business forecasting a) The second naive model with P=0.9 produces the best fit because it has alower RMSE.b) The second naive model with P=0.9 produces the best fit because it has ahigher RMSE.c) The second naive model with P=0.9 is less biased because it has a lowerRMSE.d) The simple naive model produces the best fit because it has a lower RMSE.e) The simple naive model produces the best fit because it has a higher RMSE.f) The simple naive model is less biased because it has a lower RMSE.AKTUEOtsopeWindow Use the following table and MS Excel to produce two forecasts for January 2020, thesimple naive model and the second naive model with P=0.9. Use the predictedvalues (the forecasted values) from both forecasts for 2017 through 2019 tocalculate the RMSE for both models. Which of the following most accuratelysummarizes the results?YearX2015 122972016127702017133402018 139932019145452020

Given the data as Year X 2015 12297 2016 12770 2017 13340 2018 13993 2019 14545 2020…

Answered: Use the following table and MS Excel to…

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

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If a forecaster wanted to choose a forecasting model that avoided having a large forecasting error in any given period, which error metric should they use when evaluating their forecasting options? CFE O AFE MAD MAPE O MSE
Which forecast method requires that you know the period of the data? Which forecast method should be used when the data has no overall upwards or downwards movement and no repeating pattern? Which forecast method should be used when the data moves down to the right and has no repeating pattern? Which forecast method should be used when the data has a repeating pattern and an upward trend? Which forecast method should be used when the data has a repeating pattern with no overall upwards or downwards movement?
Explain whether each scenario below is a regression, classification, or unsupervised learn- ing problem. If it is a supervised learning scenario, indicate whether we are more interested in inference or prediction. Finally, provide in each case the number of observations, n, and the number of predictors, p. (1) An online retailer must decide whether to display advertisement A or advertisement B to each customer on the basis of collected customer demographics (age, income, zip code, and gender). A set of 150 of its customers have already expressed a preference for one advertisement or the other. (2) A policy analyst is interested in discovering factors that are associated with the crime rate across different U.S. cities. For each of 500 cities, the policy analyst gathers the following data: the crime rate, unemployment rate, population, median income, median home price, and state. (3) The the channel owner to see where the subscribers are located, their age and gender, the times and days…
The busiest time of the day at the Taco Town fast-food restaurant is between 11:00 A.M. and 2:00 P.M. Taco Town’s service is very labor dependent, and a critical factor for providing quick service is the number of employees on hand during this 3-hour period. To determine the number of employees it needs during each hour of the 3-hour lunch period, Taco Town requires an accurate forecasting model. Following are the number of customers served at Taco Town during each hour of the lunch period for the past 20 weekdays: Develop a forecast model that you believe will best forecast Taco Town’s customer demand for the next day and explain why you selected this model.
Compare the two models based on the forecast errors. Which is the appropriate model to use? Why? Seasonally adjusted linear trend Time, Linear Trend Date Quarter Sales lel e le| e e F. 31/03/2016 1. 1 184 222.25 30/06/2016 2 221 274.25 30/09/2016 3 3 241 282.64 31/12/2016 4 4 541 456.64 31/03/2017 1 5 219 207.81 30/06/2017 2 6 297 259.81 30/09/2017 3 7 330 268.2 31/12/2017 4 335 442.2 31/03/2018 9 202 193.37 30/06/2018 10 283 245.37 30/09/2018 3 11 286 253.76 31/12/2018 4 12 377 427.76 31/03/2019 1 13 243 178.93 30/06/2019 2 14 292 230.93 30/09/2019 3 15 131 239.32 31/12/2019 4 16 528 413.32
Top of Form a)Plot the monthly sales data. b)Forecast January sales using each of the following: i)Naive method. ii)A 3-month moving average. iii)A 6-month weighted average using .1, .1, .1, .2, .2, and .3, with the heaviest weights applied to the most recent months. iv)Exponential smoothing using an α = .3 and a September forecast of 18.
Refer to the gasoline sales time series data in the given table. Compute four-week and five-week moving averages for the time series. Round your answers to two decimal places. Compute the MSE for the four-week and five-week moving average forecasts. Round your intermediate calculations and final answers to two decimal places. What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? Recall that MSE for the three-week moving average is 12.89.
State at least one type of conclusion that could be made from the data provided for the KPI. Suggest one other data source that would provide more information to make data-driven decisions. Explain what information that source would supply. (The blue one is mine) or pretty much what can be said of the clustered bar chart
Would someone familiar with SPSS be able to help with sub-question d please?
B. What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is____°F. C. What is wrong with this predicted value? The first variable should have been the dependent variable. It is unrealistically high. The value 3000 is far outside of the range of observed values. It is only an approximation. An unrounded value would be considered accurate. Nothing is wrong with this value. It can be treated as an accurate prediction.
B. What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is____°F. C. What is wrong with this predicted value? The first variable should have been the dependent variable. It is unrealistically high. The value 3000 is far outside of the range of observed values. It is only an approximation. An unrounded value would be considered accurate. Nothing is wrong with this value. It can be treated as an accurate prediction.
Describe the three major categories of data (time-series, cross-sectional, and panel).

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