Probability............
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Geluneq
Mathematics
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given ten students,six,of which are females, if two students are selected at random,without replacement,what is the probability that both students are female?
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RSCH-6210I-5/RSCH-6210Y-5/RSCH-6200I-5/RSCH-6200Y-5-Quant Reasoning & Analysis2019 Fall Quarter 08/26-11/17-PT27
From the General Social Survey dataset found in this week’s Learning Resources, use the SPSS software and choose one con ...
RSCH-6210I-5/RSCH-6210Y-5/RSCH-6200I-5/RSCH-6200Y-5-Quant Reasoning & Analysis2019 Fall Quarter 08/26-11/17-PT27
From the General Social Survey dataset found in this week’s Learning Resources, use the SPSS software and choose one continuous and one categorical variable Note: this dataset will be different from your Assignment dataset).
As you review, consider the implications for positive social change based on the results of your data.
By Day 3
Post, present, and report a descriptive analysis for your variables, specifically noting the following:
For your continuous variable:
Report the mean, median, and mode.
What might be the better measure for central tendency? (i.e., mean, median, or mode) and why?
Report the standard deviation.
How variable are the data?
How would you describe this data?
What sort of research question would this variable help answer that might inform social change?
Post the following information for your categorical variable:
A frequency distribution.
An appropriate measure of variation.
How variable are the data?
How would you describe this data?
What sort of research question would this variable help answer that might inform social change?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
11 pages
MATGAM02 Rasmussen Module 4 Pure Strategy Equilibrium Quiz
Calculate the pure strategy equilibrium of the game shown below using Minimax method Calculate the pure strategy equilibri ...
MATGAM02 Rasmussen Module 4 Pure Strategy Equilibrium Quiz
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The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of college, statistics homework help
The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of colleg ...
The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of college, statistics homework help
The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of college. The weights of 100 freshmen at a certain University were observed in August and then re-examined in May. The claim that the mean difference is equal to 15 pounds is tested at a 0.05 significance level. The result is "fail to reject the null hypothesis".What do the results suggest?What factors could have affected the results?How can these results be used to make changes on campus
MBA 6300 case study, quantitative analysis
There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), nu ...
MBA 6300 case study, quantitative analysis
There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), number of bedrooms, and number of bathrooms.The data in the Case Study No. 2.xlsx file pertains to a random sample of houses located in a particular geographic area.
Develop the following simple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area.
Sale price based upon number of bedrooms.
Sale price based upon number of bathrooms.
Develop the following multiple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area and number of bedrooms.
Sale price based upon square feet of living area and number of bathrooms.
Sale price based upon number of bedrooms and number of bathrooms.
Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms.
Discuss the joint statistical significance of each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Discuss the individual statistical significance of the coefficient for each independent variable for each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Compare any of the preceding simple and multiple linear regression models that were found to be jointly and individually statistically significant at a 90% level of confidence and select the preferred regression model. Explain your selection using the appropriate regression statistics.
Interpret the coefficient for each independent variable (or variables) associated with your selected preferred regression model.
Using the preferred regression model, predict the sale price of a house with the following values for the independent variables: 3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms.(Hint: You should only use the values for those independent variables that are specifically associated with your selected preferred regression model.)
Prepare a single Microsoft Excel file using a separate worksheet for each question
Walden University Week 5 Regression Model in Healthcare Management Paper
THE ANSWER MUST BE ORIGINAL AND NOT FROM ANOTHER STUDENT'S PAPER
Use the sources provided in the details to answer the que ...
Walden University Week 5 Regression Model in Healthcare Management Paper
THE ANSWER MUST BE ORIGINAL AND NOT FROM ANOTHER STUDENT'S PAPER
Use the sources provided in the details to answer the question and use this scenario to answer the question:
Reducing the Readmission of Patients
Discussion: Building Regression Models in Health Care
Finding evidence-based relationships among variables is an important tool for any healthcare administration leader. On a daily basis, healthcare administration leaders may want to see what variables are correlated so that they can implement quality improvement.
This week, you think of scenarios where building and interpreting regression models would be useful for healthcare administration leaders. You might consider building off of your Week 4 Discussion.
For example, Jenna, a healthcare administration leader, determined last week that patient satisfaction scores had fallen from the mean of 87. She wants to know why. She believes that it may have something to do with patient waiting time and time spent with the doctor. Thus, her dependent variable (y) is patient satisfaction and the independent variables are waiting time (x1) and time spent with the doctor (x2). She can evaluate the relationship between these two variables using correlation; bivariate scatterplots for y vs. x1 and y vs. x2; and regression techniques.
For this Discussion, think about a healthcare scenario where multiple regression might be useful in your organization or one with which you are familiar. Consider what your dependent and independent variables might be for conducting a multiple regression analysis. Build a small example, and run the regression analysis.
By Day 3
Post a description of the dependent and independent variables you will use for your multiple regression analysis, and then explain your regression model in terms of your dependent and independent variables. Explain how you might measure your variables. Be specific and provide examples.
Required Readings
Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.
Chapter 10, "Regression Analysis: Estimating Relationships"
Chapter 11, "Regression Analysis: Statistical Inference"
Fulton, L., Lasdon, L. S., & McDaniel, R. R. (2007). Cost drivers and resource allocation in military health care systems. Military Medicine, 172(3), 244–249.
Required Media
Marin, M., & Hamadani, L. (2013). Checking linear regression assumptions in R [Video file]. Retrieved from https://youtu.be/eTZ4VUZHzxw
Dunn, C. (2020). A fast and automatic variable selection for multiple linear regression [Video file]. Retrieved from https://www.youtube.com/watch?v=GyQsSzX3O_A
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Most Popular Content
RSCH-6210I-5/RSCH-6210Y-5/RSCH-6200I-5/RSCH-6200Y-5-Quant Reasoning & Analysis2019 Fall Quarter 08/26-11/17-PT27
From the General Social Survey dataset found in this week’s Learning Resources, use the SPSS software and choose one con ...
RSCH-6210I-5/RSCH-6210Y-5/RSCH-6200I-5/RSCH-6200Y-5-Quant Reasoning & Analysis2019 Fall Quarter 08/26-11/17-PT27
From the General Social Survey dataset found in this week’s Learning Resources, use the SPSS software and choose one continuous and one categorical variable Note: this dataset will be different from your Assignment dataset).
As you review, consider the implications for positive social change based on the results of your data.
By Day 3
Post, present, and report a descriptive analysis for your variables, specifically noting the following:
For your continuous variable:
Report the mean, median, and mode.
What might be the better measure for central tendency? (i.e., mean, median, or mode) and why?
Report the standard deviation.
How variable are the data?
How would you describe this data?
What sort of research question would this variable help answer that might inform social change?
Post the following information for your categorical variable:
A frequency distribution.
An appropriate measure of variation.
How variable are the data?
How would you describe this data?
What sort of research question would this variable help answer that might inform social change?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
11 pages
MATGAM02 Rasmussen Module 4 Pure Strategy Equilibrium Quiz
Calculate the pure strategy equilibrium of the game shown below using Minimax method Calculate the pure strategy equilibri ...
MATGAM02 Rasmussen Module 4 Pure Strategy Equilibrium Quiz
Calculate the pure strategy equilibrium of the game shown below using Minimax method Calculate the pure strategy equilibrium of the game shown below ...
The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of college, statistics homework help
The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of colleg ...
The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of college, statistics homework help
The freshman 15 refers to the urban legend that students gain an average of 15 pounds during their freshman year of college. The weights of 100 freshmen at a certain University were observed in August and then re-examined in May. The claim that the mean difference is equal to 15 pounds is tested at a 0.05 significance level. The result is "fail to reject the null hypothesis".What do the results suggest?What factors could have affected the results?How can these results be used to make changes on campus
MBA 6300 case study, quantitative analysis
There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), nu ...
MBA 6300 case study, quantitative analysis
There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), number of bedrooms, and number of bathrooms.The data in the Case Study No. 2.xlsx file pertains to a random sample of houses located in a particular geographic area.
Develop the following simple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area.
Sale price based upon number of bedrooms.
Sale price based upon number of bathrooms.
Develop the following multiple linear regression models to predict the sale price of a house based upon a 90% level of confidence.Write the regression equation for each model.
Sale price based upon square feet of living area and number of bedrooms.
Sale price based upon square feet of living area and number of bathrooms.
Sale price based upon number of bedrooms and number of bathrooms.
Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms.
Discuss the joint statistical significance of each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Discuss the individual statistical significance of the coefficient for each independent variable for each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.
Compare any of the preceding simple and multiple linear regression models that were found to be jointly and individually statistically significant at a 90% level of confidence and select the preferred regression model. Explain your selection using the appropriate regression statistics.
Interpret the coefficient for each independent variable (or variables) associated with your selected preferred regression model.
Using the preferred regression model, predict the sale price of a house with the following values for the independent variables: 3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms.(Hint: You should only use the values for those independent variables that are specifically associated with your selected preferred regression model.)
Prepare a single Microsoft Excel file using a separate worksheet for each question
Walden University Week 5 Regression Model in Healthcare Management Paper
THE ANSWER MUST BE ORIGINAL AND NOT FROM ANOTHER STUDENT'S PAPER
Use the sources provided in the details to answer the que ...
Walden University Week 5 Regression Model in Healthcare Management Paper
THE ANSWER MUST BE ORIGINAL AND NOT FROM ANOTHER STUDENT'S PAPER
Use the sources provided in the details to answer the question and use this scenario to answer the question:
Reducing the Readmission of Patients
Discussion: Building Regression Models in Health Care
Finding evidence-based relationships among variables is an important tool for any healthcare administration leader. On a daily basis, healthcare administration leaders may want to see what variables are correlated so that they can implement quality improvement.
This week, you think of scenarios where building and interpreting regression models would be useful for healthcare administration leaders. You might consider building off of your Week 4 Discussion.
For example, Jenna, a healthcare administration leader, determined last week that patient satisfaction scores had fallen from the mean of 87. She wants to know why. She believes that it may have something to do with patient waiting time and time spent with the doctor. Thus, her dependent variable (y) is patient satisfaction and the independent variables are waiting time (x1) and time spent with the doctor (x2). She can evaluate the relationship between these two variables using correlation; bivariate scatterplots for y vs. x1 and y vs. x2; and regression techniques.
For this Discussion, think about a healthcare scenario where multiple regression might be useful in your organization or one with which you are familiar. Consider what your dependent and independent variables might be for conducting a multiple regression analysis. Build a small example, and run the regression analysis.
By Day 3
Post a description of the dependent and independent variables you will use for your multiple regression analysis, and then explain your regression model in terms of your dependent and independent variables. Explain how you might measure your variables. Be specific and provide examples.
Required Readings
Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.
Chapter 10, "Regression Analysis: Estimating Relationships"
Chapter 11, "Regression Analysis: Statistical Inference"
Fulton, L., Lasdon, L. S., & McDaniel, R. R. (2007). Cost drivers and resource allocation in military health care systems. Military Medicine, 172(3), 244–249.
Required Media
Marin, M., & Hamadani, L. (2013). Checking linear regression assumptions in R [Video file]. Retrieved from https://youtu.be/eTZ4VUZHzxw
Dunn, C. (2020). A fast and automatic variable selection for multiple linear regression [Video file]. Retrieved from https://www.youtube.com/watch?v=GyQsSzX3O_A
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