Given the following information:Month 1 2 3 4 5Sales 25 20 40 35 30(a) Using the two-period weighted moving average with weights assigned to more recentin order 4 and 1,(I) the sales forecast for Month 6 is(ii) the forecasting error for Month 3 is(iii) the mean absolute deviation is(b) Using an exponential smoothing technique with alpha = 0.8(i) the sales forecast for Month 6 is(ii) The forecast error for Month 5 is(iii) The mean absolute deviation is

a) i) Monthly sales data:Month12345Sales2520403530Step 1 - Calculating Weighted Moving Average for…

Answered: Given the following information: Month…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 5SE: What does the y -intercept on the graph of a logistic equation correspond to for a population...

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What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Find the mean hourly cost when the cell phone described above is used for 240 minutes.
12) The following results are an autoregression for US Exports to Mexico where the dependent variable is the lagged value of US Exports. a) Fill in the table b)Based on these regression results, what is your forecast of US Exports to Mexico for March 2005? c) Which of the two forecasts do you think is more accurate? Explain.
Assuming the lowest decile of men receive an average of $800/month in Social Security, while the highest decile receive $2200/month in Social Security how much does each receive (on average) during their years in retirement. Use the chart called: Predictive Years of Receiving Social Security Benefits. Also use the born in 1940 data.
45. Table 1.2 shows the mean annual compensation of construction workers. bab Abor Cesaione TABLE 1.2 Construction Workers' Average Annual Compensation Annual Total Compensation (dollars) Year 1999 42,598 2000 44,764 2001 47,822 2002 48,966 Source: U.S. Bureau of the Census, Statistical Abstract of the United States, 2004-2005. (a) Find the linear regression equation for the data. (b) Find the slope of the regression line. What does the slope represent? (c) Superimpose the graph of the linear regression equation on a scatter plot of the data. (d) Use the regression equation to predict the construction workers' average annual compensation in the year 2008. Tabl
The following table contains yearly data on the price of beef from 2022 to 2024: Year Price of Beef ($/Kg) 3 5 8 2022 2023 2024 Forecast the price of chicken for 2025 using the following methods: 1. A 3-Year moving average method; 2. A simple exponential smoothing method (use a=0.5); 8. A simple linear trend model.
Consider the following image of the data. Is the linear trend line a reasonable fit to the data points? Is it linear? +H R²=0.9467 No. The dashed line is well outside many of the error bars. No. The R^2 value is less than 1. Yes. The R^2 value is approximately 1. Yes. The data points have error bars and the line goes through them.
Amex PLC has gathered following information on the sales of face mask from April 2020 to September 2020. You are required to; Using linear trend equation forecast the sales of face masks for October 2020. Calculate the forecasted sales difference if you use 3-period weighted moving average designed with the following weights: July 0.2, August 0.3 and September 0.5. Month Sales ($) April 17,000 May 18,000 June 19,500 July 22,000 August 21,000 September 23,000
Table 1: Bivariate Correlations for the following variables: income, number of siblings, education, age, white, black and gender (1)rincome 1.00 (2)sibs (3)edu (4)age (5)white (6)black (1) (8)male Mean -0.06 0.30 0.16 0.12 (7)female -0.21 -0.12 0.21 13.91 Standard Dev. 5.63 (2) 1.00 -0.21 0.09 -0.27 0.27 0.02 -0.02 3.505 2.927 (3) 1.00 0.05 0.12 -0.12 0.02 -0.02 13.39 2.93 (4) 1.00 0.12 -0.12 0.03 47.12 (5) -0.03 -0.07 17.50 1.00 -1.00 0.07 .84 .37 (6) 1.00 .07 -.07 .16 .37 (7) 1.00 -1.00 i. There is a negative correlation between family size and income. ii. Income is positively correlated with education. iii. Income is positively correlated with age. iv. Income is positively correlated with white race. v. The black race is negatively correlated with income. vi. Income is negatively correlated with females. vii. Income is positively correlated with males. .56 .50 (8) 1.00 .44 .50 Note: rincome is respondent's income, sibs is respondent's number of siblings, age represents respondent's…
(15) 8. For the data below: a) Compute a three-month moving average for all appropriate months (Hint: some months cannot be computed). Actual 3-Month Automobile Tire Sales Moving Average Absolute Month Deviation Tire Sales January February 80.00 74.00 March 60.00 April May 50.00 52.00 June 58.00
The November 24, 2001, issue of The Economist published economic data for 15 industrialized nations. Included were the percent changes in gross domestic product (GDP), industrial production (IP), consumer prices (CP), and producer prices (PP) from Fall 2000 to Fall 2001, and the unemployment rate in Fall 2001 (UNEMP). An economist wants to construct a model to predict GDP from the other variables. A fit of the model GDP = , + P,IP + 0,UNEMP + f,CP + P,PP + € yields the following output: The regression equation is GDP = 1.19 + 0.17 IP + 0.18 UNEMP + 0.18 CP – 0.18 PP Predictor Coef SE Coef тР Constant 1.18957 0.42180 2.82 0.018 IP 0.17326 0.041962 4.13 0.002 UNEMP 0.17918 0.045895 3.90 0.003 CP 0.17591 0.11365 1.55 0.153 PP -0.18393 0.068808 -2.67 0.023 Predict the percent change in GDP for a country with IP = 0.5, UNEMP = 5.7, CP = 3.0, and PP = 4.1. a. b. If two countries differ in unemployment rate by 1%, by how much would you predict their percent changes in GDP to differ, other…

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