Posts under category Statistics - Your Online Class Tutors

Posts under category Statistics

Does the knowledge of being in a high-risk flood area affect housing prices?
Use the summer 2021 floods and flood risk disclosure in London as an experiment (more info here) which lead to an increase in interest in floods as seen on Google Trends. Some areas, such as those flooded, will have more impact than others. You can download data for the flood risk maps from here. You can implement a DD using areas exposed vs non-exposed before and after the event. You can find a related paper on this matter here.

assignment prompt.docx

Confidence Intervals and Hypothesis Testing
Questions 10 through 12 are based on hypothesis testing and confidence intervals. In the different textbooks you will find many criteria about when to use normal distribution and t distribution for solving these problems. Some of the criteria defined are based on approximation that when sample size is large, t distribution can be approximated to normal. However, here when you are using technology to solve problem, stick to criteria that when population standard deviation is known, normal distribution is used. In all other cases or respective of sample sizes, t distribution is used.
Q10) Consider variable overall satisfaction with the dining facility variable.
a. At 90% confidence, estimate the overall satisfaction with the fining facility.
b. At a 10% significance level, can we conclude overall satisfaction is different from 3.5? Explain criteria used to reach conclusion.
Q 11) Let’s perform analysis on satisfaction based on frequency of visit variable. You may find table completed in Question 4 useful for this analysis. At 5% significance level, test the hypothesis that those who visit dining facility daily are less satisfied that those who to not visit facility daily, In the answers provide test statistics, P value and conclusion. Use subscript 1 for daily visit and 2 for those who don’t visit dining facility daily.
This question is an example of inferential statistics that can be addressed by our expert writers.

statistics help online question.docx

All students and professionals ought to understand statistical concepts because it help them work on their research projects and conduct different research activities within environments they live. Statistics, research, and probability skills might seem difficult to master at fast, but they are interesting when one has mastered the art. My course tutor provides students with assistance who might find it challenging to understand statistical concepts.
A simple presentation of statistics is explained here:
Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. Statistics has a wide range of uses and applications in many different fields, such as business, economics, psychology, medicine, and the natural and social sciences. Some examples of the uses of statistics are:

  1. Data Analysis: Statistics enables researchers and analysts to collect and analyze data, and to make inferences and predictions based on the data.
  2. Quality Control: Statistics is used to monitor and control the quality of products and services, by using statistical techniques such as statistical process control and design of experiments.
  3. Surveys and Sampling: Statistics is used to design and analyze surveys and samples, to estimate the characteristics of a population based on a sample.
  4. Forecasting and Predictions: Statistics is used to make predictions and forecasts in areas such as weather forecasting, stock market analysis, and economic forecasting.
  5. Decision Making: Statistics is used to support decision making in a wide range of applications, such as medical research, product design, and public policy.
  6. Research: Statistics is used in many scientific fields to design experiments, collect data, and analyze and interpret the results.
    Statistics is important because it allows us to make inferences about a population based on a sample, and to make predictions about future events based on past data. It also allows us to test hypotheses and make decisions based on data, and to evaluate the reliability and validity of the conclusions we draw from data. Without statistics, it would be difficult to make sense of the vast amount of data that is generated in today's world and make informed decisions.

Descriptive statistics is a branch of statistics that deals with summarizing, describing, and presenting data. The goal of descriptive statistics is to provide a concise summary of a large amount of data in a way that is easy to understand and interpret. This can be done by using various tools such as measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation) to summarize the data, and by using visualizations such as histograms, box plots, and scatter plots to present the data in a clear and visually appealing way.

Descriptive statistics can be used to answer questions such as: what is the average value of a certain variable? What is the most common value? How much does the data vary? And it can be applied to any type of data, such as numerical, categorical, ordinal, etc. Descriptive statistics can help to understand the nature of the data and to identify patterns or outliers. However, it is important to note that descriptive statistics is not the same as inferential statistics, which deals with making inferences about a population based on a sample of data.

Collecting and analyzing large sets of data from various sources, such as sales data, website analytics, and customer behavior data.

Use statistical and data analysis techniques to identify patterns and trends in the data, and then turn that data into actionable insights.

Communicating findings and insights to the broader team, including marketing, sales, and product development, in order to inform business decisions and strategies.

Building and maintaining databases and data systems, ensuring data quality and accuracy.
Creating dashboards and visualizations that help team members easily understand complex data.

Collaborating with different departments to identify areas where data analysis can improve processes and drive business results.

Supporting forecasting and budgeting activities by providing insight and data-driven recommendations.

Communicate with customer service team to understand customer's complaints and their needs.

Use data to evaluate marketing campaigns, from customer acquisition to retention, and make recommendations for future efforts.

Continuously keep track of industry trends and competitors, and monitor company’s performance data in real-time to adjust the plan accordingly.