1. Name a few characteristics making data research and analysis in healthcare a separate industry. Think holistically and consider scientific, policy and social impact sides.

2. Explain how processed and analyzed data is used in healthcare. Offer a minimum of 2 examples where the data analysis supports health service delivery decision making.

From this reading....Qualitative Data Analysis for Health Services Research: Developing Taxonomy, Themes, and Theory.

I'm confused on the Var model and its applications, along with the impulse-response that goes along with the Var model

High-power experimental engines are being developed by the Stevens Motor Company for use in its new sports coupe. The engineers have calculated the maximum horsepower for the engine to be 590HP. Twenty five engines are randomly selected for horsepower testing. The sample has an average maximum HP of 580 with a standard deviation of 45⁢HP. Assume the population is normally distributed.

Step 1 of 2 :  

Calculate a confidence interval for the average maximum HP for the experimental engine. Use a significance level of α=0.01. Round your answers to two decimal places.

Implement in a computer the curve fitting. Generate appropriate data for your implementation (e.g., generate data from a polynomial function and add noise with variance σ2). Show the mean square error of the estimator in your implementation. What can you say about under/over fitting (that is, when the degree of your approximator is too small or too large for your data).

A random number generator picks a number from 18 to 64 in a uniform manner. Round answers to 4 decimal places when possible.

The mean of the distribution is:

The standard deviation is:

The probability that the number will be exactly 20 is P(x = 20) =

The probability that the number will be between 24 and 26 is P(24 < x < 26) =

The probability that the number will be larger than 32 is P(x > 32) =

P(x > 19 | x < 51) =

Find the 79th percentile.

Find the minimum for the upper quartile.

Object E is dependent on Objects A and B.

P(A works) = 0.90

P(A fails) = 0.10

P(B works) = 0.90

P(B fails) = 0.10

If Object A works, then Probability of Object E working is 0.6

If Object A fails, then Probability of Object E working is 0.2

If Object B works, then Probability of Object E working is 0.6

If Object B fails, then Probability of Object E working is 0.2

What is the Probability of Object E working?

The Westchester Chamber of Commerce periodically sponsors public service seminars and programs. Currently, promotional plans are under way for this year’s program. Advertising alternatives include television, radio, and newspaper. Audience estimates, costs, and maximum media usage limitations are as shown:

To ensure a balanced use of advertising media, radio advertisements must not exceed 50% of the total number of advertisements authorized. In addition, television should account for at least 10% of the total number of advertisements authorized.

1. If the promotional budget is limited to $20,700, how many commercial messages should be run on each medium to maximize total audience contact? What is the allocation of the budget among the three media? If required, round your answers to the nearest dollar.
   

   Let	T = number of television spot advertisements
   	R = number of radio advertisements
   	N = number of newspaper advertisements

   	Budget ($)
   T =
   R =
   N =
   	Total Budget = $

   
   What is the total audience reached? Round your answer to the nearest whole number.
   
   
   
2. By how much would audience contact increase if an extra $100 were allocated to the promotional budget? Round your answer to the nearest whole number.
   

Open Hurricanes data.

Test if there is a significant difference in the death by Hurricanes and Min Pressure measured. Answer the questions for Assessment. (Pick the closest answer)

7. What is the P-value?

• a. #DIV/0!
• b. 0.384808843
• c. 0.634755682
• d. None of these

8. What is the Statistical interpretation?

• a. The P-value is too large to have a conclusive answer.
• b. The P-value is too small to have a conclusive answer.
• c. ​​The P-value is much smaller than 5% thus we are certain that the average of hurricane deaths is significantly different from average min pressure.
• d. None of the above.

9. What is the conclusion?

• a. The statistics does not agree with the intuition since one would expect that stronger hurricanes to be deadlier.
• b. ​​Statistical interpretation agrees with the intuition, the lower the pressure the stronger the hurricanes.
• c. Statistics confirms that hurricanes’ pressure does relate to the death count.
• d. The test does not make statistical sense, it compares “apples and oranges”.

Year   Name   MinPressure_before   Gender_MF   Category   alldeaths
1950   Easy   958   1   3   2
1950   King   955   0   3   4
1952   Able   985   0   1   3
1953   Barbara   987   1   1   1
1953   Florence   985   1   1   0
1954   Carol   960   1   3   60
1954   Edna   954   1   3   20
1954   Hazel   938   1   4   20
1955   Connie   962   1   3   0
1955   Diane   987   1   1   200
1955   Ione   960   0   3   7
1956   Flossy   975   1   2   15
1958   Helene   946   1   3   1
1959   Debra   984   1   1   0
1959   Gracie   950   1   3   22
1960   Donna   930   1   4   50
1960   Ethel   981   1   1   0
1961   Carla   931   1   4   46
1963   Cindy   996   1   1   3
1964   Cleo   968   1   2   3
1964   Dora   966   1   2   5
1964   Hilda   950   1   3   37
1964   Isbell   974   1   2   3
1965   Betsy   948   1   3   75
1966   Alma   982   1   2   6
1966   Inez   983   1   1   3
1967   Beulah   950   1   3   15
1968   Gladys   977   1   2   3
1969   Camille   909   1   5   256
1970   Celia   945   1   3   22
1971   Edith   978   1   2   0
1971   Fern   979   1   1   2
1971   Ginger   995   1   1   0
1972   Agnes   980   1   1   117
1974   Carmen   952   1   3   1
1975   Eloise   955   1   3   21
1976   Belle   980   1   1   5
1977   Babe   995   1   1   0
1979   Bob   986   0   1   1
1979   David   970   0   2   15
1979   Frederic   946   0   3   5
1980   Allen   945   0   3   2
1983   Alicia   962   1   3   21
1984   Diana   949   1   2   3
1985   Bob   1002   0   1   0
1985   Danny   987   0   1   1
1985   Elena   959   1   3   4
1985   Gloria   942   1   3   8
1985   Juan   971   0   1   12
1985   Kate   967   1   2   5
1986   Bonnie   990   1   1   3
1986   Charley   990   0   1   5
1987   Floyd   993   0   1   0
1988   Florence   984   1   1   1
1989   Chantal   986   1   1   13
1989   Hugo   934   0   4   21
1989   Jerry   983   0   1   3
1991   Bob   962   0   2   15
1992   Andrew   922   0   5   62
1993   Emily   960   1   3   3
1995   Erin   973   1   2   6
1995   Opal   942   1   3   9
1996   Bertha   974   1   2   8
1996   Fran   954   1   3   26
1997   Danny   984   0   1   10
1998   Bonnie   964   1   2   3
1998   Earl   987   0   1   3
1998   Georges   964   0   2   1
1999   Bret   951   0   3   0
1999   Floyd   956   0   2   56
1999   Irene   987   1   1   8
2002   Lili   963   1   1   2
2003   Claudette   979   1   1   3
2003   Isabel   957   1   2   51
2004   Alex   972   0   1   1
2004   Charley   941   0   4   10
2004   Frances   960   1   2   7
2004   Gaston   985   0   1   8
2004   Ivan   946   0   3   25
2004   Jeanne   950   1   3   5
2005   Cindy   991   1   1   1
2005   Dennis   946   0   3   15
2005   Ophelia   982   1   1   1
2005   Rita   937   1   3   62
2005   Wilma   950   1   3   5
2005   Katrina   902   1   3   1833
2007   Humberto   985   0   1   1
2008   Dolly   963   1   1   1
2008   Gustav   951   0   2   52
2008   Ike   935   0   2   84
2011   Irene   952   1   1   41
2012   Isaac   965   0   1   5
2012   Sandy   945   1   2   159
                  

STAT 150 Homework

23. Random Variable X takes integer values and has the Moment Generating Function: Mx(t)= 4/(2-e^t)  -  6/(3-e^t).

Find the probability P(X ≤ 2).

If a researcher conducted a 2-tailed, non-directional hypothesis test with an alpha level of .02, what would be the corresponding critical value z-score(s)?

a.-2..32

b.2.325 and -2.325

c.+1.96 and -1.96

d. 2.055 and -2.055

Lotteries and contests in Canada are required by law to state the odds of winning. For example, the BC Lotto Max main jackpot has a 1 in 33.3 million (so p ≈ 3 · 10−8 ). Suppose we didn’t know the population proportion of jackpot winners, and wanted to calculate it using a sample of 1000 lottery players. (A) Explain why the techniques we used in class are not appropriate for finding a confidence interval for the proportion of jackpot winners. (B) Suppose our sample of 1000 lottery players contained zero winners. Even though we shouldn’t, use the techniques in class to find a 99% confidence interval. (C) Suppose our sample of 1000 lottery players contained one winner. Even though we shouldn’t, use the techniques in class to find a 99% confidence interval. (D) Explain why your answers to part (B) and (C) support the fact that we should not use the techniques from class to find a confidence interval. In other words, explain the problems with the confidence intervals found in parts (B) and (C).

Respondents in the 2017 General Social Survey (GSS) were asked “on an average work day, about how many hours do you have to relax or pursue the activities that you enjoy?”. 244 males responded to the question and 262 females responded. Men reported an average of 3.28 hours per day (with a standard deviation of 2.12 hours) and women reported an average of 2.99 hours per day (sd=2.05).

A. Calculate and interpret the 95% confidence interval for relaxation hours for men.

B. Calculate and interpret the 95% confidence interval for relaxation hours for women.

C. Using the data from Parts A and B, how do men and women in the population compare in terms of time spent relaxing?

Use Excel to complete the following question. You must submit/attach all work done in Excel

in order to receive full credit.

5. A health advocacy group conducted a study to determine if the nicotine content of a particular

brand of cigarettes was equal to the advertised amount. The cigarette brand advertised that the

average nicotine content per cigarette was 1.4 milligrams. The advocacy group randomly

sampled 24 cigarettes. The nicotine level for each of the sampled cigarettes is given below.

Nicotine mg. Nicotine mg.

1.8          1.9

1.1          1.6

1.2          1.9

1.2          1.9

1.0          2.0

2.0          1.6

1.7          1.1

2.0          1.8

2.3          1.9

1.4          1.4

0.9          1.8

2.4          1.5

a. Use Excel to obtain the sample mean and sample SD. Construct the 95 % confidence

interval. Enter the data into one, single column.

b. Provide an interpretation of the confidence interval from part A.

c. Does the obtained confidence interval contain the average nicotine level per cigarette

suggested by the cigarette maker? Explain what this means.

6. The National Heart, Lung, and Blood Institute completed a large-scale study of cholesterol and

heart disease, and reported that the national average for blood cholesterol level of 50-year old

males was 210 mg/dl. A total of 89 men with cholesterol readings in the average range (200 –

220) volunteered for a low cholesterol diet for 12 weeks. At the end of the dieting period their

average cholesterol reading was 204 mg/dl with a SD of 33 mg/dl.

a. What is the 95% confidence interval for the study described above? (submit all work)

b. Provide an interpretation of your answer to part A.

c. Does the known population mean of 210 mg/dl fall within the interval?

d. Provide an interpretation of your answer to part C.

e. How would (1) decreasing the sample size and (2) decreasing the confidence level

affect the size of the interval calculated in part A? Explain your answer.

Bryant's Pizza, Inc. is a producer of frozen pizza products. The company makes a profit of $1.00 for each regular pizza it produces and $1.50 for each deluxe pizza produced. Each pizza includes a combination of dough mix and topping mix. Currently the firm has 150 pounds of dough mix and 50 pounds of topping mix. Each regular pizza uses 1 pound of dough mix and 4 ounces of topping mix. Each deluxe pizza uses 1 pound of dough mix and 8 ounces of topping mix. Based on past demand Bryant can sell at least 50 regular pizzas and at least 25 deluxe pizzas. How many regular and deluxe pizzas should the company make in order to maximize profits?

a)Write mathmatical formulation for linear programming model.

b) Show feasible region on graph

c) Solve the problem and write optimal solution

Regular pizza =

Deluxe pizza =

Profit =

d) If you can get 1lb of either dough or topping mix, which one will you choose and why?

Can you use Twitter activity to forecast box office receipts on the opening weekend? The following data (stored in TwitterMovies indicate the Twitter activity (“want to see” and the receipts ($) per theater on the weekend a movie opened for seven movies. Solve this problem to two significant digits.

1. What is the independent variable (X) in this question?
2. What is the dependent variable (Y) in this question?
3. Create a scatter plot of the X and Y variables.
4. What is the Y intercept when X = 0?
5. What is the slope?
6. What is the correlation between the X and Y variables as measured by the R Square? How strong is this correlation?
7. Predict the mean receipts for a movie that has a Twitter activity of 100,000.
8. Should you use the model to predict the receipts for a movie that has a Twitter activity of 1,000,000? Why or why not?