Mastering Data Analysis in Excel Coursera week 4 Quiz

In this blog you will find the correct answer of the Coursera quiz Mastering Data Analysis in Excel Coursera week 4 Quiz mixsaver always try to brings best blogs and best coupon codes

1. A University admissions test has a Gaussian distribution of test scores with a mean of 500 and standard deviation of 100. One student out-performed 97.4% of all test takers.

What was their test score (rounded to the nearest whole number)?

694
502
306
972

2. A carefully machined wire comes off an assembly line within a certain tolerance. Its diameter is 100 microns, and all the wires produced have a uniform distribution of error, between -11 microns and +29 microns.

A testing machine repeatedly draws samples of 180 wires and measures the sample mean. What is the distribution of sample means?

A Uniform Distribution with mean = 109 microns and standard deviation = .8607 microns.
A Uniform Distribution with mean = 109 microns and standard deviation = 11.54 microns.
A Gaussian distribution that, in Phi notation, is written, (109, 133.33).
A Gaussian Distribution that, in Phi notation, is written (109, .7407).

3. A population of people suffering from Tachycardia (occasional rapid heart rate), agrees to test a new medicine that is supposed to lower heart rate. In the population being studied, before taking any medicine the mean heart rate was 120 beats per minute, with standard deviation = 15 beats per minute.

After being given the medicine, a sample of 45 people had an average heart rate of 112 beats per minute. What is the probability that this much variation from the mean could have occurred by chance alone?

.0173%
99.9827%
1.73%
29.690%

4. Two stocks have the following expected annual returns:

Oil stock – expected return = 9% with standard deviation = 13%

IT stock – expected return = 14% with standard deviation = 25%

The Stocks prices have a small negative correlation: R = -.22.

What is the Covariance of the two stocks?

-.0286
-.00715
-.00573
-.00219

5. Two stocks have the following expected annual returns:

Oil stock – expected return = 9% with standard deviation = 13%

IT stock – expected return = 14% with standard deviation = 25%

The Stocks prices have a small negative correlation: R = -.22.

Assume return data for the two stocks is standardized so that each is represented as having mean 0 and standard deviation 1. Oil is plotted against IT on the (x,y) axis.

What is the covariance?

-.22
-.00573
0
-1

6. Two stocks have the following expected annual returns:

Oil stock – expected return = 9% with standard deviation = 13%

IT stock – expected return = 14% with standard deviation = 25%

The Stocks prices have a small negative correlation: R = -.22.

What is the standard deviation of a portfolio consisting of 70% Oil and 30% IT?

12.68%
10.44%
17.93%
11.79%

7. Two stocks have the following expected annual returns:

Oil stock – expected return = 9% with standard deviation = 13%

IT stock – expected return = 14% with standard deviation = 25%

The Stocks prices have a small negative correlation: R = -.22.

Use MS Solver and the Markowitz Portfolio Optimization Spreadsheet to Find the weighted portfolio of the two stocks with lowest volatility.

10.43%
9.5%
10.36%
11.58%

8. You are a data-analyst for a restaurant chain and are asked to forecast first-year revenues from new store locations. You use census tract data to develop a linear model.

Your first model has a standard deviation of model error of $25,000 at a correlation of R = .30. Your boss asks you to keep working on improving the model until the new standard deviation of model error is $15,000 or less.

What positive correlation R would you need to have a model error of $15,000?

R = .428
R = .8200
R = .500
R = .572

9. An automobile parts manufacturer uses a linear regression model to forecast the dollar value of the next years’ orders from current customers as a function of a weighted sum of their past-years’ orders. The model error is assumed Gaussian with standard deviation of $130,000.

If the correlation is R = .33, and the point forecast orders $5.1 million, what is the probability that the customer will order more than $5.3 million?

93.8%
4.3%
6.2%
12.4%

10. An automobile parts manufacturer uses a linear regression model to forecast the dollar value of the next years’ orders from current customers as a function of a weighted sum of that customer’s past-years orders. The linear correlation is R = .33.

After standardizing the x and y data, what portion of the uncertainty about a customer’s order size is eliminated by their historical data combined with the model?

4.2%
3.5%
4.5%
5.2%

11. A restaurant offers different dinner “specials” each weeknight. The mean cash register receipt per table on Wednesdays is $75.25 with standard deviation of $13.50. The restaurant experiments one Wednesday with changing the “special” from blue fish to lobster. The average amount spent by 85 customers is $77.20.

How probable is it that Wednesday receipts are better than average by chance alone?

9.15%
9.05%
90.85%
8.30%

12. Your company currently has no way to predict how long visitors will spend on the Company’s web site. All it known is the average time spent is 55 seconds, with an approximately Gaussian distribution and standard deviation of 9 seconds. It would be possible, after investing some time and money in analytics tools, to gather and analyzing information about visitors and build a linear predictive model with a standard deviation of model error of 4 seconds.

How much would the P.I. G. of that model be?