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den301095 [7]
3 years ago
9

Data on average high temperatures​ (in degrees​ Fahrenheit) in July and precipitation​ (in inches) in July for 48 cities is used

to find a regression line and correlation coefficient. PRECIP​ = 2.0481​ + 0.0067 HIGH R​ (correlation coefficient)​ = 0.0358 ​(1) Give the value of the slope of the regression line. ​(2) Identify the predictor variable in this context. ​(3) Identify the response variable in this context. ​(4) Clearly interpret the numerical value of the slope in the context of the​ problem, namely connecting​ "precipitation" and​ "average high​ temperature". ​(5) Predict the amount of precipitation​ (two places past the​ decimal) for a city that has an average high temperature in July of 87.31 degrees Fahrenheit.
Mathematics
1 answer:
wel3 years ago
5 0

Answer:

Explained below.

Step-by-step explanation:

The regression equation to predict amount of precipitation​ (in inches) in July from the average high temperatures​ (in degrees​ Fahrenheit) in July is as follows:

PRECIP​ = 2.0481​ + 0.0067 HIGH

(1)

The value of the slope of the regression line is, 0.0067.

(2)

The predictor variable in this context is the average high temperatures​ (in degrees​ Fahrenheit) in July.

(3)

The response variable in this context is the amount of precipitation​ (in inches) in July.

(4)

The slope of a regression line is average rate of change in the dependent variable with one unit change in the independent variable.

The slope here is 0.0067.

This value implies that the average rate of change in the amount of precipitation​ (in inches) in July increases by 0.0067 inches with every 1°F increase in the average high temperatures​.

(5)

Compute the mount of precipitation for a city that has an average high temperature in July of 87.31°F  as follows:

PRECIP​ = 2.0481​ + 0.0067 HIGH

             = 2.0481​ + 0.0067 × 87.31°F

             = 2.633077

             ≈ 2.63 inches.

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b. X can take any integer that is greater than or equal to 1. \rm X\in \mathbb{Z}^{+}.

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Step-by-step explanation:

<h3>a.</h3>

In this setting, finding an adult in America that will watch the Super Bowl is a success. The question assumes that the chance of success is constant for each trial. The question is interested in the number of trials before the first success. Let X be the number of adults in America that needs to be surveyed until finding the first one who will watch the Super Bowl.

<h3>b.</h3>

It takes at least one trial to find the first success. However, there's rare opportunity that it might take infinitely many trials. Thus, X may take any integer value that is greater than or equal to one. In other words, X can be any positive integer: \rm X\in \mathbb{Z}^{+}.

<h3>c.</h3>

There are two discrete distributions that may model X:

  • The geometric distribution. A geometric random variable measures the number of trials before the first success. This distribution takes only one parameter: the chance of success on each trial.
  • The negative binomial distribution. A negative binomial random variable measures the number of trials before the r-th success. This distribution takes two parameters: the number of successes r and the chance of success on each trial p.

\rm NB(1, p) (note that r=1) is equivalent to \sim Geo(p). However, in this question the distribution of \rm X takes two parameters, which implies that \rm X shall follow the negative binomial distribution rather than the geometric distribution. The probability of success on each trial is 40\% = 0.40.

\rm X\sim NB(1, 0.40).

<h3>d.</h3>

The expected value of a negative binomial random variable is equal to the number of required successes over the chance of success on each trial. In other words,

\displaystyle E(\text{X}) = \frac{r}{p} = \frac{1}{0.40} = 2.5.

<h3>e.</h3>

P(\rm X = 7) = 0.0187.

Some calculators do not come with support for the negative binomial distribution. There's a walkaround for that as long as the calculator supports the binomial distribution. The r-th success occurs on the n-th trial translates to (r-1) successes on the first (n-1) trials, plus another success on the n-th trial. Find the chance of (r-1) successes in the first (n-1) trials and multiply that with the chance of success on the n-th trial.

<h3>f.</h3>

P(\text{X} = 3)+P(\text{X} = 4) = 0.230.

3 0
3 years ago
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