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Compare and contrast the different methods for factoring trinomials of the form ax2+ bx + c.
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Data Analysis For Global Managers
The following are the issues that are clearly depicted from the case. The inability of patients to pay for the medical car ...
Data Analysis For Global Managers
The following are the issues that are clearly depicted from the case. The inability of patients to pay for the medical care. Most of the patients are ...
STA 3215 RC Inferential Statistics Analytics Hypothesis Tests Excel Worksheet
CompetencyGiven a real-life application, develop a hypothesis test for a population parameter and its interpretation.Instr ...
STA 3215 RC Inferential Statistics Analytics Hypothesis Tests Excel Worksheet
CompetencyGiven a real-life application, develop a hypothesis test for a population parameter and its interpretation.InstructionsScenario (information repeated for deliverable 01, 03, and 04)A major client of your company is interested in the salary distributions of jobs in the state of Minnesota that range from $30,000 to $200,000 per year. As a Business Analyst, your boss asks you to research and analyze the salary distributions. You are given a spreadsheet that contains the following information:A listing of the jobs by titleThe salary (in dollars) for each jobIn prior engagements, you have already explained to your client about the basic statistics and discussed the importance of constructing confidence intervals for the population mean. Your client says that he remembers a little bit about hypothesis testing, but he is a little fuzzy. He asks you to give him the full explanation of all steps in a hypothesis testing and wants your conclusion about a claim that the average salary for all jobs in the state of Minnesota is less than $75,000.Background information on the DataThe data set in the spreadsheet consists of 364 records that you will be analyzing from the Bureau of Labor Statistics. The data set contains a listing of several jobs titles with yearly salaries ranging from approximately $30,000 to $200,000 for the state of Minnesota.What to SubmitYour boss wants you to submit the spreadsheet with the completed calculations. Your research and analysis should be present within the answers provided on the worksheet. Grading RubricFFCBA01234Not SubmittedNo PassCompetenceProficiencyMasteryNot SubmittedDid not correctly solve a majority of the problems or at least one problem left blank.Correctly solved a majority of the problems.Correctly solved almost all the problems.All problems are solved correctly.Not SubmittedVery few steps are provided to explain how to solve the problem OR the steps provided have several errors.Fairly complete and detailed steps are provided to explain how to solve the problem OR the steps provided have some errors.Mostly complete and detailed steps are provided to explain how to solve the problem.Complete and detailed steps are provided to explain how to solve the problem.Not SubmittedExplanations generally lack a basic understanding of the statistical concepts or lack of proper terminology.Explanations demonstrate a basic understanding of most of the statistical concepts and terminology, but some explanations may be incomplete or incorrect.Explanations demonstrate a proficient understanding of most of the statistical concepts and terminology, but with small errors.Explanations demonstrate a mastery of understanding of the statistical concepts and terminology.Not SubmittedThe majority of variables, equations, and expressions are not properly formatted.The majority of variables, equations, and expressions are properly formatted.Almost all variables, equations, and expressions are properly formatted.All variables, equations, and expressions are properly formatted.
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory � ...
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory “scenario” of just two to three sentences that describes the data file for your project and why you (the ?????? Corporation/Group) are building a regression model to predict based on the set of possible independent variables
As you learned in class in Week 2, first develop a simple linear regression model using one of the above predictors of .
Cut and paste into your report the scatter plot and the Minitab Express printout for this simple linear regression model.
Write the sample regression equation.
Interpret the meaning of the intercept and slope for your fitted model.
Interpret the meaning of the coefficient of determination .
Interpret the meaning of the standard error of the estimate .
Obtain the residual plots and cut and paste them into the report. Briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 2G.
If the linearity or normality assumptions are problematic state this but continue to Step 2G with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption appears to be seriously violated contact me.
Comment on the statistical significance of your fitted model. (Note: Every team should have a fitted model that is statistically significant so contact me immediately if this is not so).
Select a value for your independent variable in its relevant range:
Predict .
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variable has the particular value you selected.
Determine the 95% prediction interval estimate of for an individual occasion when the independent variable has the particular value you selected.
As you learned in class in Weeks 3 and 4, you will be using the set of potentially meaningful numerical independent variables and one selected “two-category” dummy variable in your study to develop a “best” multiple regression model for predicting your numerical dependent variable . Follow the “9-step modeling process” described in the Powerpoints at the end of Module 4.
Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing their respective scatter plots and paste these into your report.
Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable.
Then review Slides 6 through 16 of the Module 4 Powerpoints and assess collinearity until you are satisfied that you have a final set of possible predictors that are “independent,” i.e., not unduly correlated with each other.
Use both stepwise regression approaches and best subsets regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable).
Based on the stepwise modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the forward selection modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the backward elimination modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the adjusted criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Minitab’s “predicted” criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the smallest criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Mallows’ criterion determine which numerical independent variable or variables should be included in your regression model.
Comment on the consistency of your findings in Step 3D (1)-(7).
Cut and paste the Minitab Express printouts from Step 3D into your report.
Based on Step 3D (along with the principle of parsimony if necessary) select a “best” multiple regression model.
Using the predictor variables from your selected “best” multiple regression model, rerun the multiple regression model in order to assess its assumptions.
Look at the set of residual plots, cut and pasted them into the report, and briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 3J.
If the linearity or normality assumptions are problematic state this but continue to Step 3J with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption is violated either transform the dependent variable to log or transform particular independent variables (discuss this with me) and rerun the multiple regression model as in Step 3H.
Assess the significance of the overall fitted model.
Assess the contribution of each predictor variable.
Write the sample multiple regression equation for the “final best” model you have developed.
Interpret the meaning of the intercept and interpret the meaning of all the slopes for your fitted model (but do this in whatever units you used for Y to build the model).
Interpret the meaning of the coefficient of multiple determination .
Very briefly comment on how much has changed from the simple regression model in Step 2D to the “final” multiple regression model in Step 4B.
Interpret the meaning of the standard error of the estimate (in the units you used to build the model).
Select one value for each of your independent variables in their respective relevant ranges:
Predict . (If you used log Y take the antilog so you are back in units of Y).
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variables have the particular values you selected.
(If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
Determine the 95% prediction interval estimate of for an individual occasion when the independent variables have the particular values you selected. (If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
For your “final best” model, as per Module 1, prepare a brief descriptive analysis highlighting the key measures of central tendency, variation, and shape for your dependent variable Y and for each of the predictor variables. Show the individual histograms and boxplots for these variables. If a dummy variable was included as a predictor in your “final best” model show its summary table and bar chart.
Specific instructions for the written team project report follows.Writing the Team ReportEach team report has a title page followed by an Introduction section describing the study “scenario” and mentioning the possible predictor variables and the dependent variable. A section on the Simple Linear Regression Model is then followed by a section on the “final best” Multiple Regression Model. The final section of the report is a Discussion section assessing the gains (if any) by using the “best” multiple regression model in lieu of the simple linear regression model. All discussed Minitab Express printouts should be “cut and pasted” into the report. These should be placed either in the body of the report or in an Appendix to the report. If the latter approach is taken, be sure to number and reference these printouts when discussing them in the body of the report. ** you should do it withe Minitab Expiress(((The INFO 561 course will be using Minitab Express, the educational version of a professional statistical software package that you can rent for $30 (for six months) at the website www.OntheHub.com Minitab Express works on all PCs and also on a Mac.Come to the first class session with Minitab Express loaded on your PC or Mac))my project is about BOUND FUNDS STUDYthe file is with attachment thanks.
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly.
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly. A. $4,7 ...
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly.
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly. A. $4,722.74 B. $3,526.25 C. $4,719.22 D. $3,771.54
FPX 5008 Capella Interpreting Graphical Representations of Data Walmart PPT
You have been invited to be one of many presenters at a departmental
meeting that employees of all levels will attend. Y ...
FPX 5008 Capella Interpreting Graphical Representations of Data Walmart PPT
You have been invited to be one of many presenters at a departmental
meeting that employees of all levels will attend. You have been allotted
5–8 minutes, and the purpose of your speech is to explain the two
charts or tables that your analyst has given you.Instructions
Select two graphical representations of data, such as pie charts, bar charts, scatter plots and trend lines, or tables.
You may use published articles, annual report graphics from publicly traded companies, or any published business report.
You may find your own article that meets the criteria.
Identify the business context, such as an online store, a
brick-and-mortar business, year-end review, product kickoff, recently
merged or new IPO company, or a family-owned business.
This company background information should help explain why the data is relevant.
Interpret your chosen data representation in the context of the
business situation. The following are typical questions an analyst would
use to interpret the data:
What is being measured (the variables)?
What are the relationships among the variables?
What are the trends in the data?
How can the data be applied in the business context?
Create an effective 6-slide PowerPoint presentation that could be presented at a departmental meeting.
An effective PowerPoint presentation for this purpose typically includes:
1 title slide, APA formatted.
1 introduction slide explaining the business context.
1 slide for each of the two graphics in your report.
You may insert or paste the charts and include an appropriate citation (2 total slides for this portion of the presentation).
Explain the meaning of each graphical data representation.
1 conclusion slide in which you explain how the data may
affect the business context or how each graph may be applied in your
business context to inform decision making.
1 slide with APA-formatted references, including the source of each graph.
Prepare a short speech that presents your analysis so that it is relevant to people of all levels of the company.
1 page
Guido Fubini- a famous mathematician.
Guido Fubini, A famous mathematician, was born January 19th 1987 in Venice, Italy. His father, Lazzaro Fubini, was a mathe ...
Guido Fubini- a famous mathematician.
Guido Fubini, A famous mathematician, was born January 19th 1987 in Venice, Italy. His father, Lazzaro Fubini, was a mathematics teacher so he came from a mathematical background. Guido was influenced by his father towards mathematics when he was young. He attended secondary school in Venice where he showed that he was brilliant in mathematics.
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Most Popular Content
7 pages
Data Analysis For Global Managers
The following are the issues that are clearly depicted from the case. The inability of patients to pay for the medical car ...
Data Analysis For Global Managers
The following are the issues that are clearly depicted from the case. The inability of patients to pay for the medical care. Most of the patients are ...
STA 3215 RC Inferential Statistics Analytics Hypothesis Tests Excel Worksheet
CompetencyGiven a real-life application, develop a hypothesis test for a population parameter and its interpretation.Instr ...
STA 3215 RC Inferential Statistics Analytics Hypothesis Tests Excel Worksheet
CompetencyGiven a real-life application, develop a hypothesis test for a population parameter and its interpretation.InstructionsScenario (information repeated for deliverable 01, 03, and 04)A major client of your company is interested in the salary distributions of jobs in the state of Minnesota that range from $30,000 to $200,000 per year. As a Business Analyst, your boss asks you to research and analyze the salary distributions. You are given a spreadsheet that contains the following information:A listing of the jobs by titleThe salary (in dollars) for each jobIn prior engagements, you have already explained to your client about the basic statistics and discussed the importance of constructing confidence intervals for the population mean. Your client says that he remembers a little bit about hypothesis testing, but he is a little fuzzy. He asks you to give him the full explanation of all steps in a hypothesis testing and wants your conclusion about a claim that the average salary for all jobs in the state of Minnesota is less than $75,000.Background information on the DataThe data set in the spreadsheet consists of 364 records that you will be analyzing from the Bureau of Labor Statistics. The data set contains a listing of several jobs titles with yearly salaries ranging from approximately $30,000 to $200,000 for the state of Minnesota.What to SubmitYour boss wants you to submit the spreadsheet with the completed calculations. Your research and analysis should be present within the answers provided on the worksheet. Grading RubricFFCBA01234Not SubmittedNo PassCompetenceProficiencyMasteryNot SubmittedDid not correctly solve a majority of the problems or at least one problem left blank.Correctly solved a majority of the problems.Correctly solved almost all the problems.All problems are solved correctly.Not SubmittedVery few steps are provided to explain how to solve the problem OR the steps provided have several errors.Fairly complete and detailed steps are provided to explain how to solve the problem OR the steps provided have some errors.Mostly complete and detailed steps are provided to explain how to solve the problem.Complete and detailed steps are provided to explain how to solve the problem.Not SubmittedExplanations generally lack a basic understanding of the statistical concepts or lack of proper terminology.Explanations demonstrate a basic understanding of most of the statistical concepts and terminology, but some explanations may be incomplete or incorrect.Explanations demonstrate a proficient understanding of most of the statistical concepts and terminology, but with small errors.Explanations demonstrate a mastery of understanding of the statistical concepts and terminology.Not SubmittedThe majority of variables, equations, and expressions are not properly formatted.The majority of variables, equations, and expressions are properly formatted.Almost all variables, equations, and expressions are properly formatted.All variables, equations, and expressions are properly formatted.
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory � ...
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory “scenario” of just two to three sentences that describes the data file for your project and why you (the ?????? Corporation/Group) are building a regression model to predict based on the set of possible independent variables
As you learned in class in Week 2, first develop a simple linear regression model using one of the above predictors of .
Cut and paste into your report the scatter plot and the Minitab Express printout for this simple linear regression model.
Write the sample regression equation.
Interpret the meaning of the intercept and slope for your fitted model.
Interpret the meaning of the coefficient of determination .
Interpret the meaning of the standard error of the estimate .
Obtain the residual plots and cut and paste them into the report. Briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 2G.
If the linearity or normality assumptions are problematic state this but continue to Step 2G with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption appears to be seriously violated contact me.
Comment on the statistical significance of your fitted model. (Note: Every team should have a fitted model that is statistically significant so contact me immediately if this is not so).
Select a value for your independent variable in its relevant range:
Predict .
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variable has the particular value you selected.
Determine the 95% prediction interval estimate of for an individual occasion when the independent variable has the particular value you selected.
As you learned in class in Weeks 3 and 4, you will be using the set of potentially meaningful numerical independent variables and one selected “two-category” dummy variable in your study to develop a “best” multiple regression model for predicting your numerical dependent variable . Follow the “9-step modeling process” described in the Powerpoints at the end of Module 4.
Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing their respective scatter plots and paste these into your report.
Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable.
Then review Slides 6 through 16 of the Module 4 Powerpoints and assess collinearity until you are satisfied that you have a final set of possible predictors that are “independent,” i.e., not unduly correlated with each other.
Use both stepwise regression approaches and best subsets regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable).
Based on the stepwise modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the forward selection modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the backward elimination modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the adjusted criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Minitab’s “predicted” criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the smallest criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Mallows’ criterion determine which numerical independent variable or variables should be included in your regression model.
Comment on the consistency of your findings in Step 3D (1)-(7).
Cut and paste the Minitab Express printouts from Step 3D into your report.
Based on Step 3D (along with the principle of parsimony if necessary) select a “best” multiple regression model.
Using the predictor variables from your selected “best” multiple regression model, rerun the multiple regression model in order to assess its assumptions.
Look at the set of residual plots, cut and pasted them into the report, and briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 3J.
If the linearity or normality assumptions are problematic state this but continue to Step 3J with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption is violated either transform the dependent variable to log or transform particular independent variables (discuss this with me) and rerun the multiple regression model as in Step 3H.
Assess the significance of the overall fitted model.
Assess the contribution of each predictor variable.
Write the sample multiple regression equation for the “final best” model you have developed.
Interpret the meaning of the intercept and interpret the meaning of all the slopes for your fitted model (but do this in whatever units you used for Y to build the model).
Interpret the meaning of the coefficient of multiple determination .
Very briefly comment on how much has changed from the simple regression model in Step 2D to the “final” multiple regression model in Step 4B.
Interpret the meaning of the standard error of the estimate (in the units you used to build the model).
Select one value for each of your independent variables in their respective relevant ranges:
Predict . (If you used log Y take the antilog so you are back in units of Y).
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variables have the particular values you selected.
(If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
Determine the 95% prediction interval estimate of for an individual occasion when the independent variables have the particular values you selected. (If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
For your “final best” model, as per Module 1, prepare a brief descriptive analysis highlighting the key measures of central tendency, variation, and shape for your dependent variable Y and for each of the predictor variables. Show the individual histograms and boxplots for these variables. If a dummy variable was included as a predictor in your “final best” model show its summary table and bar chart.
Specific instructions for the written team project report follows.Writing the Team ReportEach team report has a title page followed by an Introduction section describing the study “scenario” and mentioning the possible predictor variables and the dependent variable. A section on the Simple Linear Regression Model is then followed by a section on the “final best” Multiple Regression Model. The final section of the report is a Discussion section assessing the gains (if any) by using the “best” multiple regression model in lieu of the simple linear regression model. All discussed Minitab Express printouts should be “cut and pasted” into the report. These should be placed either in the body of the report or in an Appendix to the report. If the latter approach is taken, be sure to number and reference these printouts when discussing them in the body of the report. ** you should do it withe Minitab Expiress(((The INFO 561 course will be using Minitab Express, the educational version of a professional statistical software package that you can rent for $30 (for six months) at the website www.OntheHub.com Minitab Express works on all PCs and also on a Mac.Come to the first class session with Minitab Express loaded on your PC or Mac))my project is about BOUND FUNDS STUDYthe file is with attachment thanks.
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly.
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly. A. $4,7 ...
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly.
Find the balance in an account if $3500 is invested at an annual rate of 3% for 10 years and compounded quarterly. A. $4,722.74 B. $3,526.25 C. $4,719.22 D. $3,771.54
FPX 5008 Capella Interpreting Graphical Representations of Data Walmart PPT
You have been invited to be one of many presenters at a departmental
meeting that employees of all levels will attend. Y ...
FPX 5008 Capella Interpreting Graphical Representations of Data Walmart PPT
You have been invited to be one of many presenters at a departmental
meeting that employees of all levels will attend. You have been allotted
5–8 minutes, and the purpose of your speech is to explain the two
charts or tables that your analyst has given you.Instructions
Select two graphical representations of data, such as pie charts, bar charts, scatter plots and trend lines, or tables.
You may use published articles, annual report graphics from publicly traded companies, or any published business report.
You may find your own article that meets the criteria.
Identify the business context, such as an online store, a
brick-and-mortar business, year-end review, product kickoff, recently
merged or new IPO company, or a family-owned business.
This company background information should help explain why the data is relevant.
Interpret your chosen data representation in the context of the
business situation. The following are typical questions an analyst would
use to interpret the data:
What is being measured (the variables)?
What are the relationships among the variables?
What are the trends in the data?
How can the data be applied in the business context?
Create an effective 6-slide PowerPoint presentation that could be presented at a departmental meeting.
An effective PowerPoint presentation for this purpose typically includes:
1 title slide, APA formatted.
1 introduction slide explaining the business context.
1 slide for each of the two graphics in your report.
You may insert or paste the charts and include an appropriate citation (2 total slides for this portion of the presentation).
Explain the meaning of each graphical data representation.
1 conclusion slide in which you explain how the data may
affect the business context or how each graph may be applied in your
business context to inform decision making.
1 slide with APA-formatted references, including the source of each graph.
Prepare a short speech that presents your analysis so that it is relevant to people of all levels of the company.
1 page
Guido Fubini- a famous mathematician.
Guido Fubini, A famous mathematician, was born January 19th 1987 in Venice, Italy. His father, Lazzaro Fubini, was a mathe ...
Guido Fubini- a famous mathematician.
Guido Fubini, A famous mathematician, was born January 19th 1987 in Venice, Italy. His father, Lazzaro Fubini, was a mathematics teacher so he came from a mathematical background. Guido was influenced by his father towards mathematics when he was young. He attended secondary school in Venice where he showed that he was brilliant in mathematics.
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