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F(r,s,t) = r(7s^2+5t^4)
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Hello, which variable is being differentiated?
Example: f(x,y)= x^2+y
partial derivative only to x is,
f'(x)=2x
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Colorado State University Global Springdale Shopping Survey Analysis
The major shopping areas in the community of Springdale include Springdale Mall, West Mall, and the downtown area on Main ...
Colorado State University Global Springdale Shopping Survey Analysis
The major shopping areas in the community of Springdale include Springdale Mall, West Mall, and the downtown area on Main Street. A telephone survey has been conducted to identify strengths and weaknesses of these areas and to find out how they fit into the shopping activities of local residents. The 150 respondents were also asked to provide information about themselves and their shopping habits. The data are provided in the file SHOPPING. The variables in the survey can be found in the file CODING.
In this exercise, some of the estimation techniques presented in the module will be applied to the Springfield Shopping survey results. You may assume that these respondents represent a simple random sample of all potential respondents within the community, and that the population is large enough that application of the finite population correction would not make an appreciable difference in the results.
Managers associated with shopping areas like these find it useful to have point estimates regarding variables describing the characteristics and behaviors of their customers. In addition, it is helpful for them to have some idea as to the likely accuracy of these estimates. Therein lies the benefit of the techniques presented in this module and applied here.
Item C in the description of the data collection instrument lists variables 7, 8, and 9, which represent the respondent’s general attitude toward each of the three shopping areas. Each of these variables has numerically equal distances between the possible responses, and for purposes of analysis they may be considered to be of the interval scale of measurement.
Determine the point estimate, and then construct the 95% confidence interval for µ7 = the average attitude toward Springdale Mall.
Repeat part (a) for µ8 and µ9, the average attitudes toward Downtown and West Mall, respectively.
Given the breakdown of responses for variable 26 (sex of respondent), determine the point estimate, and then construct the 95% confidence interval for p26 = the population proportion of males.
Given the breakdown of responses for variable 28 (marital status of respondent), determine the point estimate, and then construct the 95% confidence interval for p28 = the population proportion in the “single or other” category.
Assume the managers have requested estimates of the mean attitudes towards each mall with a margin of error of 0.05 for each. If the managers want to have 95% confidence that the sample mean will fall within this margin of error, how large should the sample size be for each mall?
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Create a text vector called Months with names of the 12 months of the year. Create a numeric vector Summer, with Calendar month index positions for the summer months (inclusive, with 4 elements in all). Use vector indexing to extract the text values of Months, indexed by Summer. Multiply Summer by 3. What are the values of Months, when indexed by Summer multiplied by 3? Why do you get that answer? What is the mean (average) summer month, as an integer value? Which value of Months corresponds to it? Why do you get that answer? Use the floor() and ceiling() functions to return the upper and lower limits of Months for the average Summer month. (Hint: to find out how a function works, use R help if needed.) E-commerce Data for Exercises The data set comprises responses to intercept surveys asked when users visited the site, along with data about each user’s site activity such as number of pages visited and whether a sale was completed. Identifying details for the site and customers have been removed but the observations otherwise are actual data. We will load the data set first, and then explain a few of its observations. To load the data from CSV format, use the following command (or load ecommerce-data.csv from a local location if you have downloaded it, as noted in Section 1.6.3). ecomm.df <- read.csv("https://goo.gl/hzRyFd") summary(ecomm.df) How many observations and variables are in the e-commerce data set? Compute a frequency table for the country of origin for site visits. After the United States, which country had the most visitors? Compute a two-way frequency table for the intent to purchase (intentWasPlanningToBuy), broken out by user profile. What are the proportions of parents who intended to purchase? the proportions of teachers who did? For each one, omit observations for whom the intent is unknown (blank). Among US states (recorded in the variable region), which state had the most visitors and how many? Solve the previous problem for the state with the most visitors, using the which.max() function (or repeat the same answer, if you already used it). Draw a histogram for the number of visits to the site (behavNumVisits). Adjust it for more detail in the lower values. Color the bars and add a density line. Draw a horizontal boxplot for the number of site visits. Which chart from the previous two exercises, a histogram or a boxplot, is more useful to you, and why? Draw a boxplot for site visits broken out with a unique row for each profile type. (Note: if the chart margins make it unreadable, try the following command before plotting: par(mar=c(3, 12, 2, 2)). After plotting, you can use the command par(mar=c(5, 4, 4, 2) + 0.1) to reset the chart margins.) *Write a function called MeanMedDiff that returns the absolute difference between the mean and the median of a vector. *What is the mean-median difference for number of site visits? *What is the mean-median difference for site visits, after excluding the person who had the most visits?
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Colorado State University Global Springdale Shopping Survey Analysis
The major shopping areas in the community of Springdale include Springdale Mall, West Mall, and the downtown area on Main ...
Colorado State University Global Springdale Shopping Survey Analysis
The major shopping areas in the community of Springdale include Springdale Mall, West Mall, and the downtown area on Main Street. A telephone survey has been conducted to identify strengths and weaknesses of these areas and to find out how they fit into the shopping activities of local residents. The 150 respondents were also asked to provide information about themselves and their shopping habits. The data are provided in the file SHOPPING. The variables in the survey can be found in the file CODING.
In this exercise, some of the estimation techniques presented in the module will be applied to the Springfield Shopping survey results. You may assume that these respondents represent a simple random sample of all potential respondents within the community, and that the population is large enough that application of the finite population correction would not make an appreciable difference in the results.
Managers associated with shopping areas like these find it useful to have point estimates regarding variables describing the characteristics and behaviors of their customers. In addition, it is helpful for them to have some idea as to the likely accuracy of these estimates. Therein lies the benefit of the techniques presented in this module and applied here.
Item C in the description of the data collection instrument lists variables 7, 8, and 9, which represent the respondent’s general attitude toward each of the three shopping areas. Each of these variables has numerically equal distances between the possible responses, and for purposes of analysis they may be considered to be of the interval scale of measurement.
Determine the point estimate, and then construct the 95% confidence interval for µ7 = the average attitude toward Springdale Mall.
Repeat part (a) for µ8 and µ9, the average attitudes toward Downtown and West Mall, respectively.
Given the breakdown of responses for variable 26 (sex of respondent), determine the point estimate, and then construct the 95% confidence interval for p26 = the population proportion of males.
Given the breakdown of responses for variable 28 (marital status of respondent), determine the point estimate, and then construct the 95% confidence interval for p28 = the population proportion in the “single or other” category.
Assume the managers have requested estimates of the mean attitudes towards each mall with a margin of error of 0.05 for each. If the managers want to have 95% confidence that the sample mean will fall within this margin of error, how large should the sample size be for each mall?
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Purdue University Global Business Strategy Markups & Markdowns Essay
Your business is off to a good start and you are ready to sell some goods. You must decide on your pricing strategy. Choos ...
Purdue University Global Business Strategy Markups & Markdowns Essay
Your business is off to a good start and you are ready to sell some goods. You must decide on your pricing strategy. Choose an item your business will sell and find the selling price of that item online from a competitor’s website. Describe the item and include the URL to the item you are selling in your post. Use an internet search engine to find the average markup percentage on this type of item. For example, search the average markup on lawnmowers. State what percentage you found. Use that markup percentage to calculate the cost and dollar markup of the item. Show all calculations.How satisfied are you with your markup? Using several sentences discuss what markup strategy you intend to use. Will you stick with the average industry markup or go with something else? Will you calculate all your markups by hand or use a different strategy to calculate?
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Boston University Database Management and Modeling Discussion
Create a text vector called Months with names of the 12 months of the year. Create a numeric vector Summer, with Calendar ...
Boston University Database Management and Modeling Discussion
Create a text vector called Months with names of the 12 months of the year. Create a numeric vector Summer, with Calendar month index positions for the summer months (inclusive, with 4 elements in all). Use vector indexing to extract the text values of Months, indexed by Summer. Multiply Summer by 3. What are the values of Months, when indexed by Summer multiplied by 3? Why do you get that answer? What is the mean (average) summer month, as an integer value? Which value of Months corresponds to it? Why do you get that answer? Use the floor() and ceiling() functions to return the upper and lower limits of Months for the average Summer month. (Hint: to find out how a function works, use R help if needed.) E-commerce Data for Exercises The data set comprises responses to intercept surveys asked when users visited the site, along with data about each user’s site activity such as number of pages visited and whether a sale was completed. Identifying details for the site and customers have been removed but the observations otherwise are actual data. We will load the data set first, and then explain a few of its observations. To load the data from CSV format, use the following command (or load ecommerce-data.csv from a local location if you have downloaded it, as noted in Section 1.6.3). ecomm.df <- read.csv("https://goo.gl/hzRyFd") summary(ecomm.df) How many observations and variables are in the e-commerce data set? Compute a frequency table for the country of origin for site visits. After the United States, which country had the most visitors? Compute a two-way frequency table for the intent to purchase (intentWasPlanningToBuy), broken out by user profile. What are the proportions of parents who intended to purchase? the proportions of teachers who did? For each one, omit observations for whom the intent is unknown (blank). Among US states (recorded in the variable region), which state had the most visitors and how many? Solve the previous problem for the state with the most visitors, using the which.max() function (or repeat the same answer, if you already used it). Draw a histogram for the number of visits to the site (behavNumVisits). Adjust it for more detail in the lower values. Color the bars and add a density line. Draw a horizontal boxplot for the number of site visits. Which chart from the previous two exercises, a histogram or a boxplot, is more useful to you, and why? Draw a boxplot for site visits broken out with a unique row for each profile type. (Note: if the chart margins make it unreadable, try the following command before plotting: par(mar=c(3, 12, 2, 2)). After plotting, you can use the command par(mar=c(5, 4, 4, 2) + 0.1) to reset the chart margins.) *Write a function called MeanMedDiff that returns the absolute difference between the mean and the median of a vector. *What is the mean-median difference for number of site visits? *What is the mean-median difference for site visits, after excluding the person who had the most visits?
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