Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.To prepare for this Discussion:Review Chapters 10 and 11 of the Frankfort-Nachmias & Leon-Guerrero course text and the media program found in this week’s Learning Resources related to bivariate categorical tests.Create a research question using the General Social Survey dataset that can be answered using categorical analysis.By Day 3Use SPSS to answer the research question. Post your response to the following:What is your research question?What is the null hypothesis for your question?What research design would align with this question?What dependent variable was used and how is it measured?What independent variable is used and how is it measured?If you found significance, what is the strength of the effect?Explain your results for a lay audience and further explain what the answer is to your research question.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.

Option 2: New York Survey Data InstructionsA consulting firm was hired to perform a survey on people living in New York City. The survey was completed monthly for six months by 445 randomly-selected people in different boroughs. There were a number of items on the survey, but six basic biographical items will be studied for this exercise. The data for the people surveyed in one of these monthly surveys can be found in the Excel file SURVEY. The variables that were used for the basic biographical data are found on the last page of the exercise.In this exercise, some of the estimation techniques presented in the module will be applied to the New York survey results. You may assume that these respondents represent a simple random sample of all potential respondents within the community, and that the population is large enough that application of the finite population correction would not make an appreciable difference in the results.New York City governmental agency personnel like to have point estimates regarding variables describing the biographical information of the people living within the different boroughs. It is very helpful for them to have some idea regarding the likely accuracy of these estimates as well. Therein lies the benefit of the techniques presented in this module and applied here.Item A in the description of the data collection instrument lists variables 1–5, which represent the respondent’s general attitude toward each of the five boroughs. Each of these variables has numerically equal distances between the possible responses, and for purposes of analysis they may be considered to be of the interval scale of measurement.Determine the point estimate, and then construct the 95% confidence interval for = the average attitude toward Manhattan.Repeat part (a) for μ2 through μ5, the average attitudes toward Brooklyn, Queens, The Bronx and Staten Island, respectively.Given the breakdown of responses for variable 6 (highest level of education), determine the point estimate, and then construct the 95% confidence interval for p6= the population proportion of doctoral degrees.Given the breakdown of responses for variable 7 (marital status of respondent), determine the point estimate, and then construct the 95% confidence interval for p7 = the population proportion in the “single or other” category.Assume the governmental agencies requested estimates of the mean attitudes towards each borough with a margin of error of 0.05 for each borough. If the governmental agency personnel want to have 95% confidence that the sample mean will fall within this margin of error, how large should the sample sizes be for each borough?Paper RequirementsWrite a report that uses the Written Assignment Requirements under the heading Expectations for CSU-Global Written Assignments found in the CSU-Global Guide to Writing and APA (Links to an external site.)Links to an external site.. Items that should be included, at a minimum, are a title page, an introduction, a body that answers the questions posed in the problem, and a conclusion paragraph that addresses your findings and what you have determined from the data and your analysis. As with all written assignments, you should have in-text citations and a reference page. Please include any tables of calculations, calculated values, and graphs associated with this problem in the body of your assignment response.Note: You must submit your Excel file with your report. This will aid in grading with partial credit if errors are found in the report.A. General Attitude toward Each Borough (Variables 1–5)1. Manhattan2. Brooklyn3. Queens4. The Bronx5. Staten IslandLike Very Much(5)(5)(5)(5)(5)Like(4)(4)(4)(4)(4)Neutral(3)(3)(3)(3)(3)Dislike(2)(2)(2)(2)(2)Dislike Very Much(1)(1)(1)(1)(1)B. Information about the Respondent (Variables 6–7)What is your highest level of education? (1) = Did not complete high school(2) = High school degree/GED(3) = Associate’s degree(4) = Bachelor’s degree(5) = Master’s degree(6) = Doctoral degreeMarital Status: (1) = Married, (2) = Single or other

The purpose of this assignment is to apply multiple regression concepts, interpret multiple regression analysis models, and justify business predictions based upon the analysis.For this assignment, you will use the "Strength" dataset. You will use SPSS to analyze the dataset and address the questions presented. Findings should be presented in a Word document along with the SPSS outputs.The compressive strength (Y) of concrete is influenced by the mixing proportions and by the time that it is allowed to cure, although the exact relationship between the strength and the components is unknown. The provided data includes the results of n = 1030 concrete strength experiments that include the following:Strength (in MPa): The compressive strength of the concrete.Age (in days): The number of days the concrete was allowed to cured.Coarse_Aggregate (in kg/m3): The proportion of coarse aggregate in the mix.Fine_Aggregate (in kg/m3): The proportion of fine aggregate in the mix.Cement (in kg/m3): The proportion of cement in the mix.Slag (in kg/m3): The proportion of furnace slag in the mix.Superplasticizer (in kg/m3): The proportion of plasticizer in the mix.Water (in kg/m3): The proportion of water in the mix.Ash (in kg/m3): The proportion of fly ash in the mix.Part 1:Derive various transformations of compressive strength to determine which transformation, if any, results in a variable that most closely mimics a normal distribution. To do this, plot Q-Q plots after each transformation listed below, and decide which one should be used to build a multiple linear model. Explain your answer and provide the SPSS output as an illustration.Strength (no transformation)Square root of StrengthSquared Strength(Natural) Log of StrengthReciprocal of StrengthPart 2:Based on the transformation selected in Part 1, build a multiple linear regression model with all eight predictors.Use t-tests to determine if any of the predictors significantly affect the compressive strength of concrete. Explain why each variable should or should not be included in the model. Assume α = 0.05. Show the appropriate model results to explain your answer.If any predictors from question 1 are found to be not significant, remove them and re-run the model to create a reduced model (RM). Are all the remaining variables still statistically significant? Show the appropriate model results to explain your answer.Based on the RM, should there be concern about multicollinearity among the predictors selected? Show the appropriate model results to explain your answer.After fitting the RM, derive the residual plot (standardized residuals vs. standardized predicted values) and normal probability plot. Interpret each plot.What is the coefficient of determination, R2, of the RM? How would you interpret the R2?Based on the RM, what would be the new estimated compressive strength that is currently 50 MPa, after a 10-day increase in curing time? Assume all other predictors are held constant.How would you interpret the intercept (constant) in the RM? Does the interpretation make sense given the data you used to build the RM?Part 3:Given the following components and aging time below, what is the estimated compressive strength based on the RM?Age: 50 daysCoarse_Aggregate: 900 kg/m3Fine_Aggregate: 600 kg/m3Cement: 300 kg/m3Slag: 200 kg/m3Superplasticizer: 7 kg/m3Water: 190 kg/m3Ash: 70 kg/m3Part 4:What is a 95% confidence interval of the estimate in Part 3? How would you interpret the 95% confidence interval? (Hint: Use the SPSS scoring wizard to address this question.)