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y=1/3+4/3
y= -1/2x + 11/2
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Explanation & Answer
y=1/3+4/3
y= -1/2x + 11/2
Graph is here
As we can see from graph , the solution is {7.67, 1.67}
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STAT Capella University Normal and Standard Distribution Practice Questions
You are asked to conduct a study of shelters for w ...
STAT Capella University Normal and Standard Distribution Practice Questions
You are asked to conduct a study of shelters for women who have experienced domestic violence in order to determine the necessary capacity in your city to provide housing for most of these women. After recording data for a whole year, you find that the mean number of women in shelters each night is 250, with a standard deviation of 75. Fortunately, the distribution of the number of women in shelters each night is normal, so you can answer the following questions posed by the city council.
Question 1
2 Points
If the city’s shelters have a capacity of 350, will that be enough places for abused women on 95% of all nights?
Yes
No
Question 2
2 Points
What number of shelter openings will be needed?
250
370
75
374
Question 3
2 Points
The current capacity is only 220 openings because some of the shelters have been closed. What is the percentage of nights that the number of abused women seeking shelter will exceed current capacity?
62.21%
65.54%
34.46%
40%
A criminologist developed a test to measure recidivism, where low scores indicated a lower probability of repeating the undesirable behavior. The test is normed so that it has a mean of 140 and a standard deviation of 40.
Question 4
2 Points
What is the percentile rank of a score of 172?
78.81st percentile
80th percentile
40th percentile
28.81th percentile
Question 5
2 Points
What is the z-score for a test score of 200?
-1.50
0.5
1
1.50
Question 6
2 Points
What percentage of scores falls between 100 and 160?
28.81%
53.28%
19.15%
34.13%
Question 7
2 Points
What proportion of respondents should score above 190?
0.5
1
1.50
1.25
Question 8
1.5 Points
Suppose an individual is in the 67th percentile in this test. What is his or her corresponding recidivism score?
155.9
140
157.6
112.9
According to National Collegiate Athletic Association (NCAA) data, the means and standard deviations of eligibility and retention rates (based on a 1,000-point scale) for the 2013–2014 academic year are presented, along with the fictional scores for two basketball teams, A and B. Assume that rates are normally distributed.Normal Distribution Practice data
Question 9
1.5 Points
On which criterion (eligibility or retention) did Team A do better than Team B? Calculate appropriate statistics to answer this question.
Team A has better retention and eligibility than Team B.
Team B has better retention and eligibility than Team A.
Team B has better retention but Team A has better eligibility.
Team A has better retention but Team B has better eligibility.
Question 10
1.5 Points
What proportion of the teams have retention rates below Team B?
43.25
4.325
0.4325
0.04325
Question 11
1.5 Points
What is the percentile rank of Team A’s eligibility rate?
43.25th percentile
36.54th percentile
35.94th percentile
17th percentile
6 pages
Class Project
Below is grade of a sample of 36 students of Basic Statistics course offered during 2014 at Rutgers University. 1) Make a ...
Class Project
Below is grade of a sample of 36 students of Basic Statistics course offered during 2014 at Rutgers University. 1) Make a new variable called “New ...
SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techni ...
SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techniques to address research problems
Perform regression analysis to address an authentic problem
Overview
The 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.
Scenario
You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict 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 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.
Directions
Using 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 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:
Introduction
Describe 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 Collection
Sampling the data: Select a random sample of 50 houses.
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 Analysis
Histogram: 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 Model
Scatterplot: 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.
Conclusions
Summarize 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.
I need it by next Sunday morning.
Choose two of the following three questions and post your answer:1. In your own words, what is a nonparametric test? Wha ...
I need it by next Sunday morning.
Choose two of the following three questions and post your answer:1. In your own words, what is a nonparametric test? What is a parametric test (Section 13-2)?2. In your own words, identify an advantage of using rank correlation instead of linear correlation (Section 13-6).3. In your own words, what is the difference between a nonparametric test and a distribution-free test (Section 13-7)?
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Most Popular Content
STAT Capella University Normal and Standard Distribution Practice Questions
You are asked to conduct a study of shelters for w ...
STAT Capella University Normal and Standard Distribution Practice Questions
You are asked to conduct a study of shelters for women who have experienced domestic violence in order to determine the necessary capacity in your city to provide housing for most of these women. After recording data for a whole year, you find that the mean number of women in shelters each night is 250, with a standard deviation of 75. Fortunately, the distribution of the number of women in shelters each night is normal, so you can answer the following questions posed by the city council.
Question 1
2 Points
If the city’s shelters have a capacity of 350, will that be enough places for abused women on 95% of all nights?
Yes
No
Question 2
2 Points
What number of shelter openings will be needed?
250
370
75
374
Question 3
2 Points
The current capacity is only 220 openings because some of the shelters have been closed. What is the percentage of nights that the number of abused women seeking shelter will exceed current capacity?
62.21%
65.54%
34.46%
40%
A criminologist developed a test to measure recidivism, where low scores indicated a lower probability of repeating the undesirable behavior. The test is normed so that it has a mean of 140 and a standard deviation of 40.
Question 4
2 Points
What is the percentile rank of a score of 172?
78.81st percentile
80th percentile
40th percentile
28.81th percentile
Question 5
2 Points
What is the z-score for a test score of 200?
-1.50
0.5
1
1.50
Question 6
2 Points
What percentage of scores falls between 100 and 160?
28.81%
53.28%
19.15%
34.13%
Question 7
2 Points
What proportion of respondents should score above 190?
0.5
1
1.50
1.25
Question 8
1.5 Points
Suppose an individual is in the 67th percentile in this test. What is his or her corresponding recidivism score?
155.9
140
157.6
112.9
According to National Collegiate Athletic Association (NCAA) data, the means and standard deviations of eligibility and retention rates (based on a 1,000-point scale) for the 2013–2014 academic year are presented, along with the fictional scores for two basketball teams, A and B. Assume that rates are normally distributed.Normal Distribution Practice data
Question 9
1.5 Points
On which criterion (eligibility or retention) did Team A do better than Team B? Calculate appropriate statistics to answer this question.
Team A has better retention and eligibility than Team B.
Team B has better retention and eligibility than Team A.
Team B has better retention but Team A has better eligibility.
Team A has better retention but Team B has better eligibility.
Question 10
1.5 Points
What proportion of the teams have retention rates below Team B?
43.25
4.325
0.4325
0.04325
Question 11
1.5 Points
What is the percentile rank of Team A’s eligibility rate?
43.25th percentile
36.54th percentile
35.94th percentile
17th percentile
6 pages
Class Project
Below is grade of a sample of 36 students of Basic Statistics course offered during 2014 at Rutgers University. 1) Make a ...
Class Project
Below is grade of a sample of 36 students of Basic Statistics course offered during 2014 at Rutgers University. 1) Make a new variable called “New ...
SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techni ...
SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techniques to address research problems
Perform regression analysis to address an authentic problem
Overview
The 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.
Scenario
You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict 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 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.
Directions
Using 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 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:
Introduction
Describe 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 Collection
Sampling the data: Select a random sample of 50 houses.
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 Analysis
Histogram: 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 Model
Scatterplot: 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.
Conclusions
Summarize 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.
I need it by next Sunday morning.
Choose two of the following three questions and post your answer:1. In your own words, what is a nonparametric test? Wha ...
I need it by next Sunday morning.
Choose two of the following three questions and post your answer:1. In your own words, what is a nonparametric test? What is a parametric test (Section 13-2)?2. In your own words, identify an advantage of using rank correlation instead of linear correlation (Section 13-6).3. In your own words, what is the difference between a nonparametric test and a distribution-free test (Section 13-7)?
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