statistic in quality
Question Description
answer these 6 questions, no paragraphs needed few sentences is enough ( short answers). please put the number of the question next to it answer. please use the pdf file to answer the questions
Multivariable Calculus Math Exam
Question Description
Its a multivariable calculus exam, it has 8 questions.
I will attach the exam and the formula sheet and cheat sheet to help you find the solution easier.
Please write the answers clearly and understandable.
Statistics Question
Question
Overview
When we run a hypothesis test, we are testing whether or not a claim made is valid from a statistical standpoint. We break up our region into two regions, one is called the null hypothesis (H0), the other is called the alternative hypothesis (H1).
There are some rules we have to follow when we make our hypotheses. First, the null hypothesis must always include some level of equality and to make this easier, the modern approach to writing hypotheses has us use = for all hypotheses. The alternative will then point to the appropriate region we desire to test and must be a strict inequality. This means the symbols will be >, <, or ?.
Based on this, we can break our hypotheses up into three different sets:
Left Tailed Hypotheses: H0:?=?0 vs H1:?
Right Tailed Hypotheses: vs H1:?>?0
Two Tailed Hypotheses: vs H1:???0
We determine our results from a hypothesis test by using the p-value. The p-value of our test is “the probability of seeing the result we saw, or more extreme, by random chance given that the null hypothesis is true.” If our p-value is small, we would say seeing something like this by random chance is very unlikely. If our p-value is larger, then it is more plausible to see something like this by random chance.
The value of alpha, ?, is the probability we see a type I error, which means we reject the null hypothesis when the null hypothesis is in fact the truth. As the ones doing the research, we are able to select this value ourselves. We want to choose an alpha value that appropriately identifies the ramifications of making a mistake. If the results of our test have major outcomes associated with them, we may want to have a stricter (or smaller) value chosen for alpha. If the results of our test do not have major ramifications about them, we may want to choose a less strict (or larger) value for alpha. Common alpha values are ? = 0.1, 0.05, and 0.01.
Our conclusion will be based on how our computed p-value relates to our value for alpha. If our p-value is less than alpha, we reject the null hypothesis. If our p-value is larger than alpha, we fail to reject the null hypothesis. In the infamous words of a brilliant man, “If p is small, reject them all.”
Here is the process we follow when performing a hypothesis test:
Step 1: Write out your hypotheses based on the words given in the problem
Step 2: Determine your value of alpha
Step 3: Compute your Z statistic
Step 4: Compute your P-value
Step 5: Make your conclusion
Now, let’s discuss how we compute our Z-value. If we know the population standard deviation, then our Z value will be z=x¯???n.
Now, let’s look at an example. Suppose the local car dealership claims their brand new mid-sized sedan gets at least 32 mpg on average with a standard deviation of 4 mpg. We want to test to see whether or not this claim is true. So, we collected a sample of 35 of these mid-sized sedans and found the average mpg was 30 mpg. Do we have enough evidence to dispute this claim?
Step 1: H0:?=32mpg vs H1:? <32mpg
Step 2: ? = 0.05 (Chosen to be a touch stricter due to us wanting a little more certainty in our result before we make such big purchase)
Step 3: =30?32435=?2.958
Step 4: P-value = 0.0015
Step 5: Our p-value is smaller than our alpha value, so we will reject the null hypothesis and have statistical evidence to dispute the claim
If we want to run a hypothesis test for the population proportion, we will follow a very similar construct as we did with the mean, except our Z-statistic calculation will change. Our hypotheses will be similar, except we will replace µ with p. Here are the hypotheses:
Left Tailed Hypotheses: H0_p=p0 vs H1:p<p0
Right Tailed Hypotheses: vs H1:p>p0
Two Tailed Hypotheses: vs H1:p?p0
The Z value for the population proportion hypothesis test will be computed as follows: Z=p^?ppqn.
Let’s look at an example: Recent health statistics show that an estimated 12.5% of U.S. adults currently smoke cigarettes. We decide to test this claim, and survey 1500 adults and ask them whether they smoke or not. We have found that 195 of the 1500 (13%) claim to be smokers. Is there enough evidence to reject the claim made that the true proportion is 12.5%?
Step 1: :p=0,125 vs :p? 0.125
Step 2: ? = 0.1 (Chosen to be a little less strict as the outcome of our test may not have any direct impact on anyone or anything, but could be used to inform a decision maker if needed)
Step 3: Z=p^?ppqn=0.13?0.1250.125?0.8751500=0.586
Step 4: P-value = 0.558
Step 5: Our p-value is larger than our alpha value, so we will fail to reject the null hypothesis and do not have statistical evidence to dispute the claim
Instructions
For this discussion post, we are going to run a hypothesis test using the Z-distribution. Read the following:
The average salary for a registered nurse in Las Vegas is claimed to be $82,000 with a standard deviation of $14,500. To determine if this information is accurate, we sampled 80 registered nurses working in Las Vegas and find their average salary to be $85,025. Test this at the ? = 0.05 level.
Discussion Prompts
Answer the following questions in your initial post:
What are the hypotheses based on the words given in the problem?
What is our Z?
What is the P-value?
Based on your p-value and alpha, what conclusion will we make? Do we have evidence that the claim is false?
Are there any other variables that could potentially impact the salary of a registered nurse? What are some methods of sampling we can use to ensure we have a representative population.
MAT 240 Applied Statistics Project One Report
Question
MAT 240 Project One Guidelines and Rubric
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 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 Question 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 predictor (x) and response (y) variables in a linear regression to justify the selection of variables.
Data Collection
- Sampling the data: Select a random sample of 50 houses. Describe how you obtained your sample data (provide Excel formulas as appropriate).
- Identify your predictor and response variables.
- Scatterplot: Create a scatterplot of your predictor and response variables to ensure they are appropriate for developing a linear model.
Data Analysis
- Histogram: Create a histogram for each of the two variables.
- 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 house sales and square footage.
- Compare and contrast the center, shape, spread, and any unusual characteristic for your sample of house sales with the national population (under Supporting Materials, see the National Summary Statistics and Graphs House Listing Price by Region PDF). Determine whether your sample is representative of national housing market sales.
Develop Your Regression Model
- Scatterplot: Provide a scatterplot of the variables with a line of best fit and regression equation.
- Based on your scatterplot, explain if a regression model is appropriate.
- 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.
- Calculate r: Calculate 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. For example, answer the questions: what does the slope represent in this situation? What does the intercept represent? Revisit the Scenario above.
- 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 assumed square footage of your home at 1500 square feet.
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.
You can use the following tutorial that is specifically about this assignment. Make sure to check the assignment prompt for specific numbers used for national statistics. The videos may use different national statistics. You should use the national statistics posted with this assignment.
analyzing data using descriptive statistics
QUESTION
In this discussion board, you will view the video and consider how statistical significance affects the results of a project or study. We use t-tests to determine mean scores between the pretest and posttest results. In between giving an instrument as a pretest, we teach or share information for the participants so that they learn something new based on evidence. Once that is completed, a posttest is given to see if there are higher posttest scores depending on what is proposed. For instance, if we are measuring levels of confidence with the Confidence Scale (C-Scale), we would like to see an increase from the pretest to the posttest indicating that the participants are more confident. However, if we are measuring anxiety, we would want the scores to decrease after the intervention. When providing your initial post, share the instrument you are considering for your paper and indicate if you want the scores to go up or down and what an acceptable “p value” is at the <0.05 level to have statistical significance. Please use your research text for assistance.
Required Videos
Statistical Significance and p-values (8:56 Minutes) (2020) (WO: 6, 7)
Statistical Significance and p-values Video Transcript
t-tests for Beginners (19:50 Minutes) (2016) (WO: 6, 7)
t-tests for Beginners Video Transcript
Instruments and Articles
- Psychometric properties of the Post Traumatic Stress Disorder Checklist for DSM-5 (PCL-5) in Greek women after cesarean section
- Social anxiety and celebrity worship: the mediating effects of mobile phone dependence and moderating effects of family socioeconomic status Rong Jia1†, Qing Yang1†, Bo Liu2, Han Song3 and Zhengjun Wang1*
- The Psychometric Properties of the Patient Health Questionnaire-9 in a Sample of Korean University Students
- Psychometric properties of the Strength and Difficulties Questionnaire from five European countries
Initial Post:
For your initial post, review one of the articles provided and discuss the analysis done by the authors about the instrument they are discussing. Consider the analysis section and if they had at least a sample of >100 participants (n=100), what kind of t-test did they run, and what was the p value they found. Explain that we normally expect the p value to be <0.05 to have statistically significant results-did this study achieve this based on the tables in the article? Consider if the study demonstrated clinically significant results that may be useful in practice.
When you are analyzing what was found look to see the following:
1. Did they have at least a sample of >100 participants (n=100)?
2. What kind of t-test did they run?
3. What was the p value that they found?
4. We normally expect the p value to be <0.05 to have statistically significant results-did this study achieve this based on the tables in the article?
5. Did the study demonstrated clinically significant results that may be useful globally in practice?
STATISTICS FOR CATEGORICAL DATA
Question
ODDS RATIOS AND CHI-SQUARE
This assignment focuses on categorical data. Two of the statistics most often used to test hypotheses about categorical data are odds ratios (ORs) and the chi-square. The disease-OR refers to the odds in favor of disease in the exposed group divided by the odds in favor of the unexposed group. Chi-square statistics measure the difference between the observed counts and the corresponding expected counts. The expected counts are hypothetical counts that would occur if the null hypothesis were true.
PART 1: ORS
A study conducted by López-Carnllo, Avila, and Dubrow (1994) investigated health hazards associated with the consumption of food local to a particular geographic area, in this case chili peppers particular to Mexico. It was a population-based case-control study in Mexico City on the relationship between chili pepper consumption and gastric cancer risk. Subjects for the study consisted of 213 incident cases and 697 controls randomly selected from the general population. Interviews produced the following information regarding chili consumption:
Table 1: Chili Pepper Consumption and Gastric Cancer Risk
Chili pepper consumption Case of gastric cancer Controls
Yes A = 204 B = 552
No C = 9 D = 145
Reference:
López-Carnllo, L., Avila, M. H., & Dubrow, R. (1994). Chili pepper consumption
and gastric cancer in Mexico: A case-control study. American Journal of
Epidemiology, 139(3), 263–271.
Note: You do not need to use the SPSS software to complete this assignment.
In a Microsoft Excel worksheet, calculate the odds of having gastric cancer.
In addition, provide a written interpretation of your results in APA format.
Refer to the Assignment Resources: Odds Ratio to view an example of odds ratio. The same resource is also available under lecture Testing Hypotheses.
PART 2: CHI-SQUARE
Bain, Willett, Hennekens, Rosner, Belanger, and Speizer (1981) conducted a study of the association between current postmenopausal hormone use and risk of nonfatal myocardial infarction (MI), in which 88 women reporting a diagnosis of MI and 1,873 healthy control subjects were identified from a large population of married female registered nurses aged thirty to fifty-five years. There were 32 women who currently used hormones and had a diagnosis of MI and 56 women reporting a MI and never used hormones. Of the women controls (women who did not report a MI) 825 currently use hormones and 1,048 never used hormones. To test the hypothesis that there is no association between use of postmenopausal hormones and risk of MI, chi-square statistics need to be calculated in SPSS using a 0.05 level of significance. The SPSS data are provided in the link below. The SPSS dataset consists of two variables:
Click here to access the SPSS data.
SPSS Dataset Variables
Name Label of Variable Values
Group Association Group
Control
Case
Use Hormone Use
Currently Use
Never use
Reference:
Bain, C., Willett, W., Hennekens, C. H., Rosner, B., Belanger, C., & Speizer,
F. E. (1981). Use of postmenopausal hormones and risk of myocardial
infarction. Circulation, 64(1), 42–46.
SOSC Lab 1
QUESTION
Dr. Rose was interested in studying student involvement in extracurricular activities, residence, school motivation scores, life satisfaction scores, and their scores on exams 1, 2, and 3. He found that students were either (0) not involved with extra-curricular activities on campus or (1) involved with extracurricular activities and either lived (1) on campus, (2) off-campus, or (3) with their parents. Using these data Lab 1.omv Download Lab 1.omv, please answer the questions below and provide your Jamovi file with analyses completed. Be sure to open the Jamovi software FIRST and then open the file within the Jamovi software. Here is a Step-by-Step Guide
Actionsto assist you in completing this lab in Jamovi.
Note the different variable icons used in Jamovi to represent different variable levels. These are important since a mean or average, for example, cannot be computed with Nominal Data.
Lab Details
Your submission for this Lab Assignment should be a Jamovi file with an omv extension. NOTE that you will not be able to Preview your submission since you are only able to see the file when in the Jamovi application.
Create a variable called Average Exam Score, the average of exam scores 1, 2, and 3. Use the compute mean command to do this.
Transform the Motivation Variable into Motivation Groups with three groups: Low (1-3), Medium (4-6), and High (7-9). Use the layered IF command to do this.
Create a frequency table for the residence variable.
Use the descriptive statistics function to find the mean and standard deviation of the life satisfaction variable split by two variables; extra-curricular involvement and Motivation_Groups. Report each mean and standard deviation and provide output.
Find the mean and standard deviation for exam 1, exam 2, exam 3, and average exam scores variables.
Compute histograms with a normal curve (density) for average exam scores and life satisfaction scores.
Create a histogram without a normal curve (density) for exams 1, 2, and 3.
Split the data by the motivation groups variable and find the mean exam score and life satisfaction score for each group as well as the histogram and density for each group.
Important
This lab will utilize Jamovi, to review the resources linked in your Course Tools and Resources to get started with Jamovi. Please reach out to your instructor with any questions or concerns.
You may find it best to download the software for Jamovi directly to your device for free if you wish. This can be done through the Jamovi webpage.
Discussion post answer then respond to 2 there peoples posts.
QUESTION
Central Tendency & Variability
For your main post, gather some data (N of 20 or more)
You have two options for data:
Option 1: Revisit the data you used in the last discussion – but only if you had an N of 20+ and an interval level of measurement for that variable!
Option 2: Pull some new data from the student survey for one variable that is at the interval level of measurement.
- Then, with the data you have collected:
- Calculate all measures of central tendency (mean, median, mode) and variability (range, sd) for your variable,
Post your results (use the name of your variable as the subject of your post). Use the Table icon (looks like a grid) to make a table in which to show your calculations more organized and clear. To include the formula and numbers you used, you can type them in as best you can or you can get fancy with the “Math Equation” icon (looks like the square-root symbol). (See resources below.)
For your comment posts:
Post at least two comments on someone else’s data – interpret what they calculated, discuss the patterns of central tendency and variability for their variable…
- Have fun with this – don’t overthink the interpretation… Practice what we’re learning in class…
Just put into words what it means to get the mean, median, mode, range, and sd (separate sentences for each) and overall what they tell you (Central tendency: what do they mean? Are they all the same? different? How different? Is there skew? Which statistic is best for central tendency? Variability: what do they mean? Which statistic is best for variability?)
Conclusion part for a project
QUESTION
You or your team will turn in a short report. This means you should write in full sentences, and have the following sections for each question, while being as specific as you can about your results: I. Introduction. State the question you are trying to answer, why it is a question of interest (why might we be interested in the answer), and what approach you are going to take (just the name of the approach). II. Summary of your data. This should include things like plots (histograms, boxplots) including the interpretation of the plots, and summary values such as sample means and standard deviations. You should have an idea about the trend of the data from this section. III. Diagnostics. You should discuss your assumptions here, and if you believe they are violated. Perform diagnostics for the model. If you believe assumptions are violated, note this and continue with the project. Do not consider transformation of variables for this topic. IV. Analysis. Report back the “best” model (and how you chose that as the best), confidence intervals, test-statistic/s, and p-value/s, nulls and alternatives, etc. You may use tables here, but be sure that you organize your work. Remember to write your results in full sentences where possible. V. Interpretation. State your conclusion, and what inference you may draw from your corresponding tests or confidence intervals, and any other useful statistics you may have calculated. These should all be interpreted in terms of your problem. VI. Conclusion. Summarize briefly your findings. Here you do not have to re-iterate your numeric values, but summarize all relevant conclusions.
See below! Needed by 10pm EST
Question
Problem 1. Formulate your answer based on the information below. The intensity of care delivered dropped from a budgeted case mix of 0.90 to an actual case mix of 0.85. What dollar effect did this have on actual costs?
You have been asked by management to explain the variances in costs under your inpatient capitated contract. The following data is provided. Use the following data to calculate the variances.
Budget
Actual
Inpatient Costs
$12,568,500
$16,618,350
Members
42,000
42,000
Admission Rate
0.070
0.095
Case Mix Index
0.90
0.85
Cost per Case (CMI = 1.0)
$4,750
$4,900
Problem 2. Based on the information below, what rate must be set to generate the required $80,000 in profit in the preceding example?
You have been asked to establish a pricing structure for radiology on a per-procedure basis. Present budgetary data is presented below:
Budgeted Procedures
10,000
Budgeted Cost
$400,000
Desired Profit
$80,000
It is estimated that Medicare patients comprise 40 percent of total radiology volume and will pay on average $38.00 per procedure. Approximately 10 percent of the patients are cost payers. The remaining charge payers are summarized below:
Payer
Volume%
Discount%
Blue Cross
20
4
Unity PPO
15
10
Kaiser
10
10
Self Pay
5
40
50%
Problem 3. What is the amount of variance that is attributed to the difference between the budgeted and actual wage rate per hour? Use the following data to calculate the variances.
The following information has been prepared for a home health agency.
Budget
Actual
Wage Rate per Hour
$16.00
$17.00
Fixed Hours
320
320
Variable Hours per Relative
Value Unit (RVU)
1.0
1.1
Relative Value Units (RVUs)
1,000
1,200
Total Labor Hours
1,320
1,640
Labor Costs
$21,120
$27,880
Cost per RVU
$21.12
$23.23
Budgeted costs at actual volume would be $25,344 ($21.12 × 1,200), and the total variance to be explained is $2,536 Unfavorable ($27,880 – $25,344). Be sure to specify whether the variance is favorable or unfavorable.
We Accept
