Suppose a group of volunteers is planning on building a park near a local lake. The lake is known to contain low levels of arsenic (As). Therefore, prior to starting construction, the group decides to measure the current level of arsenic in the lake.

a) If a 15.7 cm3 sample of lake water is found to have 164.5 ng As, what is the concentration of arsenic in the sample in parts per billion (ppb), assuming that the density of the lake water is 1.00 g/cm3?

One of the volunteers suggests hiring an on-site water treatment company to remove the arsenic from the lake. The company claims their process takes 2.74 days to remove 41.90 kg of As from a water source.

b) Calculate the total mass (in kg) of arsenic in the lake that the company will have to remove if the total volume of water in the lake is 0.710 km3?

c) Based on the company\'s claim and the concentration of arsenic in the lake, how many years will it take to remove all of the arsenic from the lake, assuming that there are always 365 days in a year?

The manager of an amusement park would like to be able to predict daily attendance in order to develop more accurate plans about how much food to order and how many ride operators to hire. After some consideration, he decided that the following three factors are critical:
Yesterday’s attendance
Weekday or weekend (1 if weekend, 0 if weekday)
Predicted weather
Rain forecast ( 1 if forecast for rain, 0 if not)
Sun ( 1 if mostly sunny, 0 if not)
He then took a random sample of 40 days. For each day, he recorded the attendance, the previous day’s attendance, day of the week, and weather forecast. An example of the first few lines of Data and the regression output are below:
Attendance   Yest Att   I1   I2   I3
7882   8876   0   1   0
6115   7203   0   0   0
5351   4370   0   0   0
8546   7192   1   1   0

SUMMARY OUTPUT                  
                      
Regression Statistics                  
Multiple R   0.836766353                  
R Square   0.700177929                  
Adjusted R Square   0.665912549                  
Standard Error   810.7745532                  
Observations   40                  
                      
ANOVA                      
    df   SS   MS   F   Significance F  
Regression   4   53729535   13432384   20.43398   9.28E-09  
Residual   35   23007438   657355.4          
Total   39   76736973                 
                      
    Coefficients   Standard Error   t Stat   P-value   Lower 95%   Upper 95%
Intercept   3490.466604   469.1554   7.439894   1.04E-08   2538.031   4442.903
Yest Att   0.368547078   0.077895   4.731349   3.6E-05   0.210412   0.526682
I1   1623.095785   492.5497   3.295294   0.002258   623.1668   2623.025
I2   733.4646317   394.3718   1.85983   0.071331   -67.1527   1534.082
I3   765.5429068   484.6621   -1.57954   0.123209   -1749.46   218.3734
Test to see if the model is valid. Use alpha = .05
Can we conclude that weather is a factor in determining attendance?
If the manager is looking for a way to help predict attendance, Is this a good model to use? How would you suggest making this model better?

please give proper details for the answer. Thank you

The manager of an amusement park would like to be able to predict daily attendance in order to develop more accurate plans about how much food to order and how many ride operators to hire. After some consideration, he decided that the following three factors are critical: Yesterday’s attendance Weekday or weekend Predicted weather He then took a random sample of 36 days. For each day, he recorded the attendance, the previous day’s attendance, day of the week, and weather forecast(mostly sunny, rain, cloudy). The first independent variable is interval, but the other two are nominal. a. Create the three indicator variables you need. b. Conduct a regression analysis. c. Is this model valid? Explain. d. Can we conclude that weather is a factor in determining attendance? e. Do these results provide sufficient evidence that weekend attendance is, on average, larger than weekday attendance? f. Do these results provide sufficient evidence that mostly sunny attendance is, on average, larger than cloudy attendance?

Dandy's Fun Park is evaluating the purchase of a new game to be located on its Midway.? Dandy's has narrowed their choices down to? two: the Wacky Water Race game and the

Whackminus?Aminus?Mole

game. Financial data about the two choices follows.

What is the total present value of future cash inflows and residual value from the

Whackminus?Aminus?Mole

?game?

The health of the bear population in a park is monitored by periodic measurements taken from anesthetized bears. A sample of the weights of such bears is given below. Find a​ 95% confidence interval estimate of the mean of the population of all such bear weights. The​ 95% confidence interval for the mean bear weight is the following.

data table 80 344 416 348 166 220 262 360 204 144 332 34 140 180

The purpose of this homework is to test your knowledge of GUI. Consider a fictional park where the entry price for 1 adult ticket is $50, and for 1 children ticket is $25. Write a simple GUI application that let user to enter the number of tickets and display the total price. The GUI should contain:

● One text field for the user to enter the number of adult tickets

● One text field for the user to enter the number of children tickets

● One button “Calculate total cost”

● One text field to display the total cost When the user clicks the button then the correct cost is displayed in the total price field. If the input text field is empty then it should be treated as 0 tickets.

A quality control activity analysis indicated the following four activity costs of a hotel:

Sales are $3,900,000. Prepare a cost of quality report. Round percent of sales to one decimal place.

How has the Four Seasons Hotels operationalized a Transnational Strategy in order to gain competitive advantage in the global high-end luxury hotel industry?

If Four Season wants to diversify how you would suggest to its CEO to proceed?

What other business has synergy with their core existing business?

Please answered these three questions in a detailed manner.

In this problem, assume that the distribution of differences is approximately normal. Note: For degrees of freedom d.f. not in the Student's t table, use the closest d.f. that is smaller. In some situations, this choice of d.f. may increase the P-value by a small amount and therefore produce a slightly more "conservative" answer. Suppose that at five weather stations on Trail Ridge Road in Rocky Mountain National Park, the peak wind gusts (in miles per hour) for January and April are recorded below. Wilderness District 1 2 3 4 5 January 140 123 123 64 78 April 102 111 104 88 61 Does this information indicate that the peak wind gusts are higher in January than in April? Use α = 0.01. Solve the problem using the critical region method of testing. (Let d = January − April. Round your answers to three decimal places.) test statistic = critical value = Interpret your conclusion in the context of the application. Reject the null hypothesis, there is sufficient evidence to claim average peak wind gusts are higher in January. Fail to reject the null hypothesis, there is sufficient evidence to claim average peak wind gusts are higher in January. Reject the null hypothesis, there is insufficient evidence to claim average peak wind gusts are higher in January. Fail to reject the null hypothesis, there is insufficient evidence to claim average peak wind gusts are higher in January. Compare your conclusion with the conclusion obtained by using the P-value method. Are they the same? We reject the null hypothesis using the critical region method, but fail to reject using the P-value method. The conclusions obtained by using both methods are the same. We reject the null hypothesis using the P-value method, but fail to reject using the critical region method.