Many supply managers use a monthly reported survey result known as the purchasingmanagers' index (PMI) as a leading indicator to forecast future sales for their businesses.Suppose that the PMI and your business sales data for the last 10 months are thefollowing:2 3 4 5 6 7 8 9 1042.1 43.6 42.4 38.3 40.7 44.9 46.9 49.4 49.3 51.7117Month:PMI:Sale (1000s):121122121114121120133130 140a. Construct a causal regression model using PMIl as the causal variable. How welldoes your model fit the data? (Enter r-squared as decimal (not percent). Roundyour answers to 2 decimal places.)Sales (1000s) =R? =(PMI) +b. Suppose that the PMI is truly a leading indicator. That is, the PMI value in one periodinfluences sales in the following period.b-1. Construct a new regression model using this information. (Enter r-squared asdecimal (not percent). Round your answers to 2 decimal places.)Sales (1000s)R2 =(PMI)+

A regression model is a function that represents the connection between a response, dependent,…

Answered: a. Construct a causal regression model…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

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Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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A company that supplies gasoline to a city has recorded the weeklyusage (tons/week) for the past 3 years. The file BA3653GasolineRecord.xlsxcontains this record.(a) Propose a method for predicting the demand for gasoline. Use thatmethod to forecast demand for next year (weeks 157 to 208).(b) Improve your prediction in part (a) by including a statement about demand variability. (Hint: Look at or the range of the data.) week demand (tons) 1 1174.5 2 1316.2 3 1197 4 1127.3 5 1193.1 6 1260.7 7 1378.2 8 1273.7 9 1366.4 10 1113 11 1177.7 12 1056 13 1291.2 14 1269.1 15 1289.6 16 1181 17 1249 18 1212 19 1286.2 20 1204.8 21 1266.2 22 1332.4 23 1236.3 24 1266.1 25 1415.3 26 1100.1 27 1208.1 28 1505.1 29 1282.7 30 1190.5 31 1152.8 32 1089.7 33 1404.7 34 1308.6 35 1255.4 36 1106.7 37 1484.6 38 1317.4 39 1294.7 40 1154 41 1449.9 42 1174.9 43 1466.9 44 1282.4 45 1228 46 1174 47 1196.2 48 1443.8 49…
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Pls answer b and c
Colin Alexander is a new supply chain analyst at Glade Computers. Glade is expanding its use of analytics and wants to use linear regression to construct predictive models to assist management. Colin, eager to make a good impression with his manager, volunteered to develop a model for the company's Warranty Costs (pesos) as a function of Quality Investment (pesos). Using the data in the table below, find the regression coefficients for the specified model. Warranty Costs Quality investment 47221.27 3556.63 32679.91 9599.42 35934.66 8254.17 42838.83 4606.78 33819.13 8953.61 40290.11 6275.81 54037.5 715.85 38182.87 7522.7 41644.44 6139.73 36203.05 8425.02 49586.83 2247.01 52924.74 2596.26 35566.24 9696.06 34068.8 9326.79 53011.8 1719.7 36941.07 9393.6 Enter answers to 2 decimal places.Bo: B1: Enter answers to 4 decimal places.(Intercept) Standard Error: (Intercept) p-value: Residual standard error: R^2:
From the following annual data of sales (in 000 .$.) Find the trend values by using least square method. Also estimate the sales of 2014. Year 2004 2005 2006 2007 2008 2009 2010 Sales {In 000 $} 77 88 94 85 91 98 90
When testing the IFE (International Fisher Effect), we run a linear regression, R^2 could be used to measure
The following table shows quarterly sales (in thousand units) for a product over 4 years. The overall sales average for the entire 4 years is 166.125 (thousand units). Fill in the missing values assuming there is no trend. Round seasonal indexes to 4 decimal places. Report Averages accurate to at most 2 decimal places. Report Actual sales ('000) accurate to the nearest integer. Year Year Seasonal Year 1 Year 2 Average 4 Index Quarter 205 211 182 1.1768 1 Quarter 152 170 158 156 2 Quarter 182 190 198 193 3 Quarter 128 112 113 0.7178 4
Which modeling techniques are suitable to build a predictive model ? (Multiple answer may be correct) Group of answer choices Multiple linear regression Classification tree Logistic regression Clustering
Please help with correct answers: step by step: last time submitted- answers were incorrect A statistical program is recommended. The quarterly sales data (number of copies sold) for a college textbook over the past three years follow. Quarter Year 1 Year 2 Year 3 1 1,690 1,800 1,850 2 940 900 1,100 3 2,625 2,900 2,930 4 2,500 2,360 2,615 a) Use a regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. (Round your numerical values to the nearest integer.) Qrt1 = 1 if quarter 1, 0 otherwise; Qrt2 = 1 if quarter 2, 0 otherwise; Qrt3 = 1 if quarter 3, 0 otherwise t = b) Using the equation developed in part (b), compute the quarterly forecasts for year 4. (Round your answers to the nearest ten.) quarter 1 forecast____? quarter 2 forecast____? quarter 3 forecast ____? quarter 4 forecast ____? c) Let t = 1 refer to the observation in Quarter 1 of Year 1; let t = 2 refer to the observation in Quarter 2 of Year…
According to Stuart (2010), which of the following statements about observational study designs for causal inference are typically true? Choose all that apply. Including propensity score estimates as a predictor in a regression model on outcomes can help resolve imbalance between treatment and control groups for covariates used in the propensity score model Standard diagnostic tools for binary prediction or classification models (e.g. logistic regression or classification trees) are similarly used in evaluating propensity score estimates When conditional ignorability holds given the observed covariates, then the treatment assignment will also be ignorable conditioned on the propensity scores Overfitting in propensity score estimation can achieve more efficient estimates of treatment effects than using propensity score estimates that are closer to the true propensities Unlike full matching, subclassification, and weighting methods, nearest neighbor matching does not necessarily use all…
. Suppose that you are working as an analyst. Your task is to value company XYZ based on financial data available for this company. Discuss how you would go about doing this using themultiple growth rate Gordon growth model. Critically evaluate your approach by discussing any assumptions you make (about forecasts, discount rate etc.). What data is needed to value XYZ?
Explain these term Mixture of extreme value distribution. Modelling complex relationship between the variable. Lifetime model. (Please explain in your own wording so I can easily understand)

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