The best way to compare fractions would be to make them have like
denominators. We first , in this case, need to convert from decimal to
fraction.
Converting decimals to fractions first requires an
understanding of the decimal places that fall after the decimal. One
place after the decimal is the tenths place. If you have a decimal that
ends at one place after the decimal (or in the tenths place) it can be
written as the number after the decimal in the top of the fraction and
ten (tenths place) in the denominator. ex. .5 ends one place after
the decimal and can be written as 5/10...(read as five tenths).
If a decimal ends at two places after the decimal...(ex. .75)...it
ends in the hundredths place, can be written as that number in the
numerator and 100 in the denominator....(ex 75/100) and is read as
seventy-five hundredths.
one place after the decimal is tenths (over 10), two places is
hundredths (over 100), three places is thousandths (over 1000) , four
places ten-thousandths (over 10000) and so on.
Because each decimal in your problem has a different amount of
decimal places, it makes for different denominators. But, We can add a
zero to the end of a decimal without changing it's value; if we add a
zero to the end of .5 and make it .50 , we then can write it as 50/100
and would now have like denominators.
if .5 = .50 = 50/100 and .75 = 75/100
we now have the question what fractions can fall between 50/100 and 75/100.
That would be fractions such as 51/100, 52/100, 53/100.......74/100.
Answer:
X = 28
Step-by-step explanation:
Answer:
<h2>E. x = 4</h2>
Step-by-step explanation:
The data below shows the average number of text messages sent daily by a group of people: 7, 8, 4, 7, 5, 2, 5, 4, 5, 7, 4, 8, 2,
enot [183]
It all depends. You've given us an incredibly vague question.
The outlier could be a number that's low or quite high. Also, outliers
shouldn't really contribute towards the value of the mean, median or
range related to a group of data.
They are called outliers because they are bizarre results or numbers
and should be detached from groups of data. Outliers by definition
are abnormalities or anomalies.
I'd say outliers don't really change anything, unless you actually want
to give them credibility or weight.
Large outliers can inflate the value of means, medians and ranges.
Small outliers will invariably deflate the value of means and medians.
<span>The first choice is correct.
Explanation<span>:
The formula for slope is m=(y</span></span>₂<span><span>-y</span></span>₁<span><span>)/(x</span></span>₂<span><span>-x</span></span>₁<span><span>). Once we find the slope using that formula, it goes in place of m in y=mx+b.
We do not yet have the y-intercept, but we can use one of the points to find it. We substitute the x-coordinate of one point in for x and the y-coordinate of the same point in for y; then we solve for b. This gives us the y-intercept.</span></span>
