The forecast for 2019 by the logarithmic regression method is88.441181.533486.393884.9817Question 13 (ListenThe MAE of the logarithmic regression method is1.37921.10112.07662.0653Year2002200320042005200620072008200920102011201220132014201520162017Sales45474848525356585964697073767980

Solution: Let X be the Year and Y be the sales. From the given information,

Answered: The forecast for 2019 by the…

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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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
Years Height (cm) 0.9 0.79 2.0 3.98 2.1 4.81 4.0 11.41 4.8 29.72 5.2 24.2 6.2 26.81 9.0 91.47 9.1 129.45 Use automatic linear regression (e.g. using Excel or Sheets) to find a line of best fit, with the Years as the "x" values and the Height (cm) as "y" values. Explain exactly what you did.
A simple regression model for 10 pair of data resulted in a standard error of 3.95 (i.e., Se = 3.95), and the. The sum of squares of error (SSE) is ______. a. 187.23 b. 171.63 c. 156.03 d. 140.42 e. 124.82
Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $3.98 per slice? Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.152 0.353 1.000 1.251 1.753 2.003 2.254 2.300 2.747 2.997 0.151 0.346 1.000 1.347 1.497 2.004 2.253 2.551 2.747 2.750 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is y=+x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.) The best predicted subway fare when pizza costs $3.98 per slice is $ (Round to the nearest cent as needed.)
A 18.2% B 15.7% C 22.2% D 4.4%
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Take a look at the following sample regressions (type of regression in top row), choose the type of model that best fits the data. Linear Logarithmic Intercept 6.7904 −5.6712 x 1.0607 NA In(x) NA 10.5447 se 2.4935 1.5231 R2 0.8233 0.9341 Adjusted R2 0.8013 0.9259 Linear Logrithmic Both equally Neither model explains a majority of the variation in y
The table below shows the average temperature in New York City (NYC), measured in degrees Fahrenheit (°F), where January is month 1, February is month 2, etc. Jul Jan 38.8 21.3 59.2 Using the regression, the average annual temperature in NYC is predicted to be 58.4 Jun Aug Oct Nov Feb Mar Apr 40.5 47.3 56.8 Sep 72.7 62.4 53.2 76.8 80.1 The data above can be modelled by an equation in the form y = a sin (bx+c) + d. 60.0 Ma 68.4 sin Dec 43.0
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Explain well and type the answer).
The data presented is a multiple logistic regression analysis and the models are shown below. In the models below, the data are coded as follows: p = the proportion of children with a diagnosis of ADHD, Child Exposed and Father’s Diagnosis are coded as 1 = yes and 0 = no. What is the odds ratio adjusted for father’s diagnosis? (Hint: Use only the appropriate model to find the odds ratio) (1) = –2.216 + 1.480 Child exposed (2) Father with diagnosis: = –1.665 + 1.297 Child exposed (3) Father without diagnosis: = –2.343 + 0.823 Child exposed (4) = –2.398 + 1.501 Child exposed + 0.906 Father’s diagnosis

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