A scientist measured the amounts of fertilizer given to plants, the heights to which the plants grew, and the amount of fruit th
e plants produced. 2 2-column tables with 5 rows. For table 1, column 1 is labeled Fertilizer (ounces) with entries 175, 192, 130, 128, 184. Column 2 is labeled Height of tree (inches) with entries 50, 52, 36, 35, 50. For table 2, column 1 is labeled Fertilizer (ounces) with entries 175, 192, 130, 128, 184. Column 2 is labeled amount of fruit produced (pounds) with entries 112, 115, 87, 85, 112. The scientist graphed the two sets of data and found that a positive correlation exists in each set. Which statement explains whether there is a relationship between the height of the tree and the amount of fruit produced. Although fertilizer is in both data sets and is positively correlated, only a weak correlation can exist between tree height and fruit yield. Although fertilizer is in both data sets and is positively correlated, it is impossible for any correlation to exist between tree height and fruit yield. Since fertilizer is in both data sets and is positively correlated to both tree height and fruit yield, it is likely that tree height and fruit yield are negatively correlated. Since fertilizer is in both data sets and is positively correlated to both tree height and fruit yield, it is like that tree height and fruit yield are also positively correlated.
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Answer:
Step-by-step explanation:
given that we are interested in finding out the proportion of adults in the United State who cannot cover a $400 unexpected expense without borrowing money or going into debt.
Sample size = 765
Favour = 322
a) The population is the adults in the United State who cannot cover a $400 unexpected expense without borrowing money or going into debt
b) The parameter being estimated is the population proportion P of adults in the United State who cannot cover a $400 unexpected expense without borrowing money or going into debt.
c) point estimate for proportion = sample proporiton =
d) We can use test statistic here as for proportions we have population std deviation known.
d) Std error = 0.01785(
Test statistic Z = p difference / std error
f) when estimated p is 0.50 we get Z = -4.43
g) Is true population value was 40% then
Z = 1.17 (because proportion difference changes here)