Statistics

Statistics
Instructions
You are to write a comprehensive report showing the details (formulas, calculations) of the steps taken in answering each of the questions. A minimum of 1600 and a maximum of 2000 words (excluding title page, contents, and reference list) are required for this paper.

There are two sections in this paper. Students are required to answer ALL questions in section A. In section B students are required to do question 6 and any other question. So, in total, seven questions to be answered.

NOTE: For ALL questions show the different steps leading to the answer. If you use JASP (you should use Jasp wherever it is possible, you can download it by the following link https://jasp-stats.org/download/ ), show all the output including the relevant tables and explain in detail the meaning of the output. For other calculations that do not require JASP, show the calculated steps leading to the answer.

Examples (all questions that need to be answered can be found in the attached file)

Part A

3. An analysis of the monthly incentives received by 5 salesmen: The mean and median of the incentives is $7000. The only mode among the observations is $12,000. Incentives paid to each salesman were in full thousands. What is the difference between the highest and the lowest incentive received by the 5 salesmen in the month?

4. The weight of bags of apples is normally distributed with a mean of 10kg and a standard deviation of 1kg. The weight of sack of carrots is also normally distributed with a mean of 10kg and a standard deviation of 250g. Based on this information, which product will yield the highest probability of getting a very heavy sack?

5. Suppose Stock A has had a mean price of $6.58 per share with a standard deviation of $1.88, and Stock B has had a mean price of $10.57 per share with a standard deviation of $3.02. Which stock is more volatile?

Part B

Question 6

The dataset House_Price is required to answer this question. For all sub-questions in this question show and explain ALL the relevant JASP output.
a) The Grey Road property development company have the option of acquiring land in central Somerset. They are thinking of developing a mix of middle to high level housing units, however they are doing preliminary analysis to see which factors are relevant in property pricing. Previous analysis has shown a number of factors have an impact in this regard. You have been chosen as analyst to look investigate if it makes a difference whether there is a nearby airport, water bodies (such as lakes, rivers) or both. They have collected data (House_Price) from a similar location and have asked you as an analyst to verify this for them. Using JASP, and the House_Price dataset, test whether the presence of an airport and water bodies (lake, river, both), makes a difference in terms of the price of a property.
b) The Grey road property company have also collected a lot of data on various factors which are connected with pricing of properties. The have tasked you to look into the relationships between these factors and determine which are significantly related to each other. In addition they would like for you to provide an explanation of the relationships if any between the factors. Using JASP, determine based on the House_Price dataset the relationship between the following factors, crime rate, residential area, air quality, number of rooms, age of house, district located, and rainfall level. Is any of these relationships significant? Provide an explanation for the relationships that are significant
c) The grey road property company has come to understand from preliminary analysis that crime rate, residential area, air quality, number of rooms, age of house, district located, rainfall level, parks and number of hospital beds are some of the key factors that determine house prices in Somerset. They like all private businesses have a limited amount of resources and would therefore like to focus on the most important factors and leverage that to maximize the returns on their investment. They would like for you as an analyst using the House_Price dataset to determine and rank in order of strength the factors that best predict house prices.

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