We are considering a launch of a newtype of raisin into the packaged raisin market. To do so, wecollected product ratings on a 1-10 Likert-scale from consumersutilizing the following attributes and corresponding levels,

Attribute	Level1	level 2	level 3	level 4
Rasin Chewiness	low	medium	high	n/a
Rasin Color	white	grey	brown	black
Packaging Size	small	large	n/a	n/a
Free Gift	no	yes	n/a	n/a
Raisin Aroma	none	medium	heavy	n/a
Price Compared to Market Leader	lower	same	higher	n/a

Please base youranswer to the following questions on this data. Note that eachattribute is coded numerically. For instance, for Chewiness (Low=1, Medium =2, High =3) and similarly for the other attributesreading left to right in the table above.

For each of theattributes, we code the levels into multiple dummy variables toinclude in our regression. The variables we used are asfollows:

Chew1 Chew2 Level 1 1 0 Level 2 0 1 Level 3 0 0 SizeLarge Level 1 0 Level 2 1 Aroma1 Aroma2 Level 1 1 0 Level 2 0 1 Level 3 0 0

Color1 Color2 Color3 Level 1 1 0 0 Level 2 0 1 0 Level 3 0 0 1 Level 4 0 0 0 GiftDummy Level 1 0 Level 2 1 Price1 Price2 Level 1 1 0 Level 2 0 1 Level 3 0 0

Note that for eachcategory, the number of variables is equal to the number of levels– 1.

For example, forchewiness, we only need 2 dummy variables to show 3 levels:

• Chew1 = 1 and Chew2 = 0 indicates level 1 of chewiness.
• Chew1 = 0 and Chew2 = 1 indicates level 2 of chewiness.
• If neither Chew1 or Chew2 are 1 that only leaves level 3 ofchewiness.

Regression Results fromcreating dummy variables

Coefficents	beta	std. error	t-value	p-value
intercept	5.2991	0.3240	16.353	0.0000
chew 1	-0.8659	0.2437	-3.5535	0.0006
chew 2	-0.3461	0.2438	-1.4195	0.1593
color 1	0.1211	0.2871	0.4218	0.6742
color 2	0.2145	0.2802	0.7657	0.4459
color 3	0.4799	0.2696	1.7801	0.0785
Size large	0.8992	0.2000	4.4969	0.0000
gift dummy	0.0916	0.2099	0.4365	0.6635
aroma 1	0.5468	0.2563	2.1334	0.0357
aroma 2	0.9715	0.2327	4.1742	0.0001
Price 1	0.6548	0.2157	3.0362	0.0032
Price 2	0.3237	0.2895	1.1180	0.2666

Residual standard error: 0.9418 on 88 degrees of freedom

Multiple R-Squared: 0.4276

F-statistic: 5.976 on 11 and 88 degrees of freedom, the p-valueis 3.412e-007

1. Write down the model that was estimated in the regression, withthe name of the variables and their coefficients in the model.
2. What Likert rating score would you predict for a raisin productthat has Low Chewiness, Grey Raisins, Large Package Size, FreeGift, Medium Aroma, and the Same Price as the Market Leader? Youmay round your answer to the nearest integer.
3. What product has the highest predicted rating score?
4. Would you necessarily introduce this product, the one fromprevious part, if you were the decision maker?  Why orwhy not?
5. Suppose that the predicted market share of product j isproportional to R_j; that is market share, whereR_jis the predicted Likert rating of product j. Whatwould the predicted market share be if the product described inpart b were introduced into the market consisting ofcurrent products i, ii, iii? The market currently contains thefollowing three products:
   1. High Chewiness, Grey Raisins, Small Package Size, No Free Gift,Medium Aroma, and Same Price as the Market Leader’s (Likert=6.81)
   2. Low Chewiness, Brown Raisins, Small Package Size, Free Gift,Medium Aroma, and Same Price as the Market Leader’s (Likert=6.30)
   3. Medium Chewiness, Black Raisins, Large Package Size, No FreeGift, No Aroma, and Lower Price than the Market Leader’s (Likert=7.05)
6. Do the product attributes (as a whole) provide significantpredictive power for the rating scores? Justify your answer.
7. Which product attributes, if any, have no statisticallysignificant explanatory power for rating scores? State clearly howyou arrived at your answer.