Question 4I'm trying to forecast how many TVs my shop will sell in 2021. I have data for the previous 3 years:2018: 352019:522020:36.What is the forecast for 2021 using the exponential smoothing method, given that I seed the exponential smoothingmethod with the naive forecast method to get the 2019 forecast (35), and alpha=0.87 Round to the nearest wholenumber. For example, if your answer is 203.33, enter 203 into the box.

It is given that the forecast method for 2019 is 35.

Answered: I'm trying to forecast how many TVs my…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Illustrate the Regression Discontinuity Estimators?
I recently asked this question and was wondering if you could show me how to do it, not just putting it in a caculator or excel but written out equations and seeing my information plugged in so I can understand. Thank you. Calculate the simple linear regression to determine if the amount of time spent on homework can be predicted by amount of sleep. Graph the relationship and determine, numerically, if there are any outliers. Interpret all results in a paragraph citing the appropriate statisitcs.
1:26 AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-squares. Shasia v In ongoing economic analyses, the U.S. federal government compares per capita incomes not only among different states but also for the same state at different times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have been able to predict the 1999 per capita income for a state (denoted by y) from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of fourteen states in the years 1980 and 1999 (source U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot in Figure 1, and the least- squares regression line is drawn. The equation for this line is î=3.09 + 2.47x. 1980 per capita 1999 per capita income, x income, y (in $1000s) (in $1000s) Vermont 8.7 25.9 Hawai…
ic SIS Days 1 2 Infected Individuals 678 722 a = 634.581 b = 1.073 2353 The table below shows the number of individuals infected with a disease t days after its first detected by the CDC. b = N X S For each of the following problems, enter regression equation values with at least 3 decimal places. Enter predictions to the nearest whole individual. ✓individuals III 3 4 5 756 825 882 1) Use regression to find an exponential equation that best fits the data above. The equation has form y = abt where: H 6 7 945 1017 individuals M Use the model to predict the number of individuals infected with the disease after 19 days. 8 1104 2) Use regression to find a linear equation that best fits the data above. The equation has form y = mt + b where: Use the model to predict the number of individuals infected with the disease after 19 days. 3) Use regression to find a quadratic equation that best fits the data above. The equation has form y = at² + bt + c where: S MyOpe
(d) If the life expectancy is increased by 3.5 years in a certain country, how much will the happiness index change? Round to two decimal places. (e) Use the regression line to predict the happiness index of a country with a life expectancy of 84 years. Round to two decimal places.
9. A wildlife researcher is interested in predicting a mammal’s lifespan (in years) based on its average number of hours of sleep per night. Data was obtained from a large random sample of mammals, and the regression equation turned out as follows: Predicted lifespan = 36.93 – 1.64 (average hours of sleep) Which one of the following statements is a correct interpretation of this equation? 1. Approximately 1.64% of the variability in lifespan can be explained by the regression equation. 2. As lifespan increases by one year, average hours of sleep is predicted to decrease by 1.64 hours. 3. As lifespan increases by one year, average hours of sleep is predicted to increase by 36.93 hours. 4. As average hours of sleep increases by one hour, lifespan is predicted to increase by 36.93 years. 5. As average hours of sleep increases by one hour, lifespan is predicted to decrease by 1.64 years.
8. Suppose the following estimated regression equation was determined to predict salary based on years of experience. Estimated Salary = 27,017.66 +2343.69 (Years of Experience) What is the estimated salary for an employee with 26 years of experience? Answer:
An analyst must decide between two different forecasting techniques for weekly sales of roller blades: a linear trend equation and the naive approach. The linear trend equation is F+ = 122 +1.9t, and it was developed using data from periods 1 through 10. Based on data for periods 11 through 20 as shown in the table, which of these two methods has the greater accuracy if MAD and MSE are used? (Round your intermediate calculations and final answers to 2 decimal places.) t 11 12 13 14 15 16 17 18 19 20 Units Sold 143 149 152 145 155 152 154 159 159 162 Click here for the Excel Data File MAD (Naive) MAD (Linear) MSE (Naive) MSE (Linear))
The fitted regression is Sales = 842 − 37.5 Price. (a-1) If Price = 1, what is the prediction for Sales? (Round your answer to 1 decimal place.) (b) If Price = 20, what is the prediction for Sales?
lakhs) of a certain commodity for the year On the basis of quarterly sales (in 2008-12, the following calculations were made : Trend: Y = 20 + 0.5 t with origin at 1st quarter of 2008. where 1 = time unit = one quarter and y = quarterly sales ( Seasonal variations: Quarter I II III IV Seasonal Index 80 90 120 110 Estimate the quarterly sale for the year 1979 using multiplicative model.

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