# Taylor has a set of 32 cards. There are 16 yellow cards and 16 brown cards in the set. The first 6 cards that Taylor draws are yellow, brown, yellow, yellow, yellow, and brown. Based on these results, what is the probability that the next card he draws will be yellow?

Question
Taylor has a set of 32 cards. There are 16 yellow cards and 16 brown cards in the set. The first 6 cards that Taylor draws are yellow, brown, yellow, yellow, yellow, and brown. Based on these results, what is the probability that the next card he draws will be yellow?

2020-11-02

Given:
32 cards in total
16 yellow cards
16 brown cards
First 6 drawn cards: yellow, brown, yellow, yellow, brown
Since 6 of the 32 cards were already drawn, there are $$32-6=26$$ cards remaining.
# of possible outcomes $$= 26$$
The set of cards contains 16 yellow cards, while 4 of the yellow cards were already drawn and thus there are $$16-4=12$$ yellow cards remaining that we can select on the next draw:
# of favorable outcomes $$= 12$$
The probability is the number of favorable outcomes divided by the number of possible outcomes
P(Next card is yellow)= # of favorable outcomes # of possible outcomes=$$\displaystyle\frac{{12}}{{26}}=\frac{{6}}{{13}}\sim{0.4615}={46.15}\%$$

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5) Dr. Jung now remembers that it sometimes helps to perform a square root transformation on the dependent variable in a regression problem. Modify your spreadsheet to include a new dependent variable that is the square root on the earring prices (use Excel’s SQRT( ) function). If Dr. Jung wanted to build a linear regression model to estimate the square root of earring prices using the same independent variables as before, which variables would you recommend that he use? Why?
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6) Suppose Dr. Jung decides to use clarity (X2) and carats (X3) as independent variables in a regression model to predict the square root of the earring prices. What is the estimated regression equation? What is the value of the R2 and adjusted-R2 statistics?
7) Use the regression equation identified in the previous question to create estimated prices for each of the earring sets in Dr. Jung’s sample. (Remember, your model estimates the square root of the earring prices. So you must actually square the model’s estimates to convert them to price estimates.) Which sets of earring appears to be overpriced and which appear to be bargains? Based on this analysis, which set of earrings would you suggest that Dr. Jung purchase?
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9) Suppose Dr. Jung decides to use color (X1), carats (X3) and the interaction terms X4 (color * clarity) and X5 (color * carats) as independent variables in a regression model to predict the square root of the earring prices. What is the estimated regression equation? What is the value of the R2 and adjusted-R2 statistics?
10) Use the regression equation identified in the previous question to create estimated prices for each of the earring sets in Dr. Jung’s sample. (Remember, your model estimates the square root of the earring prices. So you must square the model’s estimates to convert them to actual price estimates.) Which sets of earrings appear to be overpriced and which appear to be bargains? Based on this analysis, which set of earrings would you suggest that Dr. Jung purchase?
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