- Perform the 6 step process to test the claim: There is a positive linear correlation between the duration of an eruption and the time to the next eruption. Make sure in step four to show the scatter plot and the residual plot to see if linear regression is appropriate.
Homework Answers
a)
Ho : rho = 0
Ha : rho > 0
b)
TS = 14.6121
c)
p-value = P(t > TS) = 0.0000
d)
p-value < alpha
hence we reject the null hypothesis
e)
we conclude that There is a positive linear correlation between the duration of an eruption and the time to the next eruption
Add Answer to:
Perform the 6 step process to test the claim: There is a positive linear correlation between the duration of an eruption and the time to the next eruption. Make sure in step four to show the scatte...
Run the following multivariate linear regression models: Model 1: X3 and X4 Model 2: X2,X3,and X4...
Run the following multivariate linear regression models:
Model 1: X3 and X4
Model 2: X2,X3,and X4Model 3: X1, X3 and
X4Discuss the correlation
between each two variables using adjusted R2 and P-value. Write the
estimated equation of Y for each regression model. Briefly comment
of the Residual Plots.
SUMMARY OUTPUT Regression Statistics Tourist arrivals (X3) Residual Plot Mu R Square Adjusted R Square Standard Error Observations 0.77706686 0.60383291 0.58622549 26011267.3 48 ANOVA Significance F 4.6406E 16 2.3203E 16 34.2942181 8.9591E-10...
Run the following multivariate linear regression models: Notes: Every Professor or Tutor, I used ...
Run the following multivariate linear regression models:
Notes: Every Professor or Tutor, I used Excel to do my
data analysis ( regression) below. Thanks
1.Model 1(X3, X4):2. Model 2 ( X2, X3
&X4):3. Model 3 (X1,X3
& X4):a)
Discuss the correlation between each
two variables using adjusted R2 and P-Value
b) Write the estimated equation of Y for each regression
model.
c) Briefly comment of
the Residual Plots.
SUMMARY OUTPUT Tourist arrivals (X3) Residual Plot Regression Stotistics 80000000 Multpe R...
mpe points A Michigan travel agent wants to examine the relationship between the number of davs...
mpe points A Michigan travel agent wants to examine the relationship between the number of davs temperature anuary. Data from 10 years of bookings and temperatures were s fell below 20 degrees in December and the number of subsequent cruise bookings in collected with the following results Number of Days with Temps Below 20 in December Number of Bookings in January Number of Bookings in January 14 136 152 298 327 196 144 348 267 278 18 150 10 100...
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 ANOVA model is for a multiple regression model with two independent variables: Degrees of Sum of Mean Source Freedom Squares ...
The following ANOVA model is for a multiple regression model
with two independent variables:
Degrees
of
Sum
of
Mean
Source
Freedom
Squares
Squares
F
Regression
2
60
Error
18
120
Total
20
180
Determine the Regression Mean Square (MSR):
Determine the Mean Square Error (MSE):
Compute the overall Fstat test statistic.
Is the Fstat significant at the 0.05 level?
A linear regression was run on auto sales relative to consumer
income. The Regression Sum of Squares (SSR) was 360 and...
SUMMARY OUTPUT Regression Statistics Multiple R 0.9655 R Square 0.9321 Adjusted R Square 0.9307 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.9655 R Square 0.9321 Adjusted R Square 0.9307 Standard Error 0.5383 Observations 50 ANOVA df F 659.4383 Significance F 1.07386E-29 Regression Residual Total 1 48 49 SSM S 191.0842089 191.084209 13.90887066 0.28976814 204.9930796 Intercept Increase in profits (%) Coefficients Standard Error 2.28990 .0910 0.9513 0.0370 Stat 25.17540 25.6795 P-value .0000 0.0000 Lower 95% 2 .1070 0.8768 Upper 95% Lower 95.0%Jpper 95.0% 2.4728 2.1070 2.4728 1.0258 0.8768 10258 Increase in Manager's Salary (%) 4,00 2.00...
1. For each of the following regression models, write down the X matrix and 3 vector....
1. For each of the following regression models, write down the X matrix and 3 vector. Assume in both cases that there are four observations (a) Y BoB1X1 + B2X1X2 (b) log Y Bo B1XiB2X2+ 2. For each of the following regression models, write down the X matrix and vector. Assume in both cases that there are five observations. (a) YB1XB2X2+BXE (b) VYBoB, X,a +2 log10 X2+E regression model never reduces R2, why 3. If adding predictor variables to a...
Use the following information to answer the questions below: Heat Power is a utility company that...
Use the following information to answer the questions below: Heat Power is a utility company that would like to predict the monthly heating bill for a household in a particular region during the month of January. A random sample of 18 households in the region were selected and their January heating bill recorded. The data is shown in the table below along with the square footage of the house (SF), the age of the heating system in years (Age), and...
Run the following multivariate linear regression models:
Model 1: X3 and X4
Model 2: X2,X3,and X4Model 3: X1, X3 and
X4Discuss the correlation
between each two variables using adjusted R2 and P-value. Write the
estimated equation of Y for each regression model. Briefly comment
of the Residual Plots.
SUMMARY OUTPUT Regression Statistics Tourist arrivals (X3) Residual Plot Mu R Square Adjusted R Square Standard Error Observations 0.77706686 0.60383291 0.58622549 26011267.3 48 ANOVA Significance F 4.6406E 16 2.3203E 16 34.2942181 8.9591E-10...
Run the following multivariate linear regression models:
Notes: Every Professor or Tutor, I used Excel to do my
data analysis ( regression) below. Thanks
1.Model 1(X3, X4):2. Model 2 ( X2, X3
&X4):3. Model 3 (X1,X3
& X4):a)
Discuss the correlation between each
two variables using adjusted R2 and P-Value
b) Write the estimated equation of Y for each regression
model.
c) Briefly comment of
the Residual Plots.
SUMMARY OUTPUT Tourist arrivals (X3) Residual Plot Regression Stotistics 80000000 Multpe R...
mpe points A Michigan travel agent wants to examine the relationship between the number of davs temperature anuary. Data from 10 years of bookings and temperatures were s fell below 20 degrees in December and the number of subsequent cruise bookings in collected with the following results Number of Days with Temps Below 20 in December Number of Bookings in January Number of Bookings in January 14 136 152 298 327 196 144 348 267 278 18 150 10 100...
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 ANOVA model is for a multiple regression model
with two independent variables:
Degrees
of
Sum
of
Mean
Source
Freedom
Squares
Squares
F
Regression
2
60
Error
18
120
Total
20
180
Determine the Regression Mean Square (MSR):
Determine the Mean Square Error (MSE):
Compute the overall Fstat test statistic.
Is the Fstat significant at the 0.05 level?
A linear regression was run on auto sales relative to consumer
income. The Regression Sum of Squares (SSR) was 360 and...
SUMMARY OUTPUT Regression Statistics Multiple R 0.9655 R Square 0.9321 Adjusted R Square 0.9307 Standard Error 0.5383 Observations 50 ANOVA df F 659.4383 Significance F 1.07386E-29 Regression Residual Total 1 48 49 SSM S 191.0842089 191.084209 13.90887066 0.28976814 204.9930796 Intercept Increase in profits (%) Coefficients Standard Error 2.28990 .0910 0.9513 0.0370 Stat 25.17540 25.6795 P-value .0000 0.0000 Lower 95% 2 .1070 0.8768 Upper 95% Lower 95.0%Jpper 95.0% 2.4728 2.1070 2.4728 1.0258 0.8768 10258 Increase in Manager's Salary (%) 4,00 2.00...
1. For each of the following regression models, write down the X matrix and 3 vector. Assume in both cases that there are four observations (a) Y BoB1X1 + B2X1X2 (b) log Y Bo B1XiB2X2+ 2. For each of the following regression models, write down the X matrix and vector. Assume in both cases that there are five observations. (a) YB1XB2X2+BXE (b) VYBoB, X,a +2 log10 X2+E regression model never reduces R2, why 3. If adding predictor variables to a...
Use the following information to answer the questions below: Heat Power is a utility company that would like to predict the monthly heating bill for a household in a particular region during the month of January. A random sample of 18 households in the region were selected and their January heating bill recorded. The data is shown in the table below along with the square footage of the house (SF), the age of the heating system in years (Age), and...
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