(USE ONLY EXCEL SHEET FOR DATA, FORMULAS, andthe GRAPHS) For the following list of data (number ofsold cars in every year), apply "exponential smoothingwith trend" for all the years below. (i. e find the Forecastcase1 use \alpha = 0.01 and \including trend):beta = 0.3------case2 use: for \alpha = 0.1 and \beta = 0.05 Calculate MAD for both cases (5 %)..Decide which case values are better and why? (5 %)Assume trend is zero for the 2012 years. Year Numberof sold cars2012 107 2013 113 2014 150 2015 133 2016 151 2017 1Based on least squared regression method find theprojections for the years 2021, 2022, and 2023. Plotthe number of sold cars versus years (points graph) andshow the trend line based on your projections. (5%).

The objective of this question is to apply the exponential smoothing with trend method to forecast…

Answered: (USE ONLY EXCEL SHEET FOR DATA,…

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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Please provide a detailed solution for the question below (Table: Physicians per 10,000 Population and Life Expectancy) The table contains a subset of data reported in 2016 by the World Health Organization. Use these data to generate the regression equation for predicting life expectancy in a country from the number of physicians per 10,000 people in that country. To get you started, the mean and the standard deviation for both variables have been calculated. .Please provide detailed solution Table: Physicians per 10,000 Population and Life Expectancy Country Physicians/10,000 people Life expectancy Bangladesh 4.8 72.7 Cambodia 1.7 69.4 United States 25.9 78.5 Egypt 8.1 70.5 France 32.3 82.9 Japan 24.1 84.2
**Based on the regression results, answer the following questions** Data are collected on the income (measured in $1000’s of $’s), house value (measured in $1000’s of $’s), years in a home, age, sex (=1 for male, =0 for female), and the amount outstanding on a mortgage (measured in $1000’s of $’s). The dependent variable is income. Based on the regression results, answer the following questions e) Are any of the explanatory (independent) variables significant at the 10% level of significance? How do you know? f) What is the predicted value of income for a person that has been in a house for 5 years, is 38 years old, is a male, has a house valued at $250,000, and has $100,000 left on their mortgage? (g) What is the effect on the income of a person being a male versus a female?
I need help please soon as possible
While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. After performing a regression, they find that the relationship is (claims per year) = 0.385*(age) + 5.218. If a customer is 44.315 years old and they make an average of 11.239 claims per year, the residual is -11.04. Interpret this residual in terms of the problem. Question 10 options: 1) The number of claims per year is 11.04 claims less than what we would expect. 2) The age is 11.04 years larger than what we would expect. 3) The age is 11.04 years less than what we would expect. 4) The number of claims per year is 11.239 claims less than what we would expect. 5) The number of claims per year is 11.04 claims…
The number of cans of soft drinks sold in a machine each week is recorded below. Develop forecasts for the period indicated and error calculations. 155, 145, 155, 162, 180, 165, 172, 149, 170, 172 Exponential smoothing. Use an alpha level of .8 What is the forecast for week 10? What is the Mean Absolute Error (MAE)?
The following regression equation is based on the analysis of four variables: SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads. RADIO_ADS is the number of radio advertisements aired weekly by the agency. WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads. The variable WEB_VISITS is the number of weekly visitors to their educational website. These data have been recorded every week for the past three years. WEB_VISITS (expected) = 109 + 0.2*SM_DOLLARS + 1.5*RADIO_ADS + 1.1*WS_DOLLARS The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $150 without changing the amount spent on radio ads or web search ads? (Round your answer to the nearest whole number.)
Please answer the attached question and its sub-part. Thanks
A patient is classified as having gestational diabetes if their average glucose level is above 140 milligrams per deciliter (mg/dl) one hour after a sugary drink is ingested. Rebecca's doctor is concerned that she may suffer from gestational diabetes. There is variation both in the actual glucose level and in the blood test that measures the level. Rebecca's measured glucose level one hour after ingesting the sugary drink varies according to the Normal distribution with μ=140+5 mg/dl and σ=5+1 mg/dl. Using the Central Limit Theorem, determine the probability of Rebecca being diagnosed with gestational diabetes if her glucose level is measured: Once? n=5+2 times n=5+4 times Comment on the relationship between the probabilities observed in (a), (b), and (c). Explain, using concepts from lecture why this occurs and what it means in context.
The following regression equation is based on the analysis of four variables: SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads. RADIO_ADS is the number of radio advertisements aired weekly by the agency. WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads. The variable WEB_VISITS is the number of weekly visitors to their educational website. These data have been recorded every week for the past three years. WEB_VISITS (expected) = 208 + 1.5*SM_DOLLARS + 0.5*RADIO_ADS + 0.8*WS_DOLLARS The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $108 without changing the amount spent on radio ads or web search ads? (Round your answer to the nearest whole number.)
Give two examples of parameters and explain each in terms of point estimates and point estimators.
please only answer the last part "Suppose that real income per capita in New Jersey increases by 1% in the next year. Use the results in column (4) to predict the change in the number of traffic fatalities in the next year. "
What test involves an analysis of rate of change in Y per single point increase in X and what is the “predictor”?

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