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dem82 [27]
4 years ago
6

Based on the shape of the graph above, describe one or more functions you can think of to model the data. (Hint: Think of the gr

aph in multiple parts.)

Mathematics
1 answer:
Valentin [98]4 years ago
7 0

Answer:

  A polynomial together with the absolute value function can make a pretty good model (see the attachment)

Step-by-step explanation:

With a sufficient number of specified points, a polynomial can make a pretty good model of almost any smooth function. Here, the function's derivative is undefined at a couple of points, so there are some options for those. If the slopes match on either side of those zeros, then the absolute value function can be used to model the "reflection" at the x-axis. Otherwise, a piecewise description can be used.

The left portion of the curve looks a little like a sine wave, but a cubic or other polynomial can model that wave fairly well. The portion to the right of the maximum looks like a bouncing ball, so can be modeled by a piecewise quadratic function.

_____

<em>Comment on the graph</em>

The attached graph is of a 9th-degree polynomial model. Points were added until the model almost matched the curve. If additional points are defined, a higher-degree model may give a better fit. The "bounce" is modeled by the absolute value function.

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Step-by-step explanation:

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The lengths of pregnancies are normally distributed with a mean of 266 days and a standard deviation of 15 days. a. Find the pro
MrMuchimi

Answer:

a. 0.313% (0.003134842261), b. 237.79 days (237.788095878)

Step-by-step explanation:

In this case, the length of pregnancies is a normally distributed variable, with a mean of 266 days, and a standard deviation of 15 days.  

A graph showing the distribution, with regions of interest for the answer, is presented below.

<h3>First Part: Find the probability of a pregnancy lasting 307 days or longer.</h3>

To answer the question regarding <em>the probability of a pregnancy lasting 307 days or longer</em>, it is necessary to calculate what the cumulative probability distribution value is at 307 days. By the way, according to the graph below, 307 days are quite far from the population mean (266 days).

Using the function <em>normaldist(266,15).cdf(307)</em>, from free Desmos software on Internet, we find that, at this length (307 days), the sum of all probabilities for all cases at this value is 99.69%  (0.996865157739).

Considering that the total area of the curve is 1, then <em>the probability of pregnancy lasting 307 days or longer</em> is 1 - 0.996865157739 or 0.003134842261 (or 0.00313), approximately 0.313%, a very low probabilty.

This probability is showed as the "light blue" region at the right extreme of the graph.

<h3>Second Part: Find the length that separates premature babies from those who are not premature.</h3>

To find the length that separates premature babies from those who are not premature, it is a question about <em>find the days related with the probability of 3% (or 0.03)</em> to find such premature babies. So, it is a question of finding a percentile (or 100-quantiles): given the cumulative normal distribution curve, what is the value (length of pregnancies) that represents this 3%.

Using the function <em>quantile(normaldist(266,15), 0.03)</em>, from free Desmos software on Internet, we obtained a value of 237.79 days (237.788095878) for the length of pregnacies of premature babies. In other words, those babies whose mothers have a length of pregnancy <em>lower</em> than 237.79 days are considered premature, or this is "the length that separates premature babies from those who are not premature".

The area below 237.79 days is the blue shaded region in the graph below, at the left extreme of it.

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