Homework Answers
Normal Probability Plot: it is a graphical technique used to see whether or not data set is normally distributed.
Uses: It helps in finding whether the data is normally distributed or not. It also tells about best fit of the data in the the normal distribution.
The given graphical representation in the question is the good fit of normal distribution since the plotted value is in almost st. line.
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(e) What is a normal probability plot? Briefly state its uses. Interpret the following normal probability...
(e) Using the following residual plot from Excel, write a critique on the fitted linear regression...
(e) Using the following residual plot from Excel, write a critique on the fitted linear regression model. [3 marks] Residual Plot 8000 6000 4000 2000 Residual -2000 -4000 -6000 -8000 -10000 - 12000 Dec98 Mayop Sep01 Octos Mar07 Julos Jan 03 Jun 04 Quarters
Decide (with short explanations) whether the following statements are true or false. e) In a simple linear regression model with explanatory variable x and outcome variable y, we have these summary s...
Decide (with short explanations) whether the following
statements are true or false.
e) In a simple linear regression model with explanatory variable x and outcome variable y, we have these summary statisties z-10, s/-3 sy-5 and у-20. For a new data point with x = 13, it is possible that the predicted value is y = 26. f A standard multiple regression model with continuous predictors and r2, a categorical predictor T with four values, an interaction between a and...
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...
What type of experimental design is employed in this problem? 1. 3. Use a multiple boxplot in ord...
can you answer step by step please
What type of experimental design is employed in this problem? 1. 3. Use a multiple boxplot in order to compare responses B,C, D) are to bo oompared for read wear aner 24,000 mles of demng Anexpennent 4. State clearly the hypothesis that we are testing in this probiem 5. Run ANOVA on the data and ganerate the output with plots f there is eny significent difference fferent mean? (Hint use the boxplots from...
QUESTION 19 For the following software output, check each assumption/condition to run linear regression and state...
QUESTION 19 For the following software output, check each assumption/condition to run linear regression and state whether it is appropriate to use linear regression. Bivariate Fit of pluto By alpha 20 15 10 5 0 e 0.05 0.15 C 0.1 alpha Linear Fit Linear Fit pluto -0.597417 16543195*alpha Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.915999 0.911999 2.172963 6.73913 23 Analysis of Variance Sum of DF Squares Mean Square Source...
Need help with stats true or false questions Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% con...
Need help with stats true or false questions
Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% confidence interval for A In this case, a t-test with significance level 1% rejects the null hypothesis Ho : A-0 against a two sided alternative. b) Complicated models with a lot of parameters are better for prediction then simple models with just a few parameters c)...
The question asks: what model would you use? Briefly describe why you would use this m...
The question asks: what model would you use? Briefly
describe why you would use this model. What concerns, if
any, would you have about the effectiveness of your model? Provide
your responses and your justification in the space
below.
Attached is the output of linear regression. my first thoughts
are that my other choices are higher order regression models. how
do I figure this out?
SUMMARY OUTPUT Regression Statistics Multiple R 0.87402183 R Sqae 0.76391416 Adjusted RS 0.7048927 Standard Erro...
please use R for this problem, provide R codes. Kiplinger's "Best Values in Public Colleges" provides...
please use R for this problem, provide R codes.
Kiplinger's "Best Values in Public Colleges" provides a ranking of U.S. public colleges based on a combination of various measures of academics and affordability. The dataset "EX11-18BESTVAL.csv" includes a sample of 25 colleges from Kiplinger's 2015 report. Let's focus on the relationship between the average debt in dollars at graduation (AveDebt, the response variable) and the explanatory variables Admit (admission rate), GradRate (graduation rate), InCost Aid (in-state cost per year after...
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...
a.Present (here) the plot of the residuals of this simple linear regression model against its fitted...
a.Present (here) the plot of the residuals of this simple
linear regression model against its fitted values.
b. Describe (here) the appearance of this residual
plot.
c.State (here) the RMSE of this regression.
MPG 43.1 19.9 19.2 Horsepower 48 110 105 165 139 103 115 155 142 150 71 76 65 100 84 58 88 92 139 110 90 17.7 18.1 20.3 21.5 16.9 ISS 185 27.2 41.5 46.6 23.7 27.2 39.1 28.0 24.0 20.2 20.5 28.0 34.7 36.1 35.7...
(e) Using the following residual plot from Excel, write a critique on the fitted linear regression model. [3 marks] Residual Plot 8000 6000 4000 2000 Residual -2000 -4000 -6000 -8000 -10000 - 12000 Dec98 Mayop Sep01 Octos Mar07 Julos Jan 03 Jun 04 Quarters
Decide (with short explanations) whether the following
statements are true or false.
e) In a simple linear regression model with explanatory variable x and outcome variable y, we have these summary statisties z-10, s/-3 sy-5 and у-20. For a new data point with x = 13, it is possible that the predicted value is y = 26. f A standard multiple regression model with continuous predictors and r2, a categorical predictor T with four values, an interaction between a and...
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...
can you answer step by step please
What type of experimental design is employed in this problem? 1. 3. Use a multiple boxplot in order to compare responses B,C, D) are to bo oompared for read wear aner 24,000 mles of demng Anexpennent 4. State clearly the hypothesis that we are testing in this probiem 5. Run ANOVA on the data and ganerate the output with plots f there is eny significent difference fferent mean? (Hint use the boxplots from...
QUESTION 19 For the following software output, check each assumption/condition to run linear regression and state whether it is appropriate to use linear regression. Bivariate Fit of pluto By alpha 20 15 10 5 0 e 0.05 0.15 C 0.1 alpha Linear Fit Linear Fit pluto -0.597417 16543195*alpha Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.915999 0.911999 2.172963 6.73913 23 Analysis of Variance Sum of DF Squares Mean Square Source...
Need help with stats true or false questions
Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% confidence interval for A In this case, a t-test with significance level 1% rejects the null hypothesis Ho : A-0 against a two sided alternative. b) Complicated models with a lot of parameters are better for prediction then simple models with just a few parameters c)...
The question asks: what model would you use? Briefly
describe why you would use this model. What concerns, if
any, would you have about the effectiveness of your model? Provide
your responses and your justification in the space
below.
Attached is the output of linear regression. my first thoughts
are that my other choices are higher order regression models. how
do I figure this out?
SUMMARY OUTPUT Regression Statistics Multiple R 0.87402183 R Sqae 0.76391416 Adjusted RS 0.7048927 Standard Erro...
please use R for this problem, provide R codes.
Kiplinger's "Best Values in Public Colleges" provides a ranking of U.S. public colleges based on a combination of various measures of academics and affordability. The dataset "EX11-18BESTVAL.csv" includes a sample of 25 colleges from Kiplinger's 2015 report. Let's focus on the relationship between the average debt in dollars at graduation (AveDebt, the response variable) and the explanatory variables Admit (admission rate), GradRate (graduation rate), InCost Aid (in-state cost per year after...
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...
a.Present (here) the plot of the residuals of this simple
linear regression model against its fitted values.
b. Describe (here) the appearance of this residual
plot.
c.State (here) the RMSE of this regression.
MPG 43.1 19.9 19.2 Horsepower 48 110 105 165 139 103 115 155 142 150 71 76 65 100 84 58 88 92 139 110 90 17.7 18.1 20.3 21.5 16.9 ISS 185 27.2 41.5 46.6 23.7 27.2 39.1 28.0 24.0 20.2 20.5 28.0 34.7 36.1 35.7...
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