Suppose you are climbing a hill whose shape is given by the equation z = 1300 − 0.005x2 − 0.01y2, where x, y, and z are measured
in meters, and you are standing at a point with coordinates (120, 80, 1164). The positive x-axis points east and the positive y-axis points north. (a) If you walk due south, will you start to ascend or descend?
1 answer:
Answer:
ascend
Step-by-step explanation:
There are a couple of ways to figure this. One is to look at the partial derivative ...
The sign of it is negative, so when y decreases, z increases. Going south means decreasing the y-coordinate, so moving in that direction will cause you to ascend.
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Another way to figure this is to evaluate z for some small movement in the southerly direction. In the attached, we find that moving 1 m south to (120, 79, 1165.59) causes an elevation increase of about 1.6 m. Walking south causes you to ascend.
Note that this is consistent with our first result, as -0.02y = -0.02(80) = -1.6. So a change in y of -1 should cause a change in z of about (-1.6)(-1) = 1.6.
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Answer:
a) 0.1829
b) 0.6823
c) 0.0413
Step-by-step explanation:
We are given the following information:
We treat adult having little confidence in the newspaper as a success.
P(Adult have little confidence) = 62% = 0.62
Then the number of adults follows a binomial distribution, where
where n is the total number of observations, x is the number of success, p is the probability of success.
Now, we are given n = 10
a) exactly 5
0.1829 is the probability that exactly 5 out of 10 U.S.adults have very little confidence in newspapers.
b) atleast six
0.6823 is the probability that atleast 6 out of 10 U.S. adults have very little confidence in newspapers.
c) less than four
0.0413 is the probability that less than 4 out of 10 U.S. adults have very little confidence in newspapers.
The solution to this system is (x, y) = (8, -22).
The y-values get closer together by 2 units for each 2-unit increase in x. The difference at x=2 is 6, so we expect the difference in y-values to be zero when we increase x by 6 (from 2 to 8).
You can extend each table after the same pattern.
In table 1, x-values increase by 2 and y-values decrease by 8.
In table 2, x-values increase by 2 and y-values decrease by 6.
The attachment shows the tables extended to x=10. We note that the y-values are the same (-22) for x=8 (as we predicted above). That means the solution is ...
... (x, y) = (8, -22)
Answer:
So on this case for the significance level assumed we see that
so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.
So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.
Step-by-step explanation:
Let's suppose that we have the following linear model:
Where Y is the dependent variable and X the independent variable. represent the intercept and
the slope.
In order to estimate the coefficients we can use least squares procedure.
If we are interested in analyze if we have a significant relationship between the dependent and the independent variable we can use the following system of hypothesis:
Null Hypothesis:
Alternative hypothesis:
Or in other words we want to check is our slope is significant (X have an effect in the Y variable )
In order to conduct this test we are assuming the following conditions:
a) We have linear relationship between Y and X
b) We have the same probability distribution for the variable Y with the same deviation for each value of the independent variable
c) We assume that the Y values are independent and the distribution of Y is normal
The significance level assumed on this case is
The standard error for the slope is given by this formula:
Th degrees of freedom for a linear regression is given by since we need to estimate the value for the slope and the intercept.
In order to test the hypothesis the statistic is given by:
The p value on this case would be given by:
So on this case for the significance level assumed we see that
so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.
So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.