Homework Answers
Correct option : Trend and Seasonality
Reason : We can see that the graph is going up and overall there is an increase over the time which represents an upward trend.
Now, Seasonality refers to patterns that repeat with a fixed period of time. We can see a definite pattern in the above plot where the values increase and decrease within a fixed interval of time.
So, we can say that the model is able to capture Trend and Seasonality.
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Please explain your answer Suppose that we use least-squares to fit a seasonal-means trend to this...
Please explain your answer Suppose that we use least-squares to fit a polynomial trend to this...
Please explain your answer
Suppose that we use least-squares to fit a polynomial trend to this time series. Figure 4 displays the original time series plot along with the fitted values. Time Series and Polynomial Fit of the Trend 10 15 Time Figure 4 Which of the following characteristics is the model able to capture? Trend Seasonality Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer Time Series and the Seasonal-Means+ Polynomial Trend 10 15 Time Figure 5...
Please explain your answer
Time Series and the Seasonal-Means+ Polynomial Trend 10 15 Time Figure 5 Which of the following characteristics is the model able to capture? Trend Seasonality Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer Consider the time series plot of the differenced data in Figure 2....
Please explain your answer
Consider the time series plot of the differenced data in Figure 2. Time Series Plot of the Differenced Data 10 15 20 Time Figure 2 Which of the following characteristics are present in the time series plot of the differenced data (refer figure 2)? Periodicity Seasonality Periodicity and seasonality Periodicity and heteroskedasticity
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the ...
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the best linear least-squares fit to the following data. x2.4 3.6 3.64 4.7 5.3 y| 33.8 34.7 35.5 36.0 37.5 38.1 Plot both the linear function and the data points on the same axis system Solution We can solve the problem with the following MATLAB commands x[2.4;3.6; 3.6;4.1;4.7;5.3]; y-L33.8;34.7;35.5;36.0;37.5;38.1 X [ones ( size (x)),x); % build the matrix X for linear model %...
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform a SVD and then use it to solve the...
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform a SVD and then use it to solve the least squares problem.
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform...
A) 380 370 360 350 01/1985 01/1987 01/1989 01/1991 01/1993 01/1995 01/1997 01/1999 01/2001 01/2003 date...
A) 380 370 360 350 01/1985 01/1987 01/1989 01/1991 01/1993 01/1995 01/1997 01/1999 01/2001 01/2003 date B) 15 10 10 15 01/1985 01/1989 01/1993 01/1997 01/2001 01/2005 date C) 2- 2- 01/1985 01/1989 01/1991 01/1993 01/1995 01/1997 01/1999 01/2001 01/2003 date D) 01/1985 01/1987 01/1989 01/1991 01/1993 01/1995 01/1997 01/1999 01/2001 01/2003 01/2005 Atmospheric Carbon Dioxide Record from Alert, Canada. The time series plot in Figure A displays Monthly Carbon dioxide (CO2) measurements at Alert, NWT, Canada from July 1985...
2.4 We have defined the simple linear regression model to be y =B1 + B2x+e. Suppose...
2.4 We have defined the simple linear regression model to be y =B1 + B2x+e. Suppose however that we knew, for a fact, that ßı = 0. (a) What does the linear regression model look like, algebraically, if ßı = 0? (b) What does the linear regression model look like, graphically, if ßı = 0? (c) If Bi=0 the least squares "sum of squares" function becomes S(R2) = Gyi - B2x;)?. Using the data, x 1 2 3 4 5...
PLEASE ANSWER ALL parts . IF YOU CANT ANSWER ALL, KINDLY ANSWER PART (E) AND PART(F)...
PLEASE ANSWER ALL parts .
IF YOU CANT ANSWER ALL, KINDLY ANSWER PART (E) AND
PART(F)
FOR PART (E) THE REGRESSION MODEL IS ALSO GIVE AT THE
END.
REGRESSION MODEL:
We will be returning to the mtcars dataset, last seen in assignment 4. The dataset mtcars is built into R. It was extracted from the 1974 Motor Trend US magazine, and comcaprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models). You can find...
Problem: The relationship between a variable g and time t for a certain process is given...
Problem: The relationship between a variable g and time t for a certain process is given by: go=p, In +e 2 sin(0.3t) A set of 25 experimental data pairs were obtained. We want to calculate the optimal values of the relationship parameters P. and p, in the least-squares sense. Write a MATLAB script that solves the curve fitting problem using the least-squares method. Requirements: 1. Your script must calculate the values of the parameters P, and p, and display their...
3. (25 pts) Consider the data points: t y 0 1.20 1 1.16 2 2.34 3 6.08 ake a least squares fitting of these data using the model yü)- Be + Be-. Suppose we want to m (a) Explain how you would comput...
3. (25 pts) Consider the data points: t y 0 1.20 1 1.16 2 2.34 3 6.08 ake a least squares fitting of these data using the model yü)- Be + Be-. Suppose we want to m (a) Explain how you would compute the parameters β | 1 . Namely, if β is the least squares solution of the system Χβ y, what are the matrix X and the right-hand side vector y? what quantity does such β minimize? (b)...
Please explain your answer
Suppose that we use least-squares to fit a polynomial trend to this time series. Figure 4 displays the original time series plot along with the fitted values. Time Series and Polynomial Fit of the Trend 10 15 Time Figure 4 Which of the following characteristics is the model able to capture? Trend Seasonality Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer
Time Series and the Seasonal-Means+ Polynomial Trend 10 15 Time Figure 5 Which of the following characteristics is the model able to capture? Trend Seasonality Trend and seasonality Seasonality and heteroskedasticity
Please explain your answer
Consider the time series plot of the differenced data in Figure 2. Time Series Plot of the Differenced Data 10 15 20 Time Figure 2 Which of the following characteristics are present in the time series plot of the differenced data (refer figure 2)? Periodicity Seasonality Periodicity and seasonality Periodicity and heteroskedasticity
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the best linear least-squares fit to the following data. x2.4 3.6 3.64 4.7 5.3 y| 33.8 34.7 35.5 36.0 37.5 38.1 Plot both the linear function and the data points on the same axis system Solution We can solve the problem with the following MATLAB commands x[2.4;3.6; 3.6;4.1;4.7;5.3]; y-L33.8;34.7;35.5;36.0;37.5;38.1 X [ones ( size (x)),x); % build the matrix X for linear model %...
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform a SVD and then use it to solve the least squares problem.
4. We have the following data r 12 3 2 4.2 5. When you fitted a linear model to this data set, you solved a least squares problem. Your task here is to perform...
A) 380 370 360 350 01/1985 01/1987 01/1989 01/1991 01/1993 01/1995 01/1997 01/1999 01/2001 01/2003 date B) 15 10 10 15 01/1985 01/1989 01/1993 01/1997 01/2001 01/2005 date C) 2- 2- 01/1985 01/1989 01/1991 01/1993 01/1995 01/1997 01/1999 01/2001 01/2003 date D) 01/1985 01/1987 01/1989 01/1991 01/1993 01/1995 01/1997 01/1999 01/2001 01/2003 01/2005 Atmospheric Carbon Dioxide Record from Alert, Canada. The time series plot in Figure A displays Monthly Carbon dioxide (CO2) measurements at Alert, NWT, Canada from July 1985...
2.4 We have defined the simple linear regression model to be y =B1 + B2x+e. Suppose however that we knew, for a fact, that ßı = 0. (a) What does the linear regression model look like, algebraically, if ßı = 0? (b) What does the linear regression model look like, graphically, if ßı = 0? (c) If Bi=0 the least squares "sum of squares" function becomes S(R2) = Gyi - B2x;)?. Using the data, x 1 2 3 4 5...
PLEASE ANSWER ALL parts .
IF YOU CANT ANSWER ALL, KINDLY ANSWER PART (E) AND
PART(F)
FOR PART (E) THE REGRESSION MODEL IS ALSO GIVE AT THE
END.
REGRESSION MODEL:
We will be returning to the mtcars dataset, last seen in assignment 4. The dataset mtcars is built into R. It was extracted from the 1974 Motor Trend US magazine, and comcaprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models). You can find...
Problem: The relationship between a variable g and time t for a certain process is given by: go=p, In +e 2 sin(0.3t) A set of 25 experimental data pairs were obtained. We want to calculate the optimal values of the relationship parameters P. and p, in the least-squares sense. Write a MATLAB script that solves the curve fitting problem using the least-squares method. Requirements: 1. Your script must calculate the values of the parameters P, and p, and display their...
3. (25 pts) Consider the data points: t y 0 1.20 1 1.16 2 2.34 3 6.08 ake a least squares fitting of these data using the model yü)- Be + Be-. Suppose we want to m (a) Explain how you would compute the parameters β | 1 . Namely, if β is the least squares solution of the system Χβ y, what are the matrix X and the right-hand side vector y? what quantity does such β minimize? (b)...
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