Category: Statistics

In a unimodal symmetric distribution, about 68% of the values fall within one st

In a unimodal symmetric distribution, about 68% of the values fall within one standard deviation of the mean, and about 95% fall within two standard deviations of the mean. How does that apply to the manufacturing of aircraft or cars? Can you think of examples where a company would use information based on income, height, weight, age, or other demographics to produce products?
Select an industry, company, or product that you think uses (or should use) the normal distribution to aid in the design or marketing of a product. Do some research on the industry, company, or product if needed.
Identify the industry, company, or product you selected, and then discuss the following in your initial response. Be sure to include a reference.
Would using the normal distribution would be advantageous for the company? Why or why not?
What are some ethical ramifications of designing products using information based on the normal distribution?
Copy and paste the questions into your initial post.

The grader asks us to” Find the most important car characteristics that affect p

The grader asks us to” Find the most important car characteristics that affect prices for cars”. My teammates have done and completed the python codes to prove their conclusion: “Performance, Size, Symboling, Normalized Losses, Combined-Mpg, FWD, RWD, 4-WD, Mid-Range”. Now they need me to write our process into words with about 4 pages. I did not participate in previous discussion as I have other projects to do. They are off and go to holidays now. I upload their python and draft ideas now, and hope you can write into words. No GRAPHS can be put into the paper, only Words and Sentences. I have also uploaded the grader’s instructions and criterions for grading. I have also uploaded some materials that might be helpful for you to have a look. To be precise, you don’t have to handle python codes, but just to write the process one step by one step. Imagine like, we have to do presentation based on our python code, so the 4-page paper is what we say out with running the python code “First, we do…; then, we do…; Our hypothesis is…; we use models like…; we run regressions to analyze…” These kinds of sentences to fulfill the grader’s need.

1. Each answer should contain four parts.
In PART 1: you should justify the t

1. Each answer should contain four parts.
In PART 1: you should justify the type of statistical test used and state ALL hypotheses for the analysis.
When answering this section you should consider what variables are used. If it is problem that compares mean then what are the independent variables (IV’s) and dependent variables (DV’s how many levels of each IV, are the levels between subject or within subject? If you are examining a relationship then what is being predicted and what are the predictors? If you are examining association then which variables are being associated?
Be explicit in why you chose the test you chose. For example, if you have a 2×3 factorial ANOVA it is not good enough to just state the test you chose.
PART II should include your JASP output. You are encouraged to include an exploratory graph from JASP to help you understand your analvsis.
PART IlI should contain a written passage USING APA FORMATTING to describe your results and conclusion. Be sure to report the appropriate descriiptive statistics of the data being analyzed, state the results of the statistical tests performed, and make a concluding statement regarding the applications of the results found. An example of this section is provided below. You will find the “Reporting Results” Power Point file posted on the Moodle site to be helpful.
PART IV – IF the problem contains questions other than those related to the data analyses, e.g. questions regarding the design of the study, put the answers to those questions in a separate section labeled Part
IV. If no additional question is included just mark Part IV as “N/A”

I would like you to write a letter of recommendation for transfer university tha

I would like you to write a letter of recommendation for transfer university that aligns with the student profile sought by the Columbia University School of General Studies because the professor asked me to write it by myself.
Where to apply to
I am planning to apply to the Columbia University School of General Studies, majoring in Cognitive Science.
Link About Columbia University School of General Studies
https://www.gs.columbia.edu/content/mission-vision-goals
Relationship with the professor
Professor and I have had a relationship from February to June 2023, and taught me statistics. The descriiption about the statistics course I took is below.
Math 54: Elementary Statistics This course covers concepts and procedures of descriiptive statistics, elementary probability theory, and inferential statistics. Course content includes: summarizing data; computation and interpretation of descriiptive statistics; classical probability theory; probability distributions; binomial, normal, T, Chi-square and F distributions; making inferences; decisions and predictions. This course develops, analyzes, and interprets confidence intervals for population parameters, hypothesis testing for both one and two populations, correlation and regression, ANOVA, and test for independence. This course develops statistical thinking through the study of applications in a variety of disciplines. The use of a statistical/graphing calculator and/or statistical analysis software (Stat Crunch, Excel) is integrated into the course.
Brief Details about me
My current GPA is 4.0.
I’m the president of Japanese Student Association.
I’m the secretary of Phi Theta Kappa.
I have some work experiences.
I have an associate degree in Economics with the highest honors of Santa Monica College, which requires to graduate with 4.0 GPA.
Resume and Final Project
My resume and the final project of the class are attached named Resume(3) and Analyzing Housing Prices in Santa Monica.
Why I would like to major in Cognitive Science.
My interest in cognitive science began not from a classroom but from a personal defeat at “Shogi” (Japanese chess). The losses to younger, less-experienced players sparked my profound curiosity, paralleling the world’s astonishment when an AI-first defeated a top Japanese chess player. This intersection of personal experience and global technological advancement set the trajectory of my academic pursuits.
Embarking on this, I sought to unravel the mystery behind my defeat. What strategic intricacies had I overlooked? How could a novice defeat me? These questions led me to delve into AI and cognitive science. I began to learn machine learning. As I developed my shogi AI, I ventured beyond traditional learning methods, embracing a hands-on approach that honed my analytical skills.
The development process was challenging and enlightening. I employed techniques such as the Monte Carlo tree search, integrating endgame “Tsume” (checkmate) searches, and a df-pn algorithm to enhance the AI’s capability in longer sequences. Each obstacle in refining the AI’s endgame strategies provided a deeper understanding of decision-making mechanisms in machines and, by extension, humans.
My shogi AI’s participation in league matches against conventional software was a crucible that tested and refined its decision-making algorithms. These matches were not merely competitions but experimental setups that allowed me to observe, analyze, and improve how AIs make decisions. Through iterative trial-and-error testing against this AI opponent, the root cause of my inferior performance was revealed. Positional assessments indicating an 80% expected win probability for my playing habit would plunge to 40% in practical application. This hobby revelation via leading-edge recreation analytics crystallized the need for rigorous self-adjustment by confronting the harsh truth of counterproductive tendencies hidden beneath prized intuitions. This practical application of the theory was instrumental in deepening my understanding of the cognitive processes, a cornerstone of cognitive science.
In conclusion, it has shaped my understanding of learning, problem-solving, and decision-making. I’ve come to appreciate the intricate dance between human thought and machine logic, realizing that each offers unique insights into the other. This epiphany has fueled my passion for cognitive science and provided a clear vision for my future studies.

In this project, you will have a chance to showcase your analytical techniques a

In this project, you will have a chance to showcase your analytical techniques as well as decision-making skills on a business problem. As the outcome of this project, you will make judgements that are informed by data analyses.
The Case
You are an investment consultant. An important client of yours wants to invest in real estate in a U.S. city that has been experiencing an uptick in growth. The client wants to purchase 10 real estate properties that will have the highest sales prices in the future. She pre-selected 25 potential houses that appear to be good deals. You job is to predict the sales prices of the 25 properties as close to reality as possible. Based on your prediction, you need to make the decision on which 10 properties (i.e., ranked highest in future sale prices) to invest.
To make predictions, the broker of the properties has given you the relevant information of the pre-selected 25 potential houses. The information contained in the “Prediction” data sheet.
In addition, you are given data collected on 125 houses sold in the city over the past 2 years. These data are contained in the “Sales” data sheet. The data collected on the houses include:
1. ID – the property ID, which is the unique IDs of the real estate properties
2. House Size – the size of the house in square feet
3. Lot Size– the size of the lot in acres
4. Rooms – the total number of rooms in the house
5. Bathrooms –the number of bathrooms in the house
6. Utilities – this describes the average monthly utility cost (in $)
7. Year Built – this describes the year when the house was constructed
8. Overall Condition – this is a rating of the overall condition of the house. The numeric scale of this rating is:
1 – Very Poor 2 – Poor 3 – Fair 4 – Average 5 – Good 6 – Excellent 7 – Very Excellent
9. HOA fee – this describes the monthly homeowner associate fee
10. Nearby School Rating – this is the rating of the property’s school zone
11. Price (in $) – this is the sales price of the houses
Tasks
Your client would like you to help her interpret and eventually predict the house price. So here are your tasks.
1. Estimate a multiple regression equation of house price.
2. Based on the results, what do you think would predict house price? For example, does school rating influence the price of the house? Explain your answers (make sure to report the appropriate statistics that inform your conclusions).
3. The client wants to invest in 10 properties that will have the highest house price in the future. She pre-selected 25 potential houses that appear to be good businesses. You job is to predict the prices of the 25 houses. The data on the 25 potential houses are contained in the “Prediction” sheet.
4. Based on your prediction, you need to make the decision on which 10 houses (i.e., ranked highest in price) to invest in.
Note:
a. Report your solUtions/answers to 1, 2, and 4 in the “Report” sheet.
b. For Task #3, fill in the predicted house prices in the “Prediction” sheet.
c. Create a new data sheet (or as many as necessary) to show the relevant output/results of your analyses. You can do this by copying and pasting the StatTools outputs. Highlight relevant results and add comments, where needed. Please know that only the relevant outputs should be included.

Hi,
I have three CSV files by different breed types, with data about, how much

Hi,
I have three CSV files by different breed types, with data about, how much time does it take for the dog to get rehomed – (rehomed column).
I want you to check based on the graphical summaries, try to propose distributions that might be reasonably used to model rehoming time for each breed (each csv file).
You can try checking the CSV file with Normal, Poisson, Exponential, Uniform, Binomial and Geometric Distribution and try find if anyone of these distribution is a good fit or not. If these Distribution are not good fit, explain me why they are not a good it, for each any every Distribution for all three CSV file.
You can check the suitability of any proposed models for the csv file with these testing including qq plot, Histogram, CDF, Hypotheses testing, Kolmogorov-Smirnov test, Shapiro-Wilk test, Chi-squared goodness of fit tests. You can also test with other testing methods too.

You have provide me with the R programming code and which model is used for that CSV file including why that model is a good fit model to the csv file or why all the above mentioned models are not good fit models for our individual csv file.
You have to do that for all three files.

Collect data from finance.yahoo.com and FRED database and use Microsoft Excel to

Collect data from finance.yahoo.com and FRED database and use Microsoft Excel to run a multivariate regression model for a publicly traded company using economic variables.
Round results to two decimal places when possible. If not, explain.
Use 30 observations for each variable. Same format for all variables.
Compute, analyze, and explain the following results from data.
Null hypothesis
Alternative hypothesis
F-test
R^2
Linear equation
Forecast
Multicollinearity test for all independent variables
Data must be formatted
Use 3 references to support regression. Include works cited page for references.
Include four parts
1.Choose between 3 to 5 valuation measures from finance.yahoo.com
2.Present the null and alternative hypothesis and explain the significance.
3.Introduce and analyze results with ANOVA table using a 5% significance level.
Identify which variables have the best predictive ability.
Explain significance of f test, t-statistic, and p-value.
Run forecast and determine if the company’s stock is undervalued or overvalued.
Explain significance if some of the p-values are below the significance level.
Explain significance of whether or not the t-values are too low.
4. Summarize and convey conclusions regarding the topic.

1. Developing and Understanding of Real Estate Prices
As you are aware, many fac

1. Developing and Understanding of Real Estate Prices
As you are aware, many factors (i.e., variables) have an impact on determining the price of a house. A few
well-known of these factors are size of the house (square feet), location, lot size, type of heating system, living
area, number of bathrooms and many more, as seen depicted in the datafile from a random sample of houses
for sale obtained from the Zillow listing of from North Florida. For your convenience, the data is seen in the
Excel template to easy your computation and analysis.
You are asked to study the data to inform a group of investors interested in buying a number of houses and
making recommendations to other buyers. It is clear that you can do quite a number of multiple regression
analyses to derive many insights from the data. However, for the sake of simplicity, please answers the
following questions.
a. Run a data analysis to derive multiple regression model to determine house price based on the following
most common five (5) factors, namely, Lot Size (given in acres) and Age, Land Value, Living Area, and # of
Rooms. Provide your ANOVA table and the key significant information (i.e., findings), and your interpretation
and recommendations.
b. Do you find any abnormalities with your model; Yes, or No? Write a brief paragraph (at least three to five
sentences to explain/justify your answer. Please be specific.
c. What interpretation would you attribute the Land Value factor?

Use the Design of Experiments & Cause-and-effect analysis to analyze the data; p

Use the Design of Experiments & Cause-and-effect analysis to analyze the data; probably they both need to make some charts to solve like the sample of using Pareto Analysis. The sample Pareto Analysis is finished. In addition, the format of answering 3 three questions is filling out the blanks under 5 subtopics, with some charts and words.

Open the document above to view the assignment
This part of the project is the f

Open the document above to view the assignment
This part of the project is the formal paper that summarizes your results. This assignment may look intimidating, but you can think of it as 3 problems. (one for each of your hypotheses) that are formally presented all together. These 3 problems resemble many problems covered in our course workbook, and written homework problems from Chapters 3 and 4. That is, we are using the process of statistical inference 3 times, on (# 4, 5, and 6) to make conclusions about population parameters (a single proportion, a difference of proportion, and a difference of means). What is different about this assignment is that the results are being presented in a more formal context.
Begin working on the project by completing #3, #4, and #5. You will need to know the results of these questions before you write the Abstract (#1).
There is no “rough draft” submission for this project. In other words, you will not have an opportunity to resubmit this assignment. You are expected to seek help if you need support with this assignment. If you need assistance with the statistics portion of the assignment (#3-8), you are encouraged to seek help from Mr. G (via email, or student hours) , or LARC for statistics tutoring. If you are seeking help writing the Abstract (after you have finished Parts 3, 4, and 5), you are encouraged to visit the writing center for support. They can read over your work to see if it is meeting the requirements in the project, and provide general guidance.
For some links to examples of parts of a research paper (Abstract, and Methods) go to Project Resource Links.
See the Grading RubricActions , for How Project Part 4 will Be Scored.
(this assignment is based on the question: How many minutes does it take you to get ready to leave the house for the day?)
Feed back from the professor to take into account:
Spreadsheet:You are missing data on preference for paper books. If you do not have this data, you will need to collect your data again. Gender was not a required variable.
One of the main purposes of Parts 1 and 2 of the project were to make sure you clearly understood the the questions we are analyzing. I attempted to explain this in the comment to your Part 2 submission, but it appears that you did not read this. Please read that comment I left in Part 2, and let me know if you have any questions at all.
Hypothesis testing:
You have a general misunderstanding of what the 3 hypotheses are testing.
Hypothesis 1 (as numbered in the assignment) is not about age, it is a single proportion of people who prefer paper books.
Hypothesis 2 is a difference in proportions of people who prefer paper books between the two age groups.
Hypothesis 3 is a difference in mean number of books read per month between the two age groups.
(I will attached the instructions to all the parts of this project to give you more clarity but only part 4 is due)