Which of the following statements about time-series forecasting methodsis TRUE? Check all that apply.Linear Regression for Seasonality without Trend method is appropriate for datawith a seasonal pattern only.You choosè a small value for "k" when using the Simple Moving Average methodof order "k" to track movement in the most recent data.Linear Regression is the method of choice for data with a trend pattern.O You choose a large value for alpha when using the Exponential Smoothingmethod to give less weight to the most recent data.Linear Regression uses the time period as the dependent variable.O There is no time-series forecasting method for data with both seasonal andtrend patterns.The Simple Average method is also known as the Historical Moving Averagemethod.

Time series forecasting: It is used to make scientific predictions based on historical time stamped…

Answered: Which of the following statements about…

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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Suppose that a regression line is found to be ý = 0.5470x +7.7000 What is the predicted value for a = 7? O a. 11.53 O b. 54-45 C. 53-35 O d. -3.87
Sam had 3 caffeinated drinks on april 24 Use linear regression to predict the number non-caffeinated drink he had on April 24 and calculate the error of estimate for your prediction.
A simple linear regression model was fitted to two variables: Y, the response variable and X, the explanatory variable. A scatter plot of Y versus X as well as the residual plot after fitting the least squares regression line to the data are given below. 0.4 0.35 0.3 0.25 > 0.2 0.15 0.1 0.05 2 10 X 0.2 0.15 0.1 0.05 10 -0.05 -0.1 Residuals
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J 1 The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response why variable is city fuel consumption in miles/gallons. The predictor X variables are weight weight in pounds, DISP engine displacement leaders NHW Y Highway fuel consumption in miles/gallons. It's only one predictor X variable is used to predict the city fuel consumption, which single variable is best? Why? The best variable is (WT/HWY/DISP) because it has the best combination of (large/small) p-value, (answer) and (large/small) adjusted R^2 (answer) (type integers are decimals. Do not round)
q12
The figure accompanying this exercise is a histogram of the residuals for a simple linear regression. Which condition(s) are verified in this figure? Be specific about which of the four necessary conditions are verified in this figure. (Select all that apply.) -2 -1 Residual O1. The form of the equation that links the mean value of y to x must be correct. 02. Observations in the sample are independent of each other. 03. For individuals in the population with the same particular value of x, the distribution of the values of y is a normal distribution with no outliers that influence the results unduly. 04. The standard deviation of the values of y from the mean y is the same regardless of the value of the x variable.
Which of the following is a true attribute of a regression line? A. A regression line is a tool for making predictions about future observed values for center variables B. A regression line reduces a linear relationship to its bare essential. C. A regression line allows us to analyze a relationship without being distracted by small details. D. all of the above
27. What percentage of the variability is accounted for by the regression model?A. 1% B. 2% C. 98% D. 99%28. Is there a linear relationship between the dependent variable and the independent variables?A. Cannot be knownB. MaybeC. NoD. Yes29. What will be the value of y if x = 0?A. -12.87 B. -0.08 C. 0.70 D. 0.99
1. y=____+______x (Round to two decimal places as needed.) 2. Identify a characteristic of the data that is ignored by the regression line. A.There is no trend in the data. B.The data has a pattern that is not a straight line. C.There is an influential point that strongly affects the graph of the regression line. D.There is no characteristic of the data that is ignored by the regression line.
Which of the following are true? (Select all that apply.) 1 An assumption of a linear regression is a consistent dispersion of the observations from the regression line. 2 An assumption of a linear regression is that the residuals form a normal distribution. 3 Omitted variable bias is when you don’t include the dependent variable.
q10

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