Economist A.W. Phillips was one of the first economists to present compelling evidence of the correlation between unemployment and wage inflation. Since wages and salaries are a major input cost for companies, rising wages should lead to higher prices for products and services in an economy, ultimately pushing the overall inflation rate higher. As a result, Phillips graphed the relationship between general price inflation and unemployment, rather than wage inflation. Data on unemployment (independent) and inflation (dependent) from the Federal Reserve is on the Excel workbook. Find the equation for the regression line for this data. Find the correlation coefficient and R2. Using the data, is the correlation statistically significant at the 1% level?             Federal Reserve Economic Data   https://fred.stlouisfed.org/graph/?g=3obN           Observation date Unemployment Rate Inflation Rate 1 1960-01-01 5.2 1.2 2 1961-01-01 6.6 1.6 3 1962-01-01 5.8 0.7 4 1963-01-01 5.7 1.3 5 1964-01-01 5.6 1.6 6 1965-01-01 4.9 1.1 7 1966-01-01 4.0 1.9 8 1967-01-01 3.9 3.2 9 1968-01-01 3.7 3.6 10 1969-01-01 3.4 4.7 11 1970-01-01 3.9 6.2 12 1971-01-01 5.9 5.3 13 1972-01-01 5.8 3.3 14 1973-01-01 4.9 3.6 15 1974-01-01 5.1 9.6 16 1975-01-01 8.1 11.8 17 1976-01-01 7.9 6.7 18 1977-01-01 7.5 5.2 19 1978-01-01 6.4 6.8 20 1979-01-01 5.9 9.3 21 1980-01-01 6.3 13.9 22 1981-01-01 7.5 11.8 23 1982-01-01 8.6 8.3 24 1983-01-01 10.4 3.7 25 1984-01-01 8.0 4.3 26 1985-01-01 7.3 3.5 27 1986-01-01 6.7 4.0 28 1987-01-01 6.6 1.4 29 1988-01-01 5.7 4.1 30 1989-01-01 5.4 4.5 31 1990-01-01 5.4 5.2 32 1991-01-01 6.4 5.6 33 1992-01-01 7.3 2.7 34 1993-01-01 7.3 3.3 35 1994-01-01 6.6 2.5 36 1995-01-01 5.6 2.9 37 1996-01-01 5.6 2.8 38 1997-01-01 5.3 3.0 39 1998-01-01 4.6 1.6 40 1999-01-01 4.3 1.7 41 2000-01-01 4.0 2.8 42 2001-01-01 4.2 3.7 43 2002-01-01 5.7 1.2 44 2003-01-01 5.8 2.8 45 2004-01-01 5.7 2.0 46 2005-01-01 5.3 2.8 47 2006-01-01 4.7 4.0 48 2007-01-01 4.6 2.1 49 2008-01-01 5.0 4.3 50 2009-01-01 7.8 -0.1 51 2010-01-01 9.8 2.6 52 2011-01-01 9.1 1.7 53 2012-01-01 8.3 3.0 54 2013-01-01 8.0 1.7 55 2014-01-01 6.6 1.6 56 2015-01-01 5.7 -0.2 57 2016-01-01 4.8 1.2 58 2017-01-01 4.7 2.5 59 2018-01-01 4.0 2.1 60 2019-01-01 4.0 1.5 61 2020-01-01 3.5 2.5 62 2021-01-01 6.3 1.4

The Agri-Food Company has been in business for five years. Brian, the firm's owner, wants to develop a forecast of olive oil sales for the coming year (i.e., 2025) based on the following historical data: Year 2020 2021 2022 2023 2024 Sales 10 20 40 55 75 1. Forecast sales for 2025 using the 3-year moving average method. 2. Forecast sales for 2025 using the simple exponential smoothing method with a smoothing constant of a=0.6. 3. Forecast sales for 2025 using the simple linear trend method.

Consider the following. March April May Month Demand. January 29 February March June April July May June July What is the forecast for July based on a three-month weighted moving average applied to the above past demand data and using the weights: data)? Show all the forecasts for April through June along with the answer Month Demand Forecast January 29 February 46 58 59 46 74 58 59 = 74 88 53 10 10 1:35:14 and (largest weight is for most recent 10

Table 1. The manager of Carpet City outlet store needs to be able to forecast accurately demand for Soft Shang carpet. Demand for the past 20 weeks appears in table 1 below. week Demand Forecast Error ABS. Dev Squared Error APE 1 20           2 28           3 22           4 18           5 22           6 22           7 22           8 26           9 23           10 23           11 23           12 27           13 25           14 22           15 23           16 14           17 14           18 15           19 11           20 16           21             22             23             24…