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# You own a bakery and decide to compare your weekly flour

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This 0 page Study Guide was uploaded by an elite notetaker on Wednesday January 6, 2016. The Study Guide belongs to a course at Zuni Comprehensive Community Health Center taught by a professor in Fall. Since its upload, it has received 45 views.

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Date Created: 01/06/16

1 You own a bakery and decide to compare your weekly flour consumption in pounds X variable and the sales you make in dollars y variable each week You enter your raw data into Excel and run a simple linear regression Below are your summary output results SUMMARY OUTPUT t22a2a222222222222222221 Regression Statistics 1 t t Multiple R 07529 g R Square 05669 Adjusted R j 3 Square 05128 391 3 Standard 3 3 Error 241940 5 Observations 1O t t I 391 ANOVA 1 5 Significance 1 5 df 88 MS F F 3 Regression 1 6129716 6129716 10472 0012 g E Residual 8 4682784 585348 Total 9 10812500 3 II 1 t 1 Standard P Upper Lower COBffCienl s Error t value Lower 95 951 Upper 950 g Stat 95 3 E Intercept 21445 11365 189 010 47654 4763 47654 4763 I t X Variable 1 553 171 324 001 159 948 159 948 g i I 391 39 1 a Using the above data identify the simplelinear regression model equation that could LLLL L ml A A A i Li i A A J ALA A l A L ALLLit1 1151 l liii i i i L L l L LL 1AL i AiAi ili lL AL LALJ quot be used quottoquot prediCt Sales based on flo ur consumption l7 b Identify the percent of variability in sales y that is explained by ys relationship with flour consumption X percent variability 2 Assume you are in the analysis phase of a project Name three statistical tools that you could a l to our data in order to drive our next steps in the DMAIC process Page 1 of 5 3 Describe the following data five different ways include numbers in your answer Data 13 10 15 11 12 12 7 8 15 14 Note The above presentation is called as the venumber summary 4 In a normal distribution of measurements having a mean of 500 feet and a standard deviation of 80 feet what percent of the distribution falls between 300 and 450 feet 5 You have just performed a linear regression analysis on2successive values of a time series and you see autocorrelation What might your r be equal to 6 The null hypothesis is Ho u 10 and the alternative hypothesis is Ha u 7t 10 Assume alpha 001 If the null hypothesis was rejected what would the 99 confidence interval for u look like b 85 121 c 53 155 d 98 105 Range Eharit part dimension Mun Tue quotll39hu Fri Sat Mun TIMIE quotFilm Fri Sat my 7 Given the above range chart what can you conclude aProcess variation is unstable and unpredictable bMeasurement variation is declining 6 dDiscrimination is a problem 8 Describe two ways to determine whether your measurement system is repeatable and reproducible b 9 You are interested in developing a control chart Your dimension of concern is the diameter of a cylindrical part Every hour you take two measurements Choose the most appropriate chart anp chart b IMR chart c c chart 10 You are interested in developing a second control chart However you have collected data on the number of visible scratches on the part You take a small constant subgroup size of 2 every day Choose the most appropriate chart anp chart b IMR chart d xbarR chart 11 A hypothesis is being tested at alpha 005 At which of the following p values would the null hypothesis be rejected a 0150 c 0055 0 0350 Page 3 of 5 12 It was reported in USA Todaythat from 1999 through 2003 the number of daily spam messages sent worldwide was xYear Number Year 1 Spam Messages Sent billions 1 0 1 1999 2 2000 23 3 2001 40 4 2002 56 5 2003 73 The regression equation was determined to be y 073 159 x where yis the number of spam messages sent in billions and Xis the year number Using the model what is the predicted number of spam messages sent in 2004 a 900 billion b 785 billion c 3185 billion 13 If we want to detect a change in the process we increase our sample size a continuous b larger d normal 14 Specific models have been developed to aid in the analysis of time series data when usual regression methods are not appropriate What model uses the average of the last several values of a time series to forecast the next value Name of the model 15 A strong correlation does not mean a causeandeffect relationship Causation is only one explanation of an observed association What else could produce a strong correlation aConfounding factor bCoincidence c Common cause 16 Acorrelation coefficient r 072 would indicate a There is a strong positive correlation between two factors c There is no correlation between four factors d There is a moderate positive correlation between four factors Page 4 of 5 17 When the variability in xdecreases for example outliers are removed from the data the correlation coefficient gets closer to Page 5 of 5 18You enter your data into Excel and run a multiple regression Below are your summary output results What variables are significant and should be included in your model Name the variables weight Regression Statistics Multiple R 094898 R Square 090056 AdjustedR Square 082101 Standard Error 624921 Observations 10 ANOVA Significance df SS MS F F Regression 4 17683366 4420841 113202 00101 Residual 5 1952634 390527 Total 9 19636000 Standard Coefficients Error tStat Pvalue Lower 95 Intercept 483628 141772 34113 00190 119192 weight 213770 97575 21908 00300 464595 height 124787 73110 17068 01486 312723 power 42240 88810 04756 06544 270534 speed 02849 03917 07272 04997 07221 19 Using the above Excel summary output results answer the following questions a How many samples were collected to generate this data I b What is the correlation for this multiple regression and what does it indicate 20 Certain data is inappropriate for a regression analysis such as b There aren t any outliers n c The correlation coefficient is less than 1 d All of the above

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