A corporate analyst is testing whether mean inventory turnover has increased. Inventory turnover in six randomly chosen product distribution centers(PDCs) is shown.PDC 1PDC 2PDC 3PDC 4PDC 5PDC 6The right-tailed critical value at a = .005 isThis Year Last Year5.14.13.92.94.82.83.43.44.62.67.74.7Multiple ChoiceO1.479.4.032.1.645.

The question is about hypothesis testing.Given :No. of PDC's ( n ) = 6To find :1 ) Critical value2 )…

Answered: A corporate analyst is testing whether…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Product failure behavior. An article in Hotwire (Dec. 2002) discussed the length of time till failure of a product pro- duced at Hewlett-Packard. At the end of the product’s life- time, the time till failure is modeled using an exponential distribution with mean 500 thousand hours. In reliability jargon, this is known as the “wear-out” distribution for the product. During its normal (useful) life, assume the prod- uct’s time till failure is uniformly distributed over the range 100 thousand to 1 million hours. a. At the end of the product’s lifetime, find the probability that the product fails before 700 thousand hours. b. During its normal (useful) life, find the probability that the product fails before 700 thousand hours. c. Show that the probability of the product failing before 830 thousand hours is approximately the same for both the normal (useful) life distribution and the wear-out distribution
Cyclic Consumer 12.9 0.9 Full data set Industrial -29.3 47.2 5.9 49.5 -22 18.1 23.6 15.9 3.8 -1.3 -2.7 33.5 8.3 -8.9 9.1 -9.3 13.9 7.3 22.7 9.6 45.4 -6.5 - 18.8 -5.6 41.6 -3.5 0.6 29.3 15.7 -9.7 4.8 4.9 -5.9 32.9 27.2 4.5 11.6 11.3 4.3 34.8 16.7 - 22.3 2.9 - 27.8 43.2 7.1 - 12.9 13.6 39.5 -27.1 3.3 7.7 32.8 6.7 36.6 24.8 11.3 -0.9 222 -23 29.5 - 16.8 -3.9 16.3 66 26.5 11.4 0.1 20.5 72.8 26.8 27.9 222 2.6 -8.2 -9.5 10.2 - 15.2 7.3 -4.7 6.3 49.7 - 26.7 9.6 20.7 -4.5 16.1 3.7 21.9 23.1 16.7 8.9 -0.4 4.5 27.9 20.9 24 18.4 5.6 21.4 8.9 9.2 -6.7 4.8 - 3.3 -6.4 27.1 -9.3 36.2 -1.7 -0.4 44.7 15.2 51.2 -8.7 31.1 5.9 0.7 19.2 15.6 1.9 94 6.8 9.5 26.7 -7.2 -8.3 -6.3 73.4 5.9 - 16.2 18.7 87.5 - 10.4 -9.5 21.4 -2.9 -5.2 2.1 21.2 12.8 11.7 22.1 23.3 1.2 9.8 - 19.9 9.3 30.5 6.8 55.7 - 11.7 16.9 13.6 -3.6 3.2 2.8 -3.1 5.6 4.4 79.5 16.4 38.2 9.1 - 26.6 -9.8 8.9 6.4 26.4 67.6 -41.8 44.3 - 17.4 -2.8 5.1 14.3 -55 -6.9 11.7 -8.6 14.3 13.8 17.1 - 8.2 -0.9 -9.6 9.2 129.3 -3.3 27.1 28.7 - 39.8 36 4.4 28.3…
Based on the data of air pollution in Los Angeles in 2019, the Environmental Protection Agency (EPA) decided to publish a new stricter regulation effective in January 2020 to reduce hydrocarbon pollution. EPA wants to evaluate whether the new regulation makes significant differences in reducing pollution. EPA collects data of monthly hydrocarbon pollution level as follows: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2019 7.3 6.0 5.4 5.9 3.9 5.7 6.9 7.6 6.3 5.8 5.1 5.9 2020 5.3 6.1 5.6 5.7 3.7 4.7 6.1 7.2 6.4 5.7 4.9 5.8
Period People (Y) Ocupation percentage (X) 2009 1T 46,977,904 59.7 2009 2T 47,453,163 59.3 2009 3T 48,738,589 60.2 2009 4T 48,903,792 60.4 2010 1T 48,069,274 59.9 2010 2T 49,133,132 60.2 2010 3T 49,190,032 58.7 2010 4T 48,478,718 58.8 2011 1T 48,505,168 59.3 2011 2T 49,482,112 59.5 2011 3T 50,127,032 59.7 2011 4T 50,772,496 60.5 2012 1T 50,192,842 60.3 2012 2T 51,477,178 60.7 2012 3T 51,927,050 60.7 2012 4T 51,317,999 60 2013 1T 50,847,242 60 2013 2T 51,895,865 60.4 2013 3T 52,034,353 60.2 2013 4T 52,370,886 60.7 2014 1T 51,559,018 60.3 2014 2T 51,836,752 59.8 2014 3T 52,192,043 59.6 2014 4T 52,108,400 59.4 2015 1T 52,007,842 59.7 2015 2T 52,623,721 59.6 2015 3T 53,179,919 59.7 2015 4T 53,809,017 60.3 2016 1T 52,918,649 59.7 2016 2T 53,539,565 59.6 2016 3T 54,226,803 59.9 2016 4T 54,034,800 59.6 2017 1T 53,681,720 59.7 2017 2T 54,068,791 59.3 2017 3T 54,369,915 59.1 2017 4T 54,696,638 59.3 2018 1T 54,590,773…
Peanut Jar Data: 11.94 11.74 12.25 11.32 11.85 11.86 11.59 12.15 11.76 11.55 12.31 12.59
The economic dynamism, which is the index of productive growth (in dollars) for countries that are designated by the World Bank as middle-income are in following table ("SOCR data 2008," 2013): Table: Economic Dynamism ($) of Middle Income Countries 25.8057 37.4511 51.915 43.6952 47.8506 43.7178 58.0767 41.1648 38.0793 37.7251 39.6553 42.0265 48.6159 43.8555 49.1361 61.9281 41.9543 44.9346 46.0521 48.3652 43.6252 50.9866 59.1724 39.6282 33.6074 21.6643 Compute a 95% confidence interval for the mean economic dynamism of middle-income countries by answering the following questions (iv) Determine t score corresponding to desired confidence level Enter value in decimal form rounded to nearest hundredth. Examples of correctly entered answers: 2.01 0.37 0.60 0.00 (v) Determine error bound of the mean: Enter value in decimal form rounded to nearest ten-thousandth. Examples of correctly entered answers: 0.0000 0.0003 3.0060 2.0700…
Scenario: Two managers ranked 10 franchises by profitability potential. The sum of squared differences in their rankings is 44. Check to see if their rankings are correlated by calculating the Spearman Coefficient of Rank Correlation. (Enter numerical answers to three decimal places with zero before the decimal. For yes or no answers, enter Y for yes or N for no.) a. Calculated rs _____ b. At alpha =1% and a one-tail test, what is the critical value of rs ______ c. Does this result indicate that the rankings by the managers are positively correlated? _____ (Y or N)
Approximate the measures of center for following GFDT. Data Frequency 40 - 44 13 45 - 49 18 50 - 54 12 55 - 59 60 - 64 2 Report mode and median accurate to one decimal place. Report the mean accurate to two decimal places (or enter as a fraction). (a) mode = %3D (b) median = (c) mean
4
1. The data in Table 7–6 were collected in a clinical trial to evaluate a new compound designed to improve wound healing in trauma patients. The new compound was com- pared against a placebo. After treatment for 5 days with the new compound or placebo, the extent of wound healing was measured. Is there a difference in the extent of wound healing between the treatments? (Hint: Are treatment and the percent wound healing independent?) Run the appropriate test at a 5% level of significance. Use the data in Problem 1. Pool the data across the treatments into one sample of size n = 250. Use the pooled data to test whether the distribution of the percent wound healing is approximately normal. Specifically, use the following distribution 30%, 40%, 20%, and 10% and a = 0.05 to run the appropriate test. Percent Wound Healing Treatment 0-25% 26-50% 51-75% 76-100% New compound (n=125) 15 37 32 41 Placebo (n=125) 36 45 34 10
3.Which model do you think is the “best” reduced model? Discuss why you choose this model. Analysis of Variance Table (Step back model) Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 17.1136 0.0003079 *** sqft 1 35364 35364 28.8196 1.134e-05 *** sd 1 5961 5961 4.8576 0.0362339 * unts 1 8678 8678 7.0722 0.0130049 * gar 1 33364 33364 27.1899 1.713e-05 *** cp 1 7641 7641 6.2269 0.0189934 * Residuals 27 33131 1227 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step forward model: Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 16.6255 0.000406 *** sqft 1 35364 35364 27.9976 1.757e-05 *** sd 1 5961 5961 4.7191 0.039517 * unts 1 8678 8678 6.8705 0.014697 * gar 1 33364 33364 26.4144 2.599e-05 *** cp 1 7641 7641 6.0493 0.021176 *…
Consider the rate of return of stocks ABC and XYZ. Year 1 2 SAWN T 3 4 5 FABC 24% 10 17 4 1 ABC XYZ a. Calculate the arithmetic average return on these stocks over the sample period. (Round your answers to 2 decimal places.) rxyz 34% 12 18 1 -9 Arithmetic Average ABC XYZ % % b. Which stock has greater dispersion around the mean return?

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