You will receive $2,000 on January 1st 2004, on January 1st in 2005 and January 1st 2006. Which of the following expressions will calculate your value at time of January 1st 2004?

a

PV = $2,000​[1.06]^-1 + $2,000​[1.06]^-2 + $2,000​[1.06]^-3

b

PV = $2,000​[1.06]^1 + $2,000​[1.06]^2 + $2,000​[1.06]^3

c

PV = $2,000​[1.06]^0 + $2,000​[1.06]^1 + $2,000​[1.06]^2

d

PV = $2,000 + $2,000​[1.06]^-1 + $2,000​[1.06]^-2

Suzanne opens an account at a local bank on January 1, 2004 with a deposit of 4000 dollars. On October 1, 2004 she withdraws 1430 dollars. On April 1, 2005 she withdraws 850 dollars. And on April 1, 2007 she deposits 2090 dollars. Find the total present value of these transactions on July 1, 2006, if the account earns interest at a nominal rate of 7.6 percent convertible quarterly.

The solutions I've found on the website have not taken into account the future deposit and are incorrect, any suggestions?

1. Use the data below to find the linear regression equation that best represents the given data and predict the revenue in 2013 (Copy data to Excel)

2. Then create Two new columns that represent the predication y =mx+b for each year and percent of growth for each year = (Revenue/Predication)*100

3. Use Excel to graph the linear model (x-axis years, y-axis revenue) and the linear equation of best fit.

1. Fill out the missing entry for prediction and percent of growth
2. Find the value of the linear correlation coefficient r.
3. Find the equation of the regression line, letting Number of years be the independent (x) variable.
4. Find the coefficient of determination.
5. Find the standard error of estimate s_e.
6. Report the result in APA format
7. Show work in Excel if possible

Listed below is the number of movie tickets sold at the Library Cinema-Complex, in thousands, for the period from 2004 to 2016. Compute a five-year weighted moving average using weights of 0.1, 0.1, 0.2, 0.3, and 0.3, respectively. Describe the trend in yield. (Round your answers to 3 decimal places.)

The weighted moving averages are:

Suppose retailers would like to forecast the percentage of customers who plan to purchase gift cards during the upcoming holiday season. The following data show this percentage from 2002 to 2009. The data is as follows:

Perform the following:

1. Using a 3-period simple moving average, forecast the percentage of holiday shoppers who will purchase a gift card in 2010.

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2. Calculate the MAD for the forecast in part a.

3. Using a 3-period weighted moving average with the weights 5, 3, and 1, forecast the percentage of holiday shoppers who will purchase a gift card in 2010.

4. Calculate the MAD for the forecast in part c.

5. In which forecast do you have the most confidence?

American customer satisfaction index: Starbucks in the U.S. 2006-2016

1. Plot this set of data as a scatterplot in excel. Insert excel graph here:

2. Find the correlation coefficient.

3. Is it positive or negative?

4. What does the sign tell us?

5.What does the correlation imply about the relationship between the time and the satisfaction?

6. Is the correlation significant? Why or why not? (Answer in 1-2 complete sentences.) (Use the Pearson calculator).

7. Draw the trendline in excel. Can the regression line be used for prediction? No, it is too weak. Insert excel graph here:

74% of freshmen entering public high schools in 2006 graduated with their class in 2010. A random sample of 81 freshmen is selected. Find the probability that the proportion of students who graduated is greater than 0.750 . Write only a number as your answer. Round to 4 decimal places (for example 0.1048). Do not write as a percentage.

Data sets for daily high temperatures (in °F) in the months of August and February of 2015, 2017, and 2019:

August 2015: 80; 81; 85; 87; 84; 80; 75; 82; 83; 85; 72; 78; 79; 85; 88; 89; 90; 90; 86; 84; 80; 79; 80; 85; 79; 74; 71; 77; 83; 88; 85

February 2015: 31; 33; 23; 37; 33; 22; 37; 42; 36; 29; 32; 32; 15; 24; 12; 12; 23; 28; 15; 13; 24; 37; 30; 23; 33; 23; 22; 27

August 2017: 86; 87; 85; 86; 74; 73; 66; 79; 82; 82; 78; 81; 80; 76; 78; 81; 85; 82; 83; 81; 81; 85; 88; 74; 74; 74; 76; 73; 63; 76; 72

February 2017: 39; 35; 27; 30; 38; 45; 50; 53; 33; 27; 44; 38; 36; 43; 43; 30; 38; 62; 64; 49; 48; 57; 70; 75; 67; 36; 50; 64

August 2019: 85; 86; 85; 86; 84; 87; 82; 82; 79; 77; 79; 84; 83; 83; 82; 83; 86; 89; 87; 87; 86; 85; 74; 75; 76; 76; 72; 79; 76; 82; 76

February 2019: 18; 31; 46; 59; 58; 40; 54; 47; 27; 31; 35; 33; 34; 38; 55; 35; 39; 34; 28; 32; 48; 39; 41; 44; 37; 30; 31; 30

1. Compute 99% confidence intervals for the average daily maximum temperature in February for each of the three years of the above data. How do they compare?
2. Compute 99% confidence intervals for the average daily maximum temperature in August for each of the three years in which you collected data. How do they compare?