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mrs_skeptik [129]
2 years ago
5

The sum of the measures of the angles of a triangle is 180 degrees. angels A,B,C has ratios of 3:5:7. What are their measures ?

Mathematics
2 answers:
Igoryamba2 years ago
4 0

Answer:

<em>36:60:84</em>

<em>A     : B    : C</em>

<em>36° : 60° : 84°</em>

<em>A= 36°</em>

<em>B= 60°</em>

<em>C=84°</em>

Step-by-step explanation:

<em>3:5:7 =15</em>

<em>180÷15=12</em>

<em>3×12=36</em>

<em>36:x:y =180</em>

<em>5×12=60</em>

<em>36:60:y =180</em>

<em>7×12=84</em>

<em>36:60:84 =180</em>

hjlf2 years ago
3 0

Answer: A=45  B=75  C=105

Step-by-step explanation:

3:5:7=180 degrees
   15=180
  15 divided by15=180 divided by 15
so,    1=12

so= 3:5:7 equal to (3*12)  (5*12)  (7*12)

A,B,C angles measures respectively = 36:60:84
  if you add them (A,B,C angles) we can obtain = 36:60:84=180    
  (sum of the measures of the angles of a triangle is 180 degrees)

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Researchers are interested if a school breakfast program leads to taller children. Assume that the population of all 5 year-old
DedPeter [7]

Answer:

39-1.96\frac{1}{\sqrt{25}}=38.608    

39+1.96\frac{1}{\sqrt{25}}=39.392    

So on this case the 95% confidence interval would be given by (38.608;39.392)    

Step-by-step explanation:

Previous concepts

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".

The margin of error is the range of values below and above the sample statistic in a confidence interval.

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

\bar X=39 represent the sample mean for the sample  

\mu population mean (variable of interest)

\sigma=1 represent the population standard deviation

n=25 represent the sample size  

Solution to the problem

The confidence interval for the mean is given by the following formula:

\bar X \pm z_{\alpha/2}\frac{\sigma}{\sqrt{n}}   (1)

The margin of error is given by:

ME= z_{\alpha/2}\frac{\sigma}{\sqrt{n}}

Since the Confidence is 0.95 or 95%, the value of \alpha=0.05 and \alpha/2 =0.025, and we can use excel, a calculator or a tabel to find the critical value. The excel command would be: "=-NORM.INV(0.025,0,1)".And we see that z_{\alpha/2}=1.96

And replacing we got:

ME= 1.96 *\frac{1}{\sqrt{25}}= 0.392

Now we have everything in order to replace into formula (1):

39-1.96\frac{1}{\sqrt{25}}=38.608    

39+1.96\frac{1}{\sqrt{25}}=39.392    

So on this case the 95% confidence interval would be given by (38.608;39.392)    

7 0
3 years ago
20 points best answer gets brainliest
lana66690 [7]

<em>Note: The second image should be titled "Data Set #2"</em>

It seems like you've already answered a part of this question yourself, but let's get into the details.

<h3>Parts A and B: 5 Number Summary</h3>

The five values the questions ask you to find the minimum, maximum, median, and the first and third quartiles for both of the data sets. These data are frequently called the <em>five number summary</em> of a data set, and we can use them to create a <em>box plot</em> of our data. The meanings of maximum and minimum are pretty obvious - they're just the biggest and smallest values in the set - but the median and the first and third quartiles all refer to different "middles" in the set.

The <em>median</em> is the "middle value" of a set of ordered data. When we have an odd number of data points, the median is simply the middle number of the set, but when we have an even number, as is the case with these two data sets, we have to find the number halfway between the two middle values. In data set 1, that number is

\dfrac{16+25}{2}=\dfrac{41}{2}=20.5

In data set 2, it's the number halfway between 8 and 10, which is 9.

The median splits any set of data into two parts: all the data points <em>smaller </em>than the median, and all of those <em>larger</em> than the median. In data set one, it's the two subsets {1, 4, 9, 16} and {25, 36, 49, 64}. The median of the smaller set gives us the <em>first quartile</em>, and the median of the larger one gives us the <em>third quartile.</em>

Why "first" and "third" quartile? Where are the second and fourth ones? While they don't go be the titles officially, those values are already part of our five number summary:

  • First quartile
  • Second quartile (the median)
  • Third quartile
  • Fourth quartile (the maximum)
<h3>Part C: Range, spread, and box plots</h3>

To get a visual for how our data is spread out, we can visualize our five-number-summary with a <em>box plot</em>. I've created a box plot for each of the data sets in the first two image uploads. The little nubs on the far ends, sometimes called the "whiskers" of the plot, are the minimum and maximum of the data set; the "box" represents the <em>interquartile range</em> of the data: all the values between the first and third quartile of the data; and the notch going down through the box is the median of the data.

We can see at a glance that data set 1 spans a far greater range of values that data set 2, and that its data points tend to be more concentrated in the lower values. Data set 2, by contrast, is much more uniform; its median lies right in the center of its range, and the "box" is centered similarly along it.

Comparing the medians of two data sets, especially those with the same number of values, can give us valuable information as to how much "larger" or "smaller" one set is than the other, but we need to bring in the other numbers in the five-number summary for a better picture about how that data is spread out.

<h3>Part D: Histograms vs. Box Plots: Which one is better?</h3>

There's no correct answer to this, because each type of graph gives us insight into different aspect of a data set.

A box and whisker plot is great for understanding:

  • The range of a set of data
  • Its spread
  • Its center

While a histogram can reveal:

  • <em>How </em>and <em>where</em> values are concentrated
  • Gaps and outliers

The histogram of data set 1, set to constant intervals of 7 units, shows us that many of the values at the lower end, and get more spread out as we go further - the empty patches become more frequent as we continue to the right, suggesting that our values will become more sparse as they get larger.

Contrast that with data set 2, which has a totally flat, uniform distribution when viewed at a constant interval of 4 units. The box plot and histogram work in tandem to give us a visual, quantitative picture of our data which we can use to make informed conclusions about it.

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