged under 2 brand

The sample for brand 1 was taken from a list of 120 customers for who the total sales in the same quater of last year was 24 units

the brand 2 sample come from 180customers with a total quarterly sales last year of 21200 units

x i y i (y - \gamma X)2  
204 210 16.33332
143 140 99.22431
82 75 121.7992
256 280 129.9364
275 300 131.4171
198 190 314.9246

total =

1158

total =

1215

total =

813.6349

Now let T s  = total sales

= 24,500

\bar{y}    = \frac{1}{n}\sum_{i=1}^{n}y_{i}

=   (1215)

= 202.5

\bar{x} = \frac{1}{n}\sum_{i=1}^{n}x_{i}

= (1158

= 193

Let ratio etimator \gamma = \frac{\bar{y}}{\bar{x}}

= \frac{\bar{202.5}}{\bar{193}}

= 1.049223

This total ratio estimate

barT,=   \frac{\bar{y}}{\bar{x}} Tx

= \gamma Tx

= 1.049223 * 24500

= 25705.96

Let S_{r}^{2} =  – ፡- 7( EC - 6)

=   (813.6349)

= 162.727

Variance (\bar{T_{r}}) = \bar{N} (n - n)\frac{S_{r}^{2}}{n}

=  \frac{T_{r}}{\pi }(\frac{T_{r}}{\pi }-1)\frac{S_{r}^{2}}{n}

=   162.727 | 24500、24500 ( 109 八 103 - ||

= 433581200.7

Let SD(Tý) = F433581200.7

= 20822.612

Brand 2

xi yi (Y - \gamma x)2
137 150 21.122392
189 200 0.352792
119 125 1.6897447
63 60 47.12348
103 110 0.464574
107 100 183.9761
159 180 126.4804
63 75 66.18385
87 90 5.461118
Total = 1027 1090 452.8544

Let Tr = Total sales = 21200

  16012 = 3 1

= 121.111

i = 1027

= 114.111

Let estimator \gamma = \frac{\bar{y}}{\bar{x}}

  121.11 114.111

7= 1.061344

\bar{T}_{r}= \frac{\bar{y}}{x}T_{r}

= 71, = 1.061344 x 21200

= 22500.49

where

s - Σκι - γX)

= (452.8544)

= 50.32

Let Var (T) = ÑÑ - n). Se

一日

  50.32 21200721200 114.111114.111

= 191938.53

where

SD(T) = V191938.53

= 438.11

By solving both Brand (1) & Brand (2)

SD Brand 2 < SD Brand 1

Therefore Brand 2 data is the best data.

  

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