![(e) Using the following residual plot from Excel, write a critique on the fitted linear regression model. [3 marks] Residual](http://img.homeworklib.com/questions/e9b197e0-6e59-11ea-8e3f-73186ef15b5e.png?x-oss-process=image/resize,w_560)
Homework Answers
The fitted linear regression line does not seem to be a good fit to the data .
The error terms seem to be getting larger and larger from september 2001 to october 2005.
The linear regression model will not be the appropriate model here . You need a model of a higher order .
One thing we can observe is the effect of cycles . As the points are making a high and a through and a high again.
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(e) Using the following residual plot from Excel, write a critique on the fitted linear regression...
Forecast expected demand using the data given for lead-times of 1, 2, 6, and 12 along...
Forecast expected demand using the data given for lead-times of 1, 2, 6, and 12 along with a 90% confidence interval using each the following techniques. Experiment with any required forecasting parameters. a. Moving average b. Simple exponential smoothing c. Holt’s model d. Winter’s model e. Which method is preferred? Month Demand 1 2000 2 3000 3 3000 4 3000 5 4000 6 6000 7 7000 8 6000 9 10000 10 12000 11 14000 12 8000 13 3000 14 4000...
(d) Write down the fitted simple linear regression model (equation) and discuss its merits using the following outp...
(d) Write down the fitted simple linear regression model (equation) and discuss its merits using the following output. Consider the intercept, slope, overall goodness of model etc. when commenting. (Note that, in Excel, the time variable begins at Year 1900, i.e. 01/01/1900, 12am). [4 marks] Intercept X Variable 1 Coefficients -164070 5.736757 Standard Error t Stat P-value 28361.13278 -5.785021448 1.64E-06 0.749106082 7.65813654 6.68E-09 Lower 95% Upper 95% -221706.5175 -106433.01 4.214389946 7.2591234
HELP ASAP The following is Excel output from a fitted linear regression model relating the sale price of a home (y...
HELP ASAP
The following is Excel output from a fitted linear regression model relating the sale price of a home (y in thousands of dollars) to age of the home in years. Intercept Age Coefficients 213.365436 -1.207517218 Standard Errort Stat P value 1.450657792 147.0819 0 0.02997096940.2897 1.28 279 Lower 95% Upper 95% 210.5209309 216.2099011 1.26620524 1160749195 Refer to the information above on the regression using age to predict selling price of houses. Which of the following is the correct way...
Q3. A company in the field of fast moving consumer goods has launched several new products...
Q3. A company in the field of fast moving consumer goods has launched several new products in recent years. The sales manager has to provide a forecast for sales in the next quarter. These forecasts are used to draw up an initial production plan, which is updated daily as new data becomes available. To provide these forecasts, the sales manager has conducted regression analysis but needs your advice on its interpretation. Extracts from the Excel regression analysis for products A...
The following is Excel output from a fitted linear regression model relating the sale price of...
The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 - 1.207517218 Standard Error Stat P-value 1.450657792 147.0819 0 0.029970869 -40.2897 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148749195 Refer to the information above on the regression using age to predict selling price. Which of the following gives the 95% confidence interval for by...
Illustrate the concept of residuals using your scatterplot from Problem 2. Below is the data and...
Illustrate the
concept of residuals using your scatterplot from Problem
2.
Below is the data and scatterplot
from problem 2:
FIRM
1
2
3
4
5
6
R&S
(Millions, $), x
219
129
162
13
57
16
Sales
(Millions, $), y
5790
4300
9980
201
1904
794
Explain your answer.
Scatterplot for R&D Expenditures and Sales for Six Health Care Firms 12000 10000 8000 Sales (Millions, $), y 6000 4000 2000 50 200 250 100 150 R&D (Millions, $),x
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Err...
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Error.1488 Observations21 ANOVA Source Regression Residual Total df MS Significance of F 1.66E-07 3 308.68 35.117 102.893 2.930 17 20 358.49 49.81 Coefficient Standard Error 6.2000 0.7260 0.7260 0.9500 t Stat 3.7097 0.2755 -2.0523 0.5158 23.00 0.20 Intercept X2 X3 0.49 (i) Find the limits of the 95 percent confidence interval...
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...
6. (textbook) An analyst fitted a regression model to predict city MPG using as predictors Length...
6. (textbook) An analyst fitted a regression model to predict city MPG using as predictors Length (of car in inches), Width (of car in inches) and Weight (of car in pounds). a. Intuitively, what association do you expect between the explanatory variables and MPG? b. Do you see anything of concern about these variables being used as explanatory variables? Explain S c. What does the matrix plot done in class show you? Explain d. Write the null and alternative hypothesis...
MULTIPLE LINEAR REGRESSION Write a paragraph about what you understand from the below scatter plot matrix...
MULTIPLE LINEAR REGRESSION
Write a paragraph about what you understand from the below
scatter plot matrix A, B, C. Include significant information.
Scatter Plot for Income (Y) vs Age (X1) Scatter Plot for Income (Y) vs Life Expectancy (X2) 100 90 80 70 60 50 40 30 - 20 10 60 50 40 30 20 10 y 0.0012x+4.1682 R2 0.4281 y -3E-05x +41.749 R2 0.0027 10000 20000 30000 40000 50000 60000 70000 80000 10000 20000 30000 40000 50000 60000 70000...
(d) Write down the fitted simple linear regression model (equation) and discuss its merits using the following output. Consider the intercept, slope, overall goodness of model etc. when commenting. (Note that, in Excel, the time variable begins at Year 1900, i.e. 01/01/1900, 12am). [4 marks] Intercept X Variable 1 Coefficients -164070 5.736757 Standard Error t Stat P-value 28361.13278 -5.785021448 1.64E-06 0.749106082 7.65813654 6.68E-09 Lower 95% Upper 95% -221706.5175 -106433.01 4.214389946 7.2591234
HELP ASAP
The following is Excel output from a fitted linear regression model relating the sale price of a home (y in thousands of dollars) to age of the home in years. Intercept Age Coefficients 213.365436 -1.207517218 Standard Errort Stat P value 1.450657792 147.0819 0 0.02997096940.2897 1.28 279 Lower 95% Upper 95% 210.5209309 216.2099011 1.26620524 1160749195 Refer to the information above on the regression using age to predict selling price of houses. Which of the following is the correct way...
Q3. A company in the field of fast moving consumer goods has launched several new products in recent years. The sales manager has to provide a forecast for sales in the next quarter. These forecasts are used to draw up an initial production plan, which is updated daily as new data becomes available. To provide these forecasts, the sales manager has conducted regression analysis but needs your advice on its interpretation. Extracts from the Excel regression analysis for products A...
The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 - 1.207517218 Standard Error Stat P-value 1.450657792 147.0819 0 0.029970869 -40.2897 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148749195 Refer to the information above on the regression using age to predict selling price. Which of the following gives the 95% confidence interval for by...
Illustrate the
concept of residuals using your scatterplot from Problem
2.
Below is the data and scatterplot
from problem 2:
FIRM
1
2
3
4
5
6
R&S
(Millions, $), x
219
129
162
13
57
16
Sales
(Millions, $), y
5790
4300
9980
201
1904
794
Explain your answer.
Scatterplot for R&D Expenditures and Sales for Six Health Care Firms 12000 10000 8000 Sales (Millions, $), y 6000 4000 2000 50 200 250 100 150 R&D (Millions, $),x
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Error.1488 Observations21 ANOVA Source Regression Residual Total df MS Significance of F 1.66E-07 3 308.68 35.117 102.893 2.930 17 20 358.49 49.81 Coefficient Standard Error 6.2000 0.7260 0.7260 0.9500 t Stat 3.7097 0.2755 -2.0523 0.5158 23.00 0.20 Intercept X2 X3 0.49 (i) Find the limits of the 95 percent confidence interval...
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...
6. (textbook) An analyst fitted a regression model to predict city MPG using as predictors Length (of car in inches), Width (of car in inches) and Weight (of car in pounds). a. Intuitively, what association do you expect between the explanatory variables and MPG? b. Do you see anything of concern about these variables being used as explanatory variables? Explain S c. What does the matrix plot done in class show you? Explain d. Write the null and alternative hypothesis...
MULTIPLE LINEAR REGRESSION
Write a paragraph about what you understand from the below
scatter plot matrix A, B, C. Include significant information.
Scatter Plot for Income (Y) vs Age (X1) Scatter Plot for Income (Y) vs Life Expectancy (X2) 100 90 80 70 60 50 40 30 - 20 10 60 50 40 30 20 10 y 0.0012x+4.1682 R2 0.4281 y -3E-05x +41.749 R2 0.0027 10000 20000 30000 40000 50000 60000 70000 80000 10000 20000 30000 40000 50000 60000 70000...
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