Let Yt be the sales during month t (in thousands of dollars) for a photography studio. Let Pt be the price charged
for the portraits during month t. The data will be in the file named week 4 assignment chapter 12 problem that is
attached. Use regression to fit the following model to these data:
Yt = a + b1Yt−1 + b2Pt + et
This equation indicates that last month’s sales and the current month’s price are explanatory variables. The last
term, et, is an error term.
For your assignment, complete the following:
Part A: If the price of a portrait during Month 21 is $10, what would you predict for sales in Month 21?
Part B: Does there appear to be a problem with autocorrelation of the residual? Explain your answer.
In your Excel spreadsheet,
Utilize the regression model.
Utilize the support tool of Excel.
Predict sales as described in Part A.
Explain any problem as described in Part B
Statistical application and the interpretation of data is important in health care. Review the statistical concepts covered in this topic. In a 750-1,000 word paper, discuss the significance of statistical application in health care. Include the following:
Describe the application of statistics in health care. Specifically discuss its significance to quality, safety, health promotion, and leadership.
Consider your organization or specialty area and how you utilize statistical knowledge. Discuss how you obtain statistical data, how statistical knowledge is used in day-to-day operations and how you apply it or use it in decision making.
In preparation for writing your report to senior management next week, conduct the following descriptive statistics analyses with Microsoft® Excel®. Answer the questions below in your Microsoft® Excel® sheet or in a separate Microsoft® Word document:
Insert a new column in the database that corresponds to “Annual Sales.” Annual Sales is the result of multiplying a restaurant’s “SqFt.” by “Sales/SqFt.”
Calculate the mean, standard deviation, skew, 5-number summary, and interquartile range (IQR) for each of the variables.
Create a box-plot for the “Annual Sales” variable. Does it look symmetric? Would you prefer the IQR instead of the standard deviation to describe this variable’s dispersion? Why?
Create a histogram for the “Sales/SqFt” variable. Is the distribution symmetric? If not, what is the skew? Are there any outliers? If so, which one(s)? What is the “SqFt” area of the outlier(s)? Is the outlier(s) smaller or larger than the average restaurant in the database? What can you conclude from this observation?
What measure of central tendency is more appropriate to describe “Sales/SqFt”? Why?
Using the questions below, create a survey, and provide the survey to five people.
· What smartphone brand does your family/friends use?
· Overall, how satisfied are your family/friends with their smartphones?
· How do your family/friends rate different aspects of their experiences with their smartphones?
· How do your family/friends perceive Apple’s iPhone?
· How do your family/friends perceive the top iPhone competitors?
Discuss how you would create a well-designed survey to prevent confounding and
possible lurking variables.A well designed survey should take steps to prevent and consider some
of the “bad sampling designs – voluntary and convenience response”, “cautions about sample
surveys – undercoverage, nonresponse, response bias, and wording of questions.” Be sure to
discuss the methods you implemented to prevent theses bad sampling designs.
When applied to public administration and urban management, the objective of data analytics is to produce relevant, high quality, and timely evidence to underpin and guide effective strategic decision making. There is strong evidence showing that statistical applications can play an important role in benefiting not only private firms, but also public sector administration by generating more and better solutions that meet the needs of governance, healthcare, education, transportation, housing, assistance, and inclusion of certain socially, demographically, and geographically disadvantaged groups. Thus, with access to big data and the use of adequate analytical techniques, it has become possible to identify and measure previously invisible and therefore unsolvable issues.
In this final assignment, you are required to research and identify case studies and examples of statistical applications in public administration and urban management in the United States (e.g.: https://resources.data.gov/categories/case-studies-examples/). These cases should be concrete examples of the use and impact of advanced analytics in public administration and/or urban management, and they should systematically reflect the main challenges, opportunities, and lessons learned from the projects.
You are then required to select one specific case study of your interest that demonstrate statistical application in public administration or urban management that is uniquely aligned to your interest or professional career, for in-depth review and analysis using the following structure:
Brief introduction summarizing the problem/issues to be addressed, as well as the goals and objectives of the case or project.
What data had to be gathered or was available (include sources) to address the goals and objectives of the case or project.
State the methodologies, techniques and tools used to collect and analyze data to produce the results/findings presented.
Provide a critique of the statistical applications and summary of relevant challenges and opportunities from the case or project.
What key lessons or recommendations could be learned from your case or project for either public policy, governance, or management.
In an article in the Journal of Statistics Education (vol. , no 2), Allen Shoemaker describes a study that was reported in the Journal of American Medical Association (JAMA). It is generally accepted that the mean temperature of an adult human is 98.6°F. In his article, Shoemaker uses the data from the JAMA article to test his hypothesis. The data for the JAMA article were collected from healthy men and women, ages 18 to 40, at the University of Maryland Center for Vaccine Development, Baltimore. [Elementary Statistics: Picturing the World by Ron Larson and Betsy Farber 7th Ed.]
Data: Click on “StatCrunch” on MyLab Statistics, then “Data sets from the textbook”. Scroll down to Chapter 7, then click on Chapter 7: Case Study: Human Body Temperature: What’s Normal?
Do all of the work below with the “Women” data set Download “Women” data set.
Use a statistical software package to complete the analysis of Exploratory Data Analysis and Inferential Statistics. You are strongly encouraged to use StatCrunch (in MyLab Statistics). You may also use any other software packages (s.a. MINITAB, or SPSS). However, if you use other software, you are on your own, for your instructor will only help with question with regards to StatCrunch.
A) Exploratory Data Analysis
Construct a stem and leaf display.
Construct a histogram.
Construct a boxplot and use it to investigate the data for outliers.
Obtain the sample mean, median, mode, and standard deviation.
B) Inferential Statistics
Construct a normal probability plot or quantile-quantile (QQ) plot to assess normality. Use this assessment to determine which tests are appropriate for these data for confidence interval and test of hypothesis as you will use these procedures for parts 2 and 3 below.
Construct a 95% confidence interval for µ, for mean body temperature for all adult females in the population.
At the 5% significance level, do the data provide sufficient evidence to conclude that the mean body temperature of women is 98.6°F?
Using the software output, perform the confidence interval and the full hypothesis test.
Write a summary and findings of the project
Write a brief introduction of the project.
Interpret the mean, and standard deviation in context of the data given.
Compare the measures of center and comment on which measure of center would best describe the data given.
Comment on the shape of the distribution.
Are there any outliers in the data? Include results of your investigation for outliers.
Comment on your assessment of normality.
Which test will be most appropriate for the inferential statistics? Z-test or t-test? Why?
Write an interpretation for the confidence interval estimate in context of the data.
State the hypothesis (the null and the alternative)
State the conclusion and summary of the significance test and interpret the p-value using the context of the data.
Compare the results from the confidence interval and the significance test. Are they the same? Why do think this is the case?
When discussing the studies behind this statistic, Leonhardt focused on data out
of Singapore. Leonhardt criticized the C.D.C. statistic as “almost certainly
Write a roughly 2-page paper explaining how this case of the C.D.C. statistic
and the Singapore data behind it illustrates Heather Douglas’s position
about inductive risk and the role of values in science, and taking a stand on
the use of values in this case.
Your paper should do all of the following:
Explain Heather Douglas’s position that consideration of inductive risk can
require the use of non-epistemic values in the internal stages of science
Using Leonhardt’s article, explain how non-epistemic values, through
consideration of inductive risk, affected
analyses of the Singapore data
the C.D.C.’s statement that less than 10 percent of Covid transmission
Do you agree with the value judgments made here by the researchers and the
C.D.C.? Why or why not?
Discuss the following and give real life examples.
Why do we have a Margin of Error in Statistics? If statistics are meant to be accurate, why are they sometimes accompanied by an estimate of doubt?
What is the relationship between Margin of Error, Confidence interval and Sample Size.
You must use complete sentences and correct grammar/spelling.
Complete assignment as directed in document. Save your electronic copy as RTF (Rich Text Format) or PDF so that everyone will be able to open the document.
There is often the requirement to evaluate descriptive statistics for data within the organization or for health care information. Every year the National Cancer Institute collects and publishes data based on patient demographics. Understanding differences between the groups based on the collected data often inform health care professionals about research, treatment options, or patient education.
Using the data on the “National Cancer Institute Data” Excel spreadsheet, calculate the descriptive statistics indicated below for each of the Race/Ethnicity groups. .
Provide the following descriptive statistics:
Measures of Central Tendency: Mean, Median, and Mode
Measures of Variation: Variance, Standard Deviation, and Range (a formula is not needed for Range).
Once the data is calculated, provide a 150-250 word analysis of the descriptive statistics on the spreadsheet. This should include differences and health outcomes between groups.