Area in 1,000 mi2 of 13 western states.

122, 164, 71, 98, 84, 147, 114, 111, 98, 85, 104, 71, 77

median: __________

lower quartile: ________

upper quartile: _________

iohanetc
2020-10-28
Answered

Make a box-and-whisker plot for each data set.

Area in 1,000 mi2 of 13 western states.

122, 164, 71, 98, 84, 147, 114, 111, 98, 85, 104, 71, 77

median: __________

lower quartile: ________

upper quartile: _________

Area in 1,000 mi2 of 13 western states.

122, 164, 71, 98, 84, 147, 114, 111, 98, 85, 104, 71, 77

median: __________

lower quartile: ________

upper quartile: _________

You can still ask an expert for help

comentezq

Answered 2020-10-29
Author has **106** answers

To find the median, you must first order the data values from least to greatest:

71, 71, 77, 84, 85, 98, 98, 104, 111, 114, 122, 147, 164

The median is the middle value so the median is 98.

The lower quartile is the median of the first half of the data. The first half of the data does not include the median of the set so the first half of the data is:

71, 71, 77, 84, 85, 98

Since the first half of the data has an even number of values, the median of the first half is the average of the two middle values of 77 and 84. The median is then

The upper quartile is the median of the second half of the data. The second half of the data does not include the median of the set so the second half of the data is:

104, 111, 114, 122, 147, 164

Since the second half of the data has an even number of values, the median of the second half is the average of the two middle values of 114 and 122. The median is then

To make the box-and-whisker plot, plot the minimum of 71, the lower quartile of 80.5, the median of 98, the upper quartile of 118, and the maximum of 164. Draw a box that has sides at the lower and upper quartiles. Draw a vertical segment in the box passing through the median. Then draw segments from the minimum to the lower quartile and from the upper quartile to the maximum:

[Pic]

asked 2021-02-23

Interpreting z-scores: Complete the following statements using your knowledge about z-scores.

a. If the data is weight, the z-score for someone who is overweight would be

-positive

-negative

-zero

b. If the data is IQ test scores, an individual with a negative z-score would have a

-high IQ

-low IQ

-average IQ

c. If the data is time spent watching TV, an individual with a z-score of zero would

-watch very little TV

-watch a lot of TV

-watch the average amount of TV

d. If the data is annual salary in the U.S and the population is all legally employed people in the U.S., the z-scores of people who make minimum wage would be

-positive

-negative

-zero

a. If the data is weight, the z-score for someone who is overweight would be

-positive

-negative

-zero

b. If the data is IQ test scores, an individual with a negative z-score would have a

-high IQ

-low IQ

-average IQ

c. If the data is time spent watching TV, an individual with a z-score of zero would

-watch very little TV

-watch a lot of TV

-watch the average amount of TV

d. If the data is annual salary in the U.S and the population is all legally employed people in the U.S., the z-scores of people who make minimum wage would be

-positive

-negative

-zero

asked 2021-08-16

A furniture store is offering a series discount of 25/10 on a dining room table with a list price of $5690. Find the net cost after the series discount. Use the method of calculating series separately.

answers $1991.50

$5547.75

$3840.75

$3698.50

answers $1991.50

$5547.75

$3840.75

$3698.50

asked 2022-07-05

Show, that if ${f}_{n}\to f$ and ${f}_{n}\to g$ is $\mu $-convergent, then $f=g$ almost everywhere on $X$

Hint

Use the fact, that:

$\{x\in X\phantom{\rule{mediummathspace}{0ex}}:\phantom{\rule{mediummathspace}{0ex}}f(x)\ne g(x)\}=\bigcup _{m=1}^{\mathrm{\infty}}\{x\in X\phantom{\rule{mediummathspace}{0ex}}:\phantom{\rule{mediummathspace}{0ex}}|f(x)-g(x)|\ge \frac{1}{m}\}$

So, I don't know how to use that hint. μ convergent means (correct me if I'm wrong), that

${f}_{n}\to f\text{is}\mu \text{convergent}\phantom{\rule{1em}{0ex}}\iff \phantom{\rule{1em}{0ex}}\mu (\{x\in X\phantom{\rule{mediummathspace}{0ex}}:\phantom{\rule{mediummathspace}{0ex}}{\mathrm{\forall}}_{\epsilon}\phantom{\rule{1em}{0ex}}\underset{n\to \mathrm{\infty}}{lim}|{f}_{n}(x)-f(x)|\epsilon \})=0$

So I don't see it how the hint should be used.

It is not written what kind of measure our $\mu $ is though, usually when there's nothing written we assume it's a Lebesgue measure, but I don't know if that has to be the case here

Hint

Use the fact, that:

$\{x\in X\phantom{\rule{mediummathspace}{0ex}}:\phantom{\rule{mediummathspace}{0ex}}f(x)\ne g(x)\}=\bigcup _{m=1}^{\mathrm{\infty}}\{x\in X\phantom{\rule{mediummathspace}{0ex}}:\phantom{\rule{mediummathspace}{0ex}}|f(x)-g(x)|\ge \frac{1}{m}\}$

So, I don't know how to use that hint. μ convergent means (correct me if I'm wrong), that

${f}_{n}\to f\text{is}\mu \text{convergent}\phantom{\rule{1em}{0ex}}\iff \phantom{\rule{1em}{0ex}}\mu (\{x\in X\phantom{\rule{mediummathspace}{0ex}}:\phantom{\rule{mediummathspace}{0ex}}{\mathrm{\forall}}_{\epsilon}\phantom{\rule{1em}{0ex}}\underset{n\to \mathrm{\infty}}{lim}|{f}_{n}(x)-f(x)|\epsilon \})=0$

So I don't see it how the hint should be used.

It is not written what kind of measure our $\mu $ is though, usually when there's nothing written we assume it's a Lebesgue measure, but I don't know if that has to be the case here

asked 2022-03-22

Listed below are systolic blood pressure measurements (mm Hg) taken from the right and left arms of the same woman. Assume that the paired sample data is a simple random sample and that the differences have a distribution that is approximately normal. Use a 0.10significance level to test for a difference between the measurements from the two arms. What can be concluded?

Right arm: 146 137 140 132 132

Left-arm: 183 172 179 156 149

In this example, μd is the mean value of the differences d for the population of all pairs of data, where each individual difference d is defined as the measurement from the right arm minus the measurement from the left arm. What are the null and alternative hypotheses for the hypothesis test?

Identify the test statistic.

Identify the P-value.

Right arm: 146 137 140 132 132

Left-arm: 183 172 179 156 149

In this example, μd is the mean value of the differences d for the population of all pairs of data, where each individual difference d is defined as the measurement from the right arm minus the measurement from the left arm. What are the null and alternative hypotheses for the hypothesis test?

Identify the test statistic.

Identify the P-value.

asked 2020-11-22

Which procedure(s) decrease(s) the random error of a measure-ment:

(1) taking the average of more measurements,

(2) calibrat-ing the instrument,

(3) taking fewer measurements? Explain

(1) taking the average of more measurements,

(2) calibrat-ing the instrument,

(3) taking fewer measurements? Explain

asked 2022-05-21

Factorising this formula

I came across this simplification in an iTunes U calculus course.

$\frac{\frac{1}{{x}_{0}+\delta x}-\frac{1}{{x}_{0}}}{\delta x}=\frac{1}{\delta x}\left(\frac{{x}_{0}-({x}_{0}+\delta x)}{({x}_{0}+\delta x){x}_{0}}\right)$

I didn't understand how this was done. I can see the denominator being taken out for $\frac{1}{\delta x}$ but do not understand the remainder. Can someone give me some guidance? Thanks

I came across this simplification in an iTunes U calculus course.

$\frac{\frac{1}{{x}_{0}+\delta x}-\frac{1}{{x}_{0}}}{\delta x}=\frac{1}{\delta x}\left(\frac{{x}_{0}-({x}_{0}+\delta x)}{({x}_{0}+\delta x){x}_{0}}\right)$

I didn't understand how this was done. I can see the denominator being taken out for $\frac{1}{\delta x}$ but do not understand the remainder. Can someone give me some guidance? Thanks

asked 2022-02-02

How do you evaluate $x-3y$ if $x=3$ and $y=-2$ ?