14.3 You have two data files, one from the year 1996 and the other from the year 1997, as follows: File for 1996 (DATA96) File for 1997 (DATA97) ID Height Weight ID Height Weight 2 68 155 7 72 202 1 63 102 5 78 220 4 61 111 3 66 105 Create a SAS data set from each file (call them YEAR1996 and YEAR1997, respec- tively). Combine the data from each data set into a single file (call it BOTH).
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- Show on excelImport Illinois.xls as a table. Using logical indexing to select the years with more than 2800 cases of Diabetes. Then plot all of the data on the same axis. F1 fx Homicide B C E Year Accidents Cancer Diabetes Heart Disease Homicide| Suicide 1999 4125 25024 3004 33387 1023 1020 3 2000 4041 25365 2995 31844 991 1003 4. 2001 4077 24778 3092 30990 1087 1139 2002 4222 24737 3011 30821 1016 1145 2003 3942 24464 3044 29816 966 1011 7. 2004 4133 24289 3069 28284 871 1028 8. 2005 4182 24250 3034 28226 866 1086 9. 2006 4451 24084 2795 27007 865 1010 10 2007 4367 24115 2851 25813 863 1108 11 2008 4218 24300 2846 26078 872 1198 12 2009 3961 24185 2741 24931 881 1177 13 2010 3997 24070 2507 24959 783 1178 14 2011 4166 24007 2693 24987 812 1226 15 2012 4488 24562 2706 24667 832 1292 16 2013 4511 24491 2798 17 24839 792 1321 2014 4644 24501 2712 18 25024 792 1398 2015 4850 24713 2817 25652 19 863 1363 20 21The table shows the leading causes of death in a certain country in a recent year. The population of the country was 311311 million. If you lived in a typical city of 500,000, how many people would you expect to die of cancercancer each year? Cause Deaths Cause Deaths Heart disease 596 comma 700596,700 Alzheimer's disease 84 comma 90084,900 Cancer 575 comma 300575,300 Diabetes 73 comma 40073,400 Chronic respiratory diseases 143 comma 100143,100 Pneumonia/Influenza 53 comma 40053,400 Stroke 128 comma 600128,600 Kidney disease 45 comma 30045,300 Accidents 122 comma 500122,500 Suicide 38 comma 60038,600 About people would be expected to die of cancer each year. (Type a whole number. Round to the nearest person as needed.)
- Create scatterplot using Excel with the following variables: gender: 0 = male, 1 = female. height: in inches. weight: in pounds. First we will create a scatterplot to examine how weight is related to height, ignoring gender. To do that in Excel: Sort the data by gender: Hold down the Control key (Command key on MacOS) and click the A key to select all of the data in the worksheet. Select the Home tab, then the Editing group Sort & Filter -> Custom Sort. In the pop-up window, make sure that My list has headers box is checked and then choose gender from the pull-down menu next to Sort by. Click OK. Now select all of the data in columns B and C, select the Insert tab and in the Charts group choose Scatter. Choose the first scatterplot option (Scatter with only Markers). Now we have a scatterplot, but the data is all on the right of the plot. To fix this: Right-click on the x-axis, and choose Format Axis from the pop-up menu. Make sure that Axis Options is selected on the left, and…In IBM SPSS, what does clicking on this icon do?Please refer to the picture attached.
- tion 2 of 15 Last summer, the Smith family drove through seven different states and visited various popular landmarks. The prices of gasoline in dollars per gallon varied from state to state and are listed below. $2.34, $2.75, $2.48, $3.58, $2.87, $2.53, $3.31 Click to download the data in your preferred format. CrunchIt! CSV Excel JMP Mac Text Minitab PC Text R SPSS TI Calc Calculate the range of the price of gas. Give your solution to the nearest cent. range: dollars per gallon DELL & 4. 7 8.Identify the mode of the set of data: 87,90,86,95,2,87,88,94,86,97 ?a. 90b. 87c. 86d. 85A sales manager collected the following data on annual sales for new customer accounts and the number of years of experience for a sample of 10 salespersons. Click on the datafile logo to reference the data. DATA file A. B. a. Choose the correct scatter diagram for these data with years of experience as the independent variable. 140 Annual Sales ($1000s) +120 +100 +80 +60 +40 +20 1140' +120 ● +100 +60 +80. +40 -20 Annual Sales ($1000s) ● 8 1 Years of Experience 10 12 14 Salesperson 1 2 3 4 5 6 7 8 9 10 Years of Experience 14 10 12 Years of Experience 1 3 4 4 6 8 10 10 11 13 Annual Sales ($1000s) 80 97 92 102 103 111 119 123 117 136
- The regional transit authority for a major metropolitan area wants to determine whether there is a relationship between the age of a bus and the annual maintenance cost. A sample of ten buses resulted in the following data. Click on the datafile logo to reference the data. DATA file Age of Bus Annual Maintenance (years) Cost ($) 1 350 370 2 480 2 520 2 590 3 550 4 750 4 800 790 5 950I need assistance with parts a, b, and c. Because of high tuition costs at state and private universities, enrollments at community colleges have increased dramatically in recent years. The following data show the enrollment (in thousands) for Jefferson Community College for the nine most recent years. Click on the datafile logo to reference the data. Year Period (t) Enrollment(1,000s) 2001 1 6.5 2002 2 8.1 2003 3 8.4 2004 4 10.2 2005 5 12.5 2006 6 13.3 2007 7 13.7 2008 8 17.2 2009 9 18.1 A. Choose the correct time series plot. What type of pattern exists in the data? Positive trend pattern or negative trend pattern B. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. If required, round your answers to two decimal places. y-intercept, b0 = Slope, b1 = MSE= C. What is the forecast for year 10? Do not round your interim computations and round your final answer to two decimal places.Pyramid Lake, Nevada, is described as the pride of the Paiute Indian Nation. It is a beautiful desert lake famous for very large trout. The elevation of the lake surface (feet above sea level) varies according to the annual flow of the Truckee River from Lake Tahoe. Assume that the U.S. Geological Survey provided the following data: Year 1986 1987 1988 1989 1990 1991 1992 1993 Elevation 3817 3815 3810 3812 3808 3803 3798 3797 Year 1994 1995 1996 1997 1998 1999 2000 Elevation 3795 3797 3802 3807 3811 3816 3817 Make a time series graph.