10.1.2Table #10.1.6 contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013). Create a scatter plot and find a regression equation between house value and rental income. Then use the regression equation to find the rental income a house worth $230,000 and for a house worth $400,000. Which rental income that you calculated do you think is closer to the true rental income? Why?Table #10.1.6: Data of House Value versus Rental Value Rental Value Rental Value Rental Value Rental 81000 6656 77000 4576 75000 7280 67500 6864 95000 7904 94000 8736 90000 6240 85000 7072 121000 12064 115000 7904 110000 7072 104000 7904 135000 8320 130000 9776 126000 6240 125000 7904 145000 8320 140000 9568 140000 9152 135000 7488 165000 13312 165000 8528 155000 7488 148000 8320 178000 11856 174000 10400 170000 9568 170000 12688 200000 12272 200000 10608 194000 11232 190000 8320 214000 8528 208000 10400 200000 10400 200000 8320 240000 10192 240000 12064 240000 11648 225000 12480 289000 11648 270000 12896 262000 10192 244500 11232 325000 12480 310000 12480 303000 12272 300000 12480 10.1.4The World Bank collected data on the percentage of GDP that a country spends on health expenditures ("Health expenditure," 2013) and also the percentage of woman receiving prenatal care ("Pregnant woman receiving," 2013). The data for the countries where this information are available for the year 2011 is in table #10.1.8. Create a scatter plot of the data and find a regression equation between percentage spent on health expenditure and the percentage of woman receiving prenatal care. Then use the regression equation to find the percent of woman receiving prenatal care for a country that spends 5.0% of GDP on health expenditure and for a country that spends 12.0% of GDP. Which prenatal care percentage that you calculated do you think is closer to the true percentage? Why? Table #10.1.8: Data of Heath Expenditure versus Prenatal Care HealthExpenditure(% of GDP) PrenatalCare (%) 9.6 47.9 3.7 54.6 5.2 93.7 5.2 84.7 10.0 100.0 4.7 42.5 4.8 96.4 6.0 77.1 5.4 58.3 4.8 95.4 4.1 78.0 6.0 93.3 9.5 93.3 6.8 93.7 6.1 89.8 For each problem, state the random variables. Also, look to see if there are any outliers that need to be removed. Do the correlation analysis with and without the suspected outlier points to determine if their removal affects the correlation. The data sets in this section are in section 10.1.10.2.2Table #10.1.6 (from problem 10.1.2) contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013). Find the correlation coefficient and coefficient of determination and then interpret both.10.2.4The World Bank collected data on the percentage of GDP that a country spends on health expenditures ("Health expenditure," 2013) and also the percentage of woman receiving prenatal care ("Pregnant woman receiving," 2013). The data for the countries where this information is available for the year 2011 are in table #10.1.8 (from problem 10.1.4). Find the correlation coefficient and coefficient of determination and then interpret both.For each problem, state the random variables. The data sets in this section are in the homework for section 10.1 and were also used in section 10.2. If you removed any data points as outliers in the other sections, remove them in this sections homework too.10.3.2Table #10.1.6 (from problem 10.1.2) contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013).a.) Test at the 5% level for a positive correlation between house value and rental amount.b.) Find the standard error of the estimate.c.) Compute a 95% prediction interval for the rental income on a house worth $230,000.10.3.4The World Bank collected data on the percentage of GDP that a country spends on health expenditures ("Health expenditure," 2013) and also the percentage of woman receiving prenatal care ("Pregnant woman receiving," 2013). The data for the countries where this information is available for the year 2011 are in table #10.1.8 (from problem 10.1.4).a.) Test at the 5% level for a correlation between percentage spent on health expenditure and the percentage of woman receiving prenatal care.b.) Find the standard error of the estimate.c.) Compute a 95% prediction interval for the percentage of woman receiving prenatal care for a country that spends 5.0 % of GDP on health expenditure.In each problem show all steps of the hypothesis test. If some of the assumptions are not met, note that the results of the test may not be correct and then continue the process of the hypothesis test.11.1.2Researchers watched groups of dolphins off the coast of Ireland in 1998 to determine what activities the dolphins partake in at certain times of the day ("Activities of dolphin," 2013). The numbers in table #11.1.6 represent the number of groups of dolphins that were partaking in an activity at certain times of days. Is there enough evidence to show that the activity and the time period are independent for dolphins? Test at the 1% level. Table #11.1.6: Dolphin Activity Activity Period RowTotal Morning Noon Afternoon Evening Travel 6 6 14 13 39 Feed 28 4 0 56 88 Social 38 5 9 10 62 ColumnTotal 72 15 23 79 189 11.1.4A person’s educational attainment and age group was collected by the U.S. Census Bureau in 1984 to see if age group and educational attainment are related. The counts in thousands are in table #11.1.8 ("Education by age," 2013). Do the data show that educational attainment and age are independent? Test at the 5% level.Table #11.1.8: Educational Attainment and Age Group Education Age Group RowTotal 25-34 35-44 45-54 55-64 >64 Did not completeHS 5416 5030 5777 7606 13746 37575 CompletedHS 16431 1855 9435 8795 7558 44074 College 1-3year 8555 5576 3124 2524 2503 22282 College 4 or more years 9771 7596 3904 3109 2483 26863 ColumnTotal 40173 20057 22240 22034 26290 130794 In each problem show all steps of the hypothesis test. If some of the assumptions are not met, note that the results of the test may not be correct and then continue the process of the hypothesis test.11.2.4In Africa in 2011, the number of deaths of a female from cardiovascular disease for different age groups are in table #11.2.6 ("Global health observatory," 2013). In addition, the proportion of deaths of females from all causes for the same age groups are also in table #11.2.6. Do the data show that the death from cardiovascular disease are in the same proportion as all deaths for the different age groups? Test at the 5% level.Table #11.2.6: Deaths of Females for Different Age Groups Age 5-14 15-29 30-49 50-69 Total Cardiovascular Frequency 8 16 56 433 513 All Cause Proportion 0.10 0.12 0.26 0.52 11.2.6A project conducted by the Australian Federal Office of Road Safety asked people many questions about their cars. One question was the reason that a person chooses a given car, and that data is in table #11.2.8 ("Car preferences," 2013).Table #11.2.8: Reason for Choosing a Car Safety Reliability Cost Performance Comfort Looks 84 62 46 34 47 27 Do the data show that the frequencies observed substantiate the claim that the reason for choosing a car are equally likely? Test at the 5% level.

CompetenciesIn 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.Tutorial: Downloading Office 365 ProgramsUse this tutorial for support with Office 365 programs.

For this assignment, choose one of the films in the How We Got to Now with Steven Johnson series to watch and review for this assignment. Answer these questions...What are the challenges the characters face in overcoming problems in technology? How does critically analyzing technology add value to interactions with people in personal and professional contexts?