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DiKsa [7]
3 years ago
10

An analysis was done to predict log(price) of homes in Gainesville during Spring 2019 based on number of beds and baths along wi

th an indicator variable for NorthWest. The indicator variable was 1 if in the Northwest of Gainesville and 0 otherwise. This shows the regression output from JMP. The r-square value is 0.625955. The r-square adjusted is 0.618. The number of observations is 100. The F test statistic is 81.1634 with a p-value less than 0.0001. The p-value for the intercept is 0.0002. The p-value for beds_baths is less than 0.0001. The p-value for North West is 0.0490. How would you interpret the slope coefficient for NorthWest?
Mathematics
1 answer:
Alisiya [41]3 years ago
7 0

Answer:

Since coefficient for northwest is - 66.21,

The price of home in north west are 66.21 dollars, lower than elsewhere in the town, for the given number of beds and baths.

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If the sale tax rate is 8.75% in New York State, how much sales tax would you pay for a $35 pair of pants? And at Best Buy they
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The pants are $38.06 and the television is $1,147.50.

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7 0
3 years ago
The distribution of SAT II Math scores is approximately normal with mean 660 and standard deviation 90. The probability that 100
gayaneshka [121]

Using the <em>normal distribution and the central limit theorem</em>, it is found that there is a 0.1335 = 13.35% probability that 100 randomly selected students will have a mean SAT II Math score greater than 670.

<h3>Normal Probability Distribution</h3>

In a normal distribution with mean \mu and standard deviation \sigma, the z-score of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

  • It measures how many standard deviations the measure is from the mean.
  • After finding the z-score, we look at the z-score table and find the p-value associated with this z-score, which is the percentile of X.
  • By the Central Limit Theorem, the sampling distribution of sample means of size n has standard deviation s = \frac{\sigma}{\sqrt{n}}.

In this problem:

  • The mean is of 660, hence \mu = 660.
  • The standard deviation is of 90, hence \sigma = 90.
  • A sample of 100 is taken, hence n = 100, s = \frac{90}{\sqrt{100}} = 9.

The probability that 100 randomly selected students will have a mean SAT II Math score greater than 670 is <u>1 subtracted by the p-value of Z when X = 670</u>, hence:

Z = \frac{X - \mu}{\sigma}

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{670 - 660}{9}

Z = 1.11

Z = 1.11 has a p-value of 0.8665.

1 - 0.8665 = 0.1335.

0.1335 = 13.35% probability that 100 randomly selected students will have a mean SAT II Math score greater than 670.

To learn more about the <em>normal distribution and the central limit theorem</em>, you can take a look at brainly.com/question/24663213

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The correct answer is -3
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