Let’s say that you find that there is a correlation of 0.95 between the population of a city and the number of people who eat out frequently. What does this mean, if anything, about your chances of success if you locate your restaurant in a big city rather than in a smaller city?

300-500 words (leaning more towards 500) cite any and all resources/references if used (not necessary)

Were the ads effective?

Let’s say that a US restaurant chain, Burrito Bell, has opened seven new restaurants in seven different towns, all of about the same size and economic status. To promote the openings, the chain invested in local television commercials in each town. The chain bought more ads in some towns than others and then compared the sales volumes of the restaurants. Here are the results:

Now the chain’s management wants to know if sales went up as the number of commercials increased. In other words, are sales and the number of commercials correlated—do the two variables have a linear association? Or, put another way, does an increase in one of the variables correspond with a consistent increase or decrease in the other variable?

To find out, we have to calculate their correlation.

Correlation

Correlation measures the relationship between two variables. It measures both the strength (how much they are related) and the direction (positive or negative) of the relationship. If there is a positive correlation, whenever the value of one variable increases or decreases, so does the other. If there is a negative correlation, whenever the value of one variable is high, the other has a low one.

Correlation can only be calculated under the following conditions:

• Both variables are quantitative.
• The relationship must be linear (rather than, for example, exponential).
• Outliers are left out, as they will distort the calculation.

You can check these conditions by drawing a scatter plot. Let’s try it for the advertising and sales data from Burrito Bell.

Burrito Bell Sales vs. Advertising

Let’s draw a scatter plot for Burrito Bell’s advertising and sales data. This will tell us if the data meets the conditions for calculating the correlation for these two variables.

As the explanatory variable, the number of ads goes along the X-axis and sales, as the responsive variable, should be plotted on the Y-axis.

Now, we plot the data points for each restaurant location.

So, does it look like their advertising had any impact on sales? From the scatterplot, it appears that there might be a positive relationship between these two variables. To find out for sure, we have to calculate the correlation coefficient for the data. With quantitative variables, a linear relationship (if a relationship at all) and no outliers, this data set qualifies for a correlation calculation.

The Correlation Coefficient

The correlation coefficient (or r) measures the strength and direction of the relationship between two variables on a scale of -1 to +1. Plus one indicates a perfect positive relationship. Minus one indicates a perfect negative relationship. In real life, nearly all correlation coefficients are less than perfect. That is, they fall between 0 and 1, or between 0 and minus 1.

Correlation at Burrito Bell

Let’s try this correlation calculation for the sample Burrito Bell data on commercials vs. per-restaurant sales, with number of commercials as our x variable and sales as our y variable.

1. First, find the mean and standard deviation for each data set. The mean number of commercials is 3 with an SD of 1.73. The mean sales are $25,000 with an SD of about 1.9 thousand dollars.
2. Second, find the distance of each variable from the mean as shown. Notice how some of the numbers are positive and some are negative. If both the x and y values have the same sign, it probably means that you have a positive correlation.
3. Third, find the product of each pair of differences as shown. Notice how when the signs are the same, the product is positive.
4. Fourth, add up the products, as shown.
5. Finally, divide the sum by the number of x and y observations and by the SD of x and the SD of y. This gives you r, the correlation coefficient.

The correlation coefficient of ads run and per-restaurant sales is .735 on a scale of -1 to +1. This means that there is a strong positive correlation between the number of ads and sales. The more ads the restaurants ran, the higher the sales seemed to be.

Correlation Caveats

Even if your data set allows you to calculate its correlation coefficient, there are a number of caveats you need to watch out for.

• Correlation does not mean causation. For example, just because there is a strong correlation between temperature and hotel occupancy does not necessarily mean that higher temperatures CAUSE higher hotel occupancy—it’s just that they happen to be related. Likewise, we can’t be sure that the ads run by Burrito Bell restaurants caused the higher sales.
• A correlation may be confounded. That is, changes observed in your correlated variables may be influenced, not so much by one another, as by some third, hidden variable. For example, there is a strong positive correlation between shoe size and reading ability in children. Does this mean that big feet cause children to read better? Or perhaps reading early in life promotes foot growth!

  It may just be that older children have larger feet, and they read better. Age is the confounding factor behind this correlation: it’s the main influence behind both of the correlated variables—shoe size and reading ability.

• When there is a random or confounded relationship between two correlated variables, the correlation is said to be spurious. Imagine, for example, an investor who finds a strong correlation between the number of goals scored by his favorite hockey team during a season and the performance of a major stock index. Or, a person may notice that the rainfall totals observed during a particular spring are highest on those days when she accidentally leaves her umbrella at home. There is clearly no reasonable relationship between the variables in these examples, but when analyzing data, spurious relationships can often be difficult to detect.

Conclusion

Correlation can be a powerful tool for summarizing the strength of a relationship between two quantitative variables. And while you can always calculate a correlation coefficient between variables, the question is how much meaning you derive from it. It is important to consider all of your variables carefully when drawing conclusions from data relationships.

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Assignment 2: Fran’s Virtual Fruit Stand, Part 2
Due Week 9 and worth 160 points

Use the concepts and scenario from Assignment 1 to help Fran’s Virtual Fruit Stand increase the functionality of its online shopping cart. When a customer checks out, the shopping cart must store the required data pertaining to each item the customer is buying. Your job is to design a program that will prompt the user for the required data and then store it. The required data includes the item name, the price per pound, and the number of pounds of that item being purchased. When complete, your program should include three (3) arrays, two (2) loops, one (1) and / or conditional statement, and one (1) variable.

Using Visual Logic, design a flowchart that is also a fully functional program to add functionality to the online shopping cart. According to your design, the program must:

1. Continually accept data regarding the purchase of fruit as specified below until the user enters a sentinel value, or until five (5) items are entered, whichever comes first.
2. Prompt the user for each item and accept the name of the fruit (e.g., “dried apples”), the price per pound, and the number of pounds sold in a month.
3. Store the required data in three (3) arrays (e.g., one (1) for the item name, one (1) for the price per pound, and one (1) for the number of pounds being purchased) with corresponding index values. Note: For example, index value 3, when applied to the “itemName” array, would give us the name of the third item that the customer is buying. That same index value of 3, when applied to the “pricePerPound” array, would give us the price per pound of that same third item that the customer is buying.
4. Store up to five (5) values in each of the three (3) arrays.
5. Provide functionality in which the user ends the program by typing a sentinel value, and the program tells the user what the sentinel value is. Note: An acceptable message may read “Type n to end the program.”, where “n” is the sentinel value. If the user does not end the program in this way, then the program ends when it has collected the data for five (5) items.
6. Print an itemized receipt with the following data after the last item in the purchase has been saved to the array.
   • Item name
   • Price per pound of each item
   • Number of pounds purchased of each item
   • Subtotal price for each item, calculated as price per pound multiplied by the number of pounds
   • Total weight of the entire order (in pounds)
   • The cost of shipping which is based on the total weight of the entire order, calculated as 50 cents per pound. Note: For example, if the entire order weighs seven (7) pounds, the cost of shipping would be $3.50.
   • Grand total price of all items and shipping.

Note: Use the “console” option in the output command to accomplish this task. An example of an acceptable output has been provided below:

Assume the role of Marketing Manager. Select a product (good or service) that is sold in the United States and has sales opportunities in a foreign market. Apply your critical thinking skills and the knowledge you have acquired throughout this course and address the following in your Final Paper: 

Describe the product you selected in terms of the four utilities of customer value. 

Identify the product’s target market at home and in your stated foreign market.

Indicate the competition of the product category in both home and foreign markets.

Explain how you would apply the segmentation, targeting, and positioning (STP) approach to market the product in the foreign market.

Discuss the major environmental facts and trends in the foreign markets that might affect sales of the product.

Explain how you would develop, execute. and measure a campaign for this product considering the four p’s (product, price, promotion, and place).

Discuss the U.S. and international ethical marketing considerations.

Writing the Final Paper 

The Final Paper: 

1. Must be eight- to ten- double-spaced pages in length, and formatted according to APA style as outlined in the Ashford Writing Center (not including the title and reference pages). 

2. Must include a title page with the following:

a. Title of paper 

b. Student’s name 

c. Course name and number 

d. Instructor’s name 

e. Date submitted 

3. Must begin with an introductory paragraph that has a succinct thesis statement. 

4. Must address the topic of the paper with critical thought. 

5. Must end with a conclusion that reaffirms your thesis. 

6. Must use at least five scholarly references in addition to the course text. 

7. Must document all sources in APA style

I need help in managment exam . 

Two essay question 

start in one hour

and I have 1:15 min duration 

 Project selection (selection methods), network scheduling (CPM and PERT), and essay questions (e.g, what is WBS?  Why is WBS important?).