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 the following three factors are critical: 1. Yesterday's attendance 2. Weekday or Weekend 3. Predicted Weather He then took a random sample of 40 days. For each day, he recorded the attendance, day of the week, and weather forecast. The first independent variable is interval, but the other two are nominal. Accordingly, he created the following sets of indicator variables:

I1 = 1- (IF WEEKEND) 0 - (IF NOT)

I2 = 1 - (IF MOSTLY SUNNY IS PREDICTED) 2 - (IF NOT)

I3 = 1 - (IF RAIN IS PREDICTED) 2 - (IF NOT)

A. Conduct regression analysis

B. Is model valid? Explain

C. Can we conclude weather is a factor in determining attendance

D. Do these results provide sufficient evidence that weekend attendance is, on average, larger than weekday attendance?  

I will attach data in another question

#1. [Water Slide & Swing] You are designing a slide for a water park. In a sitting position, park guests slide a vertical distance h down the water slide, which has negligible friction. When they reach the bottom of the slide, they grab a handle at the bottom end of a 6.00-m-long uniform pole. The pole hangs vertically, initially at rest. The upper end of the pole is pivoted about a stationary, frictionless axle. The pole with a person hanging on the end swings up through an angle theta max , and then the person lets go of the pole and drops into a pool of water. Treat the person as a point mass. The pole’s moment of inertia is given by I = (1/3)ML , where L = 6.00 m is the length of the pole and M = 24.0 kg is its mass. In your design, a person of mass m = 70.0 kg is to have a maximum angle of swing of theta max = 72.0˚ after their collision with the pole.

(a) In the "collision" between the slider and the pole, why is angular momentum about the pole's pivot conserved, but linear momentum and kinetic energy are not conserved? Assume that the slider is moving horizontally when they grab the handle on the vertically hanging pole.

(b) What is the angular speed of rotation of the pole & swinger just after the swinger grabs on, in terms of the final height the swinger reaches?

(c) What is the speed of the swinger at the bottom of the slide just before reaching the pole, in terms of their speed just after grabbing the pole?

(d) For a person of mass m = 70.0 kg, what must be the starting height h in order for the pole with person to have a maximum angle of swing of theta max =  72.0˚ after the collision?

For questions 5 – 6, assume that to ride the Whirling Dervish at an amusement park, riders must be no taller than 75 in. Assume that men have normally distributed heights with a mean of 70 in. and a standard deviation of 2.8 in. 5. Find the percentage of men who will not meet the height requirement. Round to two percentage decimal places (for example, 38.29%). 6. If the height requirement is changed so that only the tallest 5% of men will be excluded from riding the Whirling Dervish due to height restrictions, what is the new height limit? Round to the nearest inch.

For questions 5 – 6, assume that to ride the Whirling Dervish at an amusement park, riders must be no taller than 75 in. Assume that men have normally distributed heights with a mean of 70 in. and a standard deviation of 2.8 in. 5. Find the percentage of men who will not meet the height requirement. Round to two percentage decimal places (for example, 38.29%). 6. If the height requirement is changed so that only the tallest 5% of men will be excluded from riding the Whirling Dervish due to height restrictions, what is the new height limit? Round to the nearest inch.

The following data show the length of the coasters at the Mega Park (x) and height of the same coasters (y). The regression equation for the data is given by y = 21.94 + 0.018x

a.      State and interpret the slope in the context of this problem, given the regression equation above.

b.      How tall does the linear regression model predict a coaster of 3450 feet long will be?

c.       Find and interpret the residual for the coaster which is 3450 feet long and has a height of 100 feet?

1. A politician wants to open a new park in the middle of town. She needs to make sure that the local community supports the plan. A nearby town recently found that 60% of the community supported a similar plan and she believes that the same proportion will support it in her district. She takes a random survey of 40 people and find that the 10 people are supportive of her plan. She wants to be certain that this is not just due to statistical error. She is going to use an alpha of .01 for her study.
   1. What is the null and alternative hypotheses? (2)
   2. What are the critical values for this study? Please list all relevant information. (2)
   3. What is the statistic for the sample? (1)
   4. What is the P value for this sample (2)
   5. Do we reject the null hypotheses? Please interpret the hypotheses. (3)

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?

A man is hiking at a park. At the beginning, he followed a straight trail. From the starting point, he traveled two miles down the first trail. Then he turned to his left by 30 degree angle to follow a second trail for one point five miles. Next, he turned to his right by 160 degree angle and follow a third trail for one point seven miles. At this point he was getting very tired and would like to get back as quickly as possible, but all of the available trails seem to lead him deeper into the woods. He would like to take a shortcut directly through the woods. How far to his right should you suggest him to turn, and how far do he have to walk, to go directly back to his starting point?

Q1: The man has to turn ____ degree to the right and walk ___ miles to the starting point.

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