Description
let S be a set consisting of all k-th roots of all natural numbers (k greater than and equal to 2)
S:= { n^(1/k) , k is natural number , k greater than and equal to 2, n is natural number}
Prove that S is countable.
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Dedan Kimathi University of Technology Actuarial Science R Programming Task
need the three questions to be completed. first two questions should be written on a piece of paper including the explanat ...
Dedan Kimathi University of Technology Actuarial Science R Programming Task
need the three questions to be completed. first two questions should be written on a piece of paper including the explanation part for question 3. deliver the r script also.
SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. Y ...
SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. You have learned how to take samples from populations and use them to produce statistics. For two quantitative variables, businesses can use scatterplots and the correlation coefficient to explore a potential linear relationship. Furthermore, they can quantify the relationship in a regression equation.PromptThis assignment picks up where the Module Two assignment left off and will use components of that assignment as a foundation.You have submitted your initial analysis to the sales team at D.M. Pan Real Estate Company. You will continue your analysis of the provided Real Estate County Data spreadsheet using your selected region to complete your analysis. You may refer back to the initial report you developed in the Module Two Assignment Template to continue the work. This document and the National Statistics and Graphs spreadsheet will support your work on the assignment.Note: In the report you prepare for the sales team, the dependent, or response, variable (y) should be the median listing price and the independent, or predictor, variable (x) should be the median square feet.Using the Module Three Assignment Template, specifically address the following:Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment.Determine r: Determine r and what it means. (What is the relationship between the variables?)Determine the strength of the correlation (weak, moderate, or strong).Discuss how you determine the direction of the association between the two variables.Is there a positive or negative association?What do you see as the direction of the correlation?Examine the Slope and Intercepts: Examine the slope b1{"version":"1.1","math":"b1"} and intercept b0{"version":"1.1","math":"b0"}.Draw conclusions from the slope and intercept in the context of this problem.Does the intercept make sense based on your observation of the line of best fit?Determine the value of the land only.Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens when x=0, which is the y-intercept.Determine the R-squared Coefficient: Determine the R-squared value.Discuss what R-squared means in the context of this analysis.Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions:Is the square footage for homes in your selected region different than for homes overall in the United States?For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)?Use the regression equation to estimate how much you would list your house for if it was 1,200 square feet.What square footage range would the graph be best used for?Guidelines for SubmissionSubmit your completed Module Three Assignment Template as a Word document that includes your response and supCompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques to address research problemsPerform regression analysis to address an authentic problemOverviewThe purpose of this project is to have you complete all of the steps of a real-world linear regression research project starting with developing a research question, then completing a comprehensive statistical analysis, and ending with summarizing your research conclusions.ScenarioYou have been hired by the D. M. Pan National Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the median housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and graphs provided.DirectionsUsing the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate County Data spreadsheet (both found in the Supporting Materials section) for your statistical analysis.Note: Present your data in a clearly labeled table and using clearly labeled graphs.Specifically, include the following in your report:IntroductionDescribe the report: Give a brief description of the purpose of your report.Define the question your report is trying to answer.Explain when using linear regression is most appropriate.When using linear regression, what would you expect the scatterplot to look like?Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.Data CollectionSampling the data: Select a random sample of 50 counties.Identify your response and predictor variables.Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.Data AnalysisHistogram: For your two variables, create histograms.Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.Interpret the graphs and statistics:Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?Develop Your Regression ModelScatterplot: Provide a graph of the scatterplot of the data with a line of best fit.Explain if a regression model is appropriate to develop based on your scatterplot.Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.Identify any possible outliers or influential points and discuss their effect on the correlation.Discuss keeping or removing outlier data points and what impact your decision would have on your model.Find r: Find the correlation coefficient (r).Explain how the r value you calculated supports what you noticed in your scatterplot.Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.Interpret regression equation: Interpret the slope and intercept in context.Strength of the equation: Provide and interpret R-squared.Determine the strength of the linear regression equation you developed.Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.ConclusionsSummarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.Did you see the results you expected, or was anything different from your expectations or experiences?What changes could support different results, or help to solve a different problem?Provide at least one question that would be interesting for follow-up research.What to SubmitTo complete this project, you must submit the following:Project One Template: Use this template to structure your report, and submit the finished version as a Word document.Supporting MaterialsThe following resources may help support your work on the project:Document: National Statistics and GraphsUse this data for input in your project report.Spreadsheet: Real Estate County DataUse this data for input in your project report.porting charts/graphs. The following resources support your work on this assignment:
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you c ...
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you can ?
algebra part 1
Question 1 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 1 options:
Question 2 (5 points) ...
algebra part 1
Question 1 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 1 options:
Question 2 (5 points)
Find the first five terms of the sequence in which a1 = –10 and an = 4an – 1 + 7, if n ? 2.
Question 2 options:
–10, –33, –125, –493, –1965
–33, –125, –493, –1965, –7853
11, 15, 19, 23, 27
47, –10, –33, –125, –493
Question 3 (5 points)
Simplify the expression.
· Question 3 options:
255
Question 4 (5 points)
Simplify the expression.
10 + 7Question 4 options:
70
3
17
Question 5 (5 points)
Solve the equation of exponential decay.
The population of a city was 572,000 in 2010. In the 2000 census the population was 607,000. What was the rate of decrease of the population from one census to the next? Round to the nearest percent.Question 5 options:
8%
6%
3%
1%
Question 6 (5 points)
Simplify: 811/4.
Question 6 options:
3
4
12
Question 7 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 7 options:
Question 8 (5 points)
Write an equation for the nth term of the sequence 4, 12, 36, 108, ...
Question 8 options:
an = 4(3)n
an = 3(4)n
an = 4(3)n – 1
an = 3(4)n – 1
Question 9 (5 points)
Simplify the expression.
9(8 + 9)Question 9 options:
216 +
216 + 81
27 + 81
216 + 9
Question 10 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 10 options:
Question 11 (5 points)
Simplify the expression.
10 + 5 – 8 – 3 Question 11 options:
2 + 2
2 + 2
80 – 15
18 + 8
Question 12 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 12 options:
Question 13 (5 points)
Simplify: .
Question 13 options:
160
3
2
Question 14 (5 points)
Solve the equation of exponential decay.
The population of a city is expected to be 440,000 in 2020. This is a decline of 12% from 2010 to 2020. Assuming this continued what would the population of the city be in 2040? Round to the nearest ten thousand.Question 14 options:
340,000
300,000
390,000
410,000
Question 15 (5 points)
Simplify the expression.
Question 15 options:
Question 16 (5 points)
Find the first five terms of the sequence in which a1 = 6 and an = –3an – 1 – 12, if n ? 2.
Question 16 options:
–30, 78, –246, 726, –2190
6, –30, 78, –246, 726
6, 6, –30, 78, –246
–15, –18, –21, –24, –27
Question 17 (5 points)
Determine whether the data in the table display exponential behavior. Explain why or why not.
x
3
2
1
–1
y
8
2
0.5
0.125
Question 17 options:
No; the domain values are at regular intervals and the range values have a common factor 0.25.
No; the domain values are not at regular intervals although the range values have a common factor.
Yes; the domain values are at regular intervals and the range values have a common factor 4.
Yes; the domain values are at regular intervals and the range values have a common factor 0.25.
Question 18 (5 points)
Solve the equation of exponential decay.
A car sells for $25,000. If the rate of depreciation is 15%, what is the value of the car after 7 years? Round to the nearest hundred.Question 18 options:
$8,000
$9,400
$7,400
$9,800
Question 19 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 19 options:
Question 20 (5 points)
Simplify. Assume that no denominator is equal to zero.
(a5b3)(a3b5)Question 20 options:
a15b15
a15b8
a8b8
a8b15
13 pages
Answers Exam 2
The response to a question has three alternatives: A, B, and C. A sample of 120 responses provides 63 A, 22 B, and 35 C. S ...
Answers Exam 2
The response to a question has three alternatives: A, B, and C. A sample of 120 responses provides 63 A, 22 B, and 35 C. Show the frequency and ...
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you c ...
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you can ?
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Dedan Kimathi University of Technology Actuarial Science R Programming Task
need the three questions to be completed. first two questions should be written on a piece of paper including the explanat ...
Dedan Kimathi University of Technology Actuarial Science R Programming Task
need the three questions to be completed. first two questions should be written on a piece of paper including the explanation part for question 3. deliver the r script also.
SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. Y ...
SNHU Selling Price and Area Analysis for DM Pan National Real Estate Company
OverviewRecall that samples are used to generate a statistic, which businesses use to estimate the population parameter. You have learned how to take samples from populations and use them to produce statistics. For two quantitative variables, businesses can use scatterplots and the correlation coefficient to explore a potential linear relationship. Furthermore, they can quantify the relationship in a regression equation.PromptThis assignment picks up where the Module Two assignment left off and will use components of that assignment as a foundation.You have submitted your initial analysis to the sales team at D.M. Pan Real Estate Company. You will continue your analysis of the provided Real Estate County Data spreadsheet using your selected region to complete your analysis. You may refer back to the initial report you developed in the Module Two Assignment Template to continue the work. This document and the National Statistics and Graphs spreadsheet will support your work on the assignment.Note: In the report you prepare for the sales team, the dependent, or response, variable (y) should be the median listing price and the independent, or predictor, variable (x) should be the median square feet.Using the Module Three Assignment Template, specifically address the following:Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment.Determine r: Determine r and what it means. (What is the relationship between the variables?)Determine the strength of the correlation (weak, moderate, or strong).Discuss how you determine the direction of the association between the two variables.Is there a positive or negative association?What do you see as the direction of the correlation?Examine the Slope and Intercepts: Examine the slope b1{"version":"1.1","math":"b1"} and intercept b0{"version":"1.1","math":"b0"}.Draw conclusions from the slope and intercept in the context of this problem.Does the intercept make sense based on your observation of the line of best fit?Determine the value of the land only.Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens when x=0, which is the y-intercept.Determine the R-squared Coefficient: Determine the R-squared value.Discuss what R-squared means in the context of this analysis.Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions:Is the square footage for homes in your selected region different than for homes overall in the United States?For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)?Use the regression equation to estimate how much you would list your house for if it was 1,200 square feet.What square footage range would the graph be best used for?Guidelines for SubmissionSubmit your completed Module Three Assignment Template as a Word document that includes your response and supCompetenciesIn this project, you will demonstrate your mastery of the following competencies:Apply statistical techniques to address research problemsPerform regression analysis to address an authentic problemOverviewThe purpose of this project is to have you complete all of the steps of a real-world linear regression research project starting with developing a research question, then completing a comprehensive statistical analysis, and ending with summarizing your research conclusions.ScenarioYou have been hired by the D. M. Pan National Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the median housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and graphs provided.DirectionsUsing the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate County Data spreadsheet (both found in the Supporting Materials section) for your statistical analysis.Note: Present your data in a clearly labeled table and using clearly labeled graphs.Specifically, include the following in your report:IntroductionDescribe the report: Give a brief description of the purpose of your report.Define the question your report is trying to answer.Explain when using linear regression is most appropriate.When using linear regression, what would you expect the scatterplot to look like?Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.Data CollectionSampling the data: Select a random sample of 50 counties.Identify your response and predictor variables.Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.Data AnalysisHistogram: For your two variables, create histograms.Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.Interpret the graphs and statistics:Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?Develop Your Regression ModelScatterplot: Provide a graph of the scatterplot of the data with a line of best fit.Explain if a regression model is appropriate to develop based on your scatterplot.Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.Identify any possible outliers or influential points and discuss their effect on the correlation.Discuss keeping or removing outlier data points and what impact your decision would have on your model.Find r: Find the correlation coefficient (r).Explain how the r value you calculated supports what you noticed in your scatterplot.Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.Interpret regression equation: Interpret the slope and intercept in context.Strength of the equation: Provide and interpret R-squared.Determine the strength of the linear regression equation you developed.Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.ConclusionsSummarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.Did you see the results you expected, or was anything different from your expectations or experiences?What changes could support different results, or help to solve a different problem?Provide at least one question that would be interesting for follow-up research.What to SubmitTo complete this project, you must submit the following:Project One Template: Use this template to structure your report, and submit the finished version as a Word document.Supporting MaterialsThe following resources may help support your work on the project:Document: National Statistics and GraphsUse this data for input in your project report.Spreadsheet: Real Estate County DataUse this data for input in your project report.porting charts/graphs. The following resources support your work on this assignment:
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you c ...
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you can ?
algebra part 1
Question 1 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 1 options:
Question 2 (5 points) ...
algebra part 1
Question 1 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 1 options:
Question 2 (5 points)
Find the first five terms of the sequence in which a1 = –10 and an = 4an – 1 + 7, if n ? 2.
Question 2 options:
–10, –33, –125, –493, –1965
–33, –125, –493, –1965, –7853
11, 15, 19, 23, 27
47, –10, –33, –125, –493
Question 3 (5 points)
Simplify the expression.
· Question 3 options:
255
Question 4 (5 points)
Simplify the expression.
10 + 7Question 4 options:
70
3
17
Question 5 (5 points)
Solve the equation of exponential decay.
The population of a city was 572,000 in 2010. In the 2000 census the population was 607,000. What was the rate of decrease of the population from one census to the next? Round to the nearest percent.Question 5 options:
8%
6%
3%
1%
Question 6 (5 points)
Simplify: 811/4.
Question 6 options:
3
4
12
Question 7 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 7 options:
Question 8 (5 points)
Write an equation for the nth term of the sequence 4, 12, 36, 108, ...
Question 8 options:
an = 4(3)n
an = 3(4)n
an = 4(3)n – 1
an = 3(4)n – 1
Question 9 (5 points)
Simplify the expression.
9(8 + 9)Question 9 options:
216 +
216 + 81
27 + 81
216 + 9
Question 10 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 10 options:
Question 11 (5 points)
Simplify the expression.
10 + 5 – 8 – 3 Question 11 options:
2 + 2
2 + 2
80 – 15
18 + 8
Question 12 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 12 options:
Question 13 (5 points)
Simplify: .
Question 13 options:
160
3
2
Question 14 (5 points)
Solve the equation of exponential decay.
The population of a city is expected to be 440,000 in 2020. This is a decline of 12% from 2010 to 2020. Assuming this continued what would the population of the city be in 2040? Round to the nearest ten thousand.Question 14 options:
340,000
300,000
390,000
410,000
Question 15 (5 points)
Simplify the expression.
Question 15 options:
Question 16 (5 points)
Find the first five terms of the sequence in which a1 = 6 and an = –3an – 1 – 12, if n ? 2.
Question 16 options:
–30, 78, –246, 726, –2190
6, –30, 78, –246, 726
6, 6, –30, 78, –246
–15, –18, –21, –24, –27
Question 17 (5 points)
Determine whether the data in the table display exponential behavior. Explain why or why not.
x
3
2
1
–1
y
8
2
0.5
0.125
Question 17 options:
No; the domain values are at regular intervals and the range values have a common factor 0.25.
No; the domain values are not at regular intervals although the range values have a common factor.
Yes; the domain values are at regular intervals and the range values have a common factor 4.
Yes; the domain values are at regular intervals and the range values have a common factor 0.25.
Question 18 (5 points)
Solve the equation of exponential decay.
A car sells for $25,000. If the rate of depreciation is 15%, what is the value of the car after 7 years? Round to the nearest hundred.Question 18 options:
$8,000
$9,400
$7,400
$9,800
Question 19 (5 points)
Simplify. Assume that no denominator is equal to zero.
Question 19 options:
Question 20 (5 points)
Simplify. Assume that no denominator is equal to zero.
(a5b3)(a3b5)Question 20 options:
a15b15
a15b8
a8b8
a8b15
13 pages
Answers Exam 2
The response to a question has three alternatives: A, B, and C. A sample of 120 responses provides 63 A, 22 B, and 35 C. S ...
Answers Exam 2
The response to a question has three alternatives: A, B, and C. A sample of 120 responses provides 63 A, 22 B, and 35 C. Show the frequency and ...
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you c ...
Ashford University Ordinary Least Squares Simple Regression Questions
let me know if you have any question please do not forget to explain to me in details how did you get the answers as you can ?
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