The following data set provides information on the lottery sales, proceeds, and prizes by year in Iowa.
| FYI | Sales | Proceeds | Prizes |
| 1986 | $85,031,584 | $27,631,613 | $39,269,612 |
| 1987 | $98,292,366 | $31,157,797 | $47,255,945 |
| 1988 | $128,948,560 | $40,090,157 | $65,820,798 |
| 1989 | $172,488,594 | $49,183,227 | $92,563,898 |
| 1990 | $168,346,888 | $50,535,644 | $90,818,207 |
| 1991 | $158,081,953 | $44,053,446 | $86,382,329 |
| 1992 | $166,311,122 | $45,678,558 | $92,939,035 |
| 1993 | $207,192,724 | $56,092,638 | $116,820,274 |
| 1994 | $206,941,796 | $56,654,308 | $116,502,450 |
| 1995 | $207,648,303 | $58,159,175 | $112,563,375 |
| 1996 | $190,004,182 | $51,337,907 | $102,820,278 |
| 1997 | $173,655,030 | $43,282,909 | $96,897,120 |
| 1998 | $173,876,206 | $42,947,928 | $96,374,445 |
| 1999 | $184,065,581 | $45,782,809 | $101,981,094 |
| 2000 | $178,205,366 | $44,769,519 | $98,392,253 |
| 2001 | $174,943,317 | $44,250,798 | $96,712,105 |
| 2002 | $181,305,805 | $48,165,186 | $99,996,233 |
| 2003 | $187,829,568 | $47,970,711 | $104,199,159 |
| 2004 | $208,535,200 | $55,791,763 | $114,456,963 |
| 2005 | $210,669,212 | $51,094,109 | $113,455,673 |
| 2006 | $339,519,523 | $80,875,796 | $122,258,603 |
| 2007 | $235,078,910 | $58,150,437 | $133,356,860 |
| 2008 | $249,217,468 | $56,546,118 | $144,669,575 |
| 2009 | $243,337,101 | $60,553,306 | $138,425,341 |
| 2010 | $256,255,637 | $57,907,066 | $150,453,787 |
| 2011 | $271,391,047 | $68,001,753 | $158,961,078 |
| 2012 | $310,851,725 | $78,731,949 | $182,442,447 |
| 2013 | $339,251,420 | $84,890,729 | $200,801,768 |
| 2014 | $314,055,429 | $73,972,114 | $186,948,985 |
| 2015 | $324,767,416 | $74,517,068 | $196,882,289 |
| 2016 | $366,910,923 | $88,024,619 | $221,767,401 |
You decided to find the linear equation that corresponds to sales and year. Create a graph using the sales and year. Add the linear equation to the graph. What is the y-intercept of the linear equation?
Round each value below to the nearest integer.
Provide your answer below: ____E+ ___
In: Statistics and Probability
Please Use R studio and show all the steps to answer this question
NY Marathon 2013 the table below shows the winning times (in minutes) for men and women in the new york city marathon between 1978 and 2013. (the race was not run in 2012 because of superstorm sandy.) assuming that performances in the big apple resemble performances elsewhere, we can think of these data as a sample of performance in marathon competitions. Create a 90% confidence interval for the mean difference in winning times for male and female marathon competitors.
|
Year |
Men |
Women |
Year |
Men |
Women |
|
1978 |
132.2 |
152.5 |
1996 |
129.9 |
148.3 |
|
1979 |
131.7 |
147.6 |
1997 |
128.2 |
148.7 |
|
1980 |
129.7 |
145.7 |
1998 |
128.8 |
145.3 |
|
1981 |
128.2 |
145.5 |
1999 |
129.2 |
145.1 |
|
1982 |
129.5 |
147.2 |
2000 |
130.2 |
145.8 |
|
1983 |
129.0 |
147.0 |
2001 |
127.7 |
144.4 |
|
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 |
134.9 131.6 131.1 131.0 128.3 128.0 132.7 129.5 129.5 130.1 131.4 131.1 |
149.5 148.6 148.1 150.3 148.1 145.5 150.8 147.5 144.7 146.4 147.6 148.1 |
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 |
128.1 130.5 129.5 129.5 130.0 129.1 128.7 129.3 128.3 125.1 Cancelled 128.4 |
145.9 142.5 143.2 144.7 145.1 143.2 143.9 148.9 148.3 143.3 Cancelled 140.1 |
In: Statistics and Probability
Please Use R studio to answer this question
NY Marathon 2013 the table below shows the winning times (in minutes) for men and women in the new york city marathon between 1978 and 2013. (the race was not run in 2012 because of superstorm sandy.) assuming that performances in the big apple resemble performances elsewhere, we can think of these data as a sample of performance in marathon competitions. Create a 90% confidence interval for the mean difference in winning times for male and female marathon competitors.
|
Year |
Men |
Women |
Year |
Men |
Women |
|
1978 |
132.2 |
152.5 |
1996 |
129.9 |
148.3 |
|
1979 |
131.7 |
147.6 |
1997 |
128.2 |
148.7 |
|
1980 |
129.7 |
145.7 |
1998 |
128.8 |
145.3 |
|
1981 |
128.2 |
145.5 |
1999 |
129.2 |
145.1 |
|
1982 |
129.5 |
147.2 |
2000 |
130.2 |
145.8 |
|
1983 |
129.0 |
147.0 |
2001 |
127.7 |
144.4 |
|
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 |
134.9 131.6 131.1 131.0 128.3 128.0 132.7 129.5 129.5 130.1 131.4 131.1 |
149.5 148.6 148.1 150.3 148.1 145.5 150.8 147.5 144.7 146.4 147.6 148.1 |
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 |
128.1 130.5 129.5 129.5 130.0 129.1 128.7 129.3 128.3 125.1 Cancelled 128.4 |
145.9 142.5 143.2 144.7 145.1 143.2 143.9 148.9 148.3 143.3 Cancelled 140.1 |
In: Statistics and Probability
3300 Econometric HW
| obs | RWAGES | PRODUCT |
| 1959 | 59.87100 | 48.02600 |
| 1960 | 61.31800 | 48.86500 |
| 1961 | 63.05400 | 50.56700 |
| 1962 | 65.19200 | 52.88200 |
| 1963 | 66.63300 | 54.95000 |
| 1964 | 68.25700 | 56.80800 |
| 1965 | 69.67600 | 58.81700 |
| 1966 | 72.30000 | 61.20400 |
| 1967 | 74.12100 | 62.54200 |
| 1968 | 76.89500 | 64.67700 |
| 1969 | 78.00800 | 64.99300 |
| 1970 | 79.45200 | 66.28500 |
| 1971 | 80.88600 | 69.01500 |
| 1972 | 83.32800 | 71.24300 |
| 1973 | 85.06200 | 73.41000 |
| 1974 | 83.98800 | 72.25700 |
| 1975 | 84.84300 | 74.79200 |
| 1976 | 87.14800 | 77.14500 |
| 1977 | 88.33500 | 78.45500 |
| 1978 | 89.73600 | 79.32000 |
| 1979 | 89.86300 | 79.30500 |
| 1980 | 89.59200 | 79.15100 |
| 1981 | 89.64500 | 80.77800 |
| 1982 | 90.63700 | 80.14800 |
| 1983 | 90.59100 | 83.00100 |
| 1984 | 90.71200 | 85.21400 |
| 1985 | 91.91000 | 87.13100 |
| 1986 | 94.86900 | 89.67300 |
| 1987 | 95.20700 | 90.13300 |
| 1988 | 96.52700 | 91.50600 |
| 1989 | 95.00500 | 92.40800 |
| 1990 | 96.21900 | 94.38500 |
| 1991 | 97.46500 | 95.90300 |
| 1992 | 100.00000 | 100.00000 |
| 1993 | 99.71200 | 100.38600 |
| 1994 | 99.02400 | 101.34900 |
| 1995 | 98.69000 | 101.49500 |
| 1996 | 99.47800 | 104.49200 |
| 1997 | 100.51200 | 106.47800 |
| 1998 | 105.17300 | 109.47400 |
| 1999 | 108.04400 | 112.82800 |
| 2000 | 111.99200 | 116.11700 |
| 2001 | 113.53600 | 119.08200 |
| 2002 | 115.69400 | 123.94800 |
| 2003 | 117.70900 | 128.70500 |
| 2004 | 118.94900 | 132.39000 |
| 2005 | 119.69200 | 135.02100 |
| 2006 | 120.44700 | 136.40000 |
Problem 2.
Use the data in the “Autocorrelation” tab to test
For Autocorrelation using the Durbin Watson Test
Graph the Residuals and determine whether they are distributed normally or whether they are biased
In: Math
USING MATLAB:
Using the data from table below fit a fourth-order polynomial to the data, but use a label for the year starting at 1 instead of 1872. Plot the data and the fourth-order polynomial estimate you found, with appropriate labels. What values of coefficients did your program find? What is the LMS loss function value for your model on the data?
| Year Built | SalePrice |
| 1885 | 122500 |
| 1890 | 240000 |
| 1900 | 150000 |
| 1910 | 125500 |
| 1912 | 159900 |
| 1915 | 149500 |
| 1920 | 100000 |
| 1921 | 140000 |
| 1922 | 140750 |
| 1923 | 109500 |
| 1925 | 87000 |
| 1928 | 105900 |
| 1929 | 130000 |
| 1930 | 138400 |
| 1936 | 123900 |
| 1938 | 119000 |
| 1939 | 134000 |
| 1940 | 119000 |
| 1940 | 244400 |
| 1942 | 132000 |
| 1945 | 80000 |
| 1948 | 129000 |
| 1950 | 128500 |
| 1951 | 141000 |
| 1957 | 149700 |
| 1958 | 172000 |
| 1959 | 128950 |
| 1960 | 215000 |
| 1961 | 105000 |
| 1962 | 84900 |
| 1963 | 143000 |
| 1964 | 180500 |
| 1966 | 142250 |
| 1967 | 178900 |
| 1968 | 193000 |
| 1970 | 149000 |
| 1971 | 149900 |
| 1972 | 197500 |
| 1974 | 170000 |
| 1975 | 120000 |
| 1976 | 130500 |
| 1977 | 190000 |
| 1978 | 206000 |
| 1980 | 155000 |
| 1985 | 212000 |
| 1988 | 164000 |
| 1990 | 171500 |
| 1992 | 191500 |
| 1993 | 175900 |
| 1994 | 325000 |
| 1995 | 236500 |
| 1996 | 260400 |
| 1997 | 189900 |
| 1998 | 221000 |
| 1999 | 333168 |
| 2000 | 216000 |
| 2001 | 222500 |
| 2002 | 320000 |
| 2003 | 538000 |
| 2004 | 192000 |
| 2005 | 220000 |
| 2006 | 205000 |
| 2007 | 306000 |
| 2008 | 262500 |
| 2009 | 376162 |
| 2010 | 394432 |
In: Computer Science
| Number | Year | Gross Income | Price Index | Adjusted Price Index | Real Income |
| 1 | 1991 | 50,599 | 136.2 | 1.362 | 37150.51 |
| 2 | 1992 | 53,109 | 140.3 | 1.403 | 37853.88 |
| 3 | 1993 | 53,301 | 144.5 | 1.445 | 36886.51 |
| 4 | 1994 | 56,885 | 148.2 | 1.482 | 38383.94 |
| 5 | 1995 | 56,745 | 152.4 | 1.524 | 37234.25 |
| 6 | 1996 | 60,493 | 156.9 | 1.569 | 38555.13 |
| 7 | 1997 | 61,978 | 160.5 | 1.605 | 38615.58 |
| 8 | 1998 | 61,631 | 163.0 | 1.630 | 37810.43 |
| 9 | 1999 | 63,297 | 166.6 | 1.666 | 37993.40 |
| 10 | 2000 | 66,531 | 172.2 | 1.722 | 38635.89 |
| 11 | 2001 | 67,600 | 177.1 | 1.771 | 38170.53 |
| 12 | 2002 | 66,889 | 179.9 | 1.799 | 37181.21 |
| 13 | 2003 | 70,024 | 184.0 | 1.840 | 38056.52 |
| 14 | 2004 | 70,056 | 188.9 | 1.889 | 37086.29 |
| 15 | 2005 | 71,857 | 195.3 | 1.953 | 36793.14 |
The data from Exhibit 3 is also in the Excel file income.xls on the course website. Use Excel, along with this file, to determine Mrs. Bella’s real income for the last fifteen years. Do this by first converting each price index from percent by dividing by 100. Then, divide gross income by your converted (adjusted) price index. Using Excel, find the mean, median, standard deviation, and variance of her past real income. Explain the meaning of these statistics. Can you use mean income to forecast future earnings? Take into account both statistical and non-statistical considerations.
In: Math
What are the key benefits and risks for Petrobras in acquiring Pecom in 2002?
Harvard Case: Drilling South: Petrobras Evaluates Pecom
In: Finance
What is the Homeland Secuirty Act of 2002? How did it come about, and what did it do? Explain in FULL detail.
In: Economics
please I want new answer Do not copy from anyone here
Learning Outcomes:
1. Identify the different elements and issues of organizations development and creating the need for change.
2. Analyze the strategic role of change in the organization and its impact on organizational performance
Overcoming barriers to change: A Corus case study
Overview of the Case:
Corus was formed in 1999 when the former British Steel plc merged with the Dutch company, Hoogovens. Corus is now a subsidiary of the Indian-owned Tata Group. Corus has three operating divisions and employs 40,000 people worldwide. Corus Strip Products UK (CSP UK) is based at Port Talbot and Llanwern, Newport in South Wales. CSP UK makes steel in strip form. This is used in markets such as vehicle manufacture, construction, electrical appliances, tubes and packaging. Corus aims to be a leader in the steel industry by providing better products, higher quality customer service and better value for money than its rivals. In 2005 CSP UK introduced a cultural plan for change called 'The Journey'. The company wanted to address a wide range of business challenges, but the common theme was the fundamental way that people at all levels went about their work. The Journey focused on the values and beliefs of its people. Vitally, this was not limited to employees, but it included contractors, suppliers and other partners. This community of people together redefined eight core values. These provided the guiding principles by which Corus people would work. By early 2007, all employees had been provided with a booklet outlining the CSP Journey values and the behaviours the company expected them to follow. The new values encourage individuals to be accountable for their actions. For example, previously, there had been tragic accidents on site and other health and safety issues, such as poor driving behaviour. This needed to change. The Journey programme has taken a positive approach so that it now steers everything CSP UK does and underpins the culture of the organization.
Questions:
1. Analyze and discuss the five key elements of successful change management.
2. Explore the processes of change associated with each element.
In: Economics
The brightness of sunlight at the earth's surface changes over time depending on whether the earth's atmosphere is more or less clear. Sunlight dimmed between 1960 and 1990. After 1990, air pollution dropped in industrial countries. Did sunlight brighten? Here are data and scatterplot from Boulder, Colorado, averaging over only clear days each year. (Other locations show similar trends.) The response variable is solar radiation in watts per square meter. Year 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Sun 244 246.8 248.3 250.4 250.5 250.5 250.5 248.8 251.3 251.7 250.8 Has sunlight brightened overall? No Yes What is the best word to describe the type of relationship? Curved Linear What is the correlation between the year and sun brightness (round your answer to at least two decimal places.)
In: Statistics and Probability