Explain how to build an short straddle, what are the purposes of an short straddle strategy? Build a real life short straddle for an American stock of your choice, pull the options contracts and paste them on the answer. Please explain each part of it, what the credit or debit will be for the transaction, include every detail of each option contract you will use to build the short straddle trade.

A local retailer claims that the mean waiting time is less than 8 minutes. A random sample of 20 waiting times has a mean of 6.3 minutes with a standard deviation of 2.1 minutes. At α = 0.01, test the retailerʹs claim. Assume the distribution is normally distributed. Use any method, however, follow the PHANTOMS acronym.

P - Parameter Statement

H - Hypotheses

A - Assumptions & Conditions

N - Name the Test and state the curve you're using

T - Test Statistic - Round your value to TWO decimals and state the command you used to find this value

O - Obtain the P-Value or Critical Value . State the command you are using to find these values

M - Make a Decision about the Null Hypothesis and explain why

S - State Your Conclusion About the Claim

1. Explain the model that best lends itself to the use of the MR MC approach.

2. Explain in detail the steps required to determine the optimal level of output.

3. Explain in detail the steps required to determine the product price that corresponds to that optimal level of output.

4. Explain in detail the steps required to determine if maximum profit or minimum losses have been achieved.

All questions with detail please!!

# Write a function called `get_state_data` that allows you to specify a state,
# then saves a .csv file (`STATE_data.csv`) with observations from that state
# This includes data about the state, as well as the counties in the state
# You should use the full any.drinking dataset in this function (not just 2012)

# Demonstrate that you function works by passing "Utah" to the function
state_Utah <- get_state_data(Utah)

############################ Binge drinking Dataset ############################

# In this section, you will ask a variety of questions regarding the
# `binge_drinking.csv` dataset. More specifically, you will analyze a subset of
# the observations of *just the counties* (exclude state/national estimates!).
# You will store your answers in a *named list*, and at the end of the section,
# Convert that list to a data frame, and write the data frame to a .csv file.
# Pay close attention to the *names* to be used in the list.

# Create a dataframe with only the county level observations from the
# `binge_driking.csv` dataset. You should (again) think of Washington D.C. as
# a state, and therefore *exclude it here*.
# However, you should include "county-like" areas such as parishes and boroughs
county_data <- binge.drinking.csv %>% distinct(state)

# Create an empty list in which to store answers to the questions below.

# What is the average county level of binge drinking in 2012 for both sexes?
# Store the number in your list as `avg_both_sexes`.

# What is the name of the county with the largest increase in male binge
# drinking between 2002 and 2012?
# Store the county name in your list as `largest_male_increase`.

# How many counties experienced an increase in male binge drinking between
# 2002 and 2012?
# Store the number in your list as `num_male_increase`.

# What fraction of counties experienced an increase in male binge drinking
# between 2002 and 2012?
# Store the fraction (num/total) in your list as `frac_male_increase`.

  
# How many counties experienced an increase in female binge drinking between
# 2002 and 2012?
# Store the number in your list as `num_female_increase`.

# What fraction of counties experienced an increase in female binge drinking
# between 2002 and 2012?
# Store the fraction (num/total) in your list as `frac_female_increase`.

# How many counties experienced a rise in female binge drinking *and*
# a decline in male binge drinking?
# Store the number in your list as `num_f_increase_m_decrease`.

# Convert your list to a data frame, and write the results
# to the file `binge_info.csv`

# The next questions return *data frames as results*:

# What is the *minimum* level of binge drinking in each state in 2012 for
# both sexes (across the counties)? Your answer should contain roughly 50 values
# (one for each state), unless there are two counties in a state with the
# same value. Your answer should be a *dataframe* with the location, state, and
# 2012 binge drinking rate. Write this to a file called `min_binge.csv`.

# What is the *maximum* level of binge drinking in each state in 2012 for
# both sexes (across the counties)? Your answer should contain roughly 50 values
# (one for each state), unless there are two counties in a state with the
# same value. Your answer should be a *dataframe* with the location, state, and
# 2012 binge drinking rate. Write this to a file called `max_binge.csv`.

  
################################# Joining Data #################################
# You'll often have to join different datasets together in order to ask more
# involved questions of your dataset. In order to join our datasets together,
# you'll have to rename their columns to differentiate them.

# First, rename all prevalence columns in the any_drinking dataset to the
# have prefix "any_" (i.e., `males_2002` should now be `any_males_2002`)
# Hint: you can get (and set!) column names using the colnames function.
# This may take multiple lines of code.

# Then, rename all prevalence columns in the binge_drinking dataset to the have
# the prefix "binge_" (i.e., `males_2002` should now be `binge_males_2002`)
# This may take multiple lines of code.

# Then, create a dataframe by joining together the both datasets.
# Think carefully about the *type* of join you want to do, and what the
# *identifying columns* are. You will use this (joined) data to answer the
# questions below.

# Create a column `diff_2012` storing the difference between `any` and `binge`
# drinking for both sexes in 2012

# Which location has the greatest *absolute* difference between `any` and
# `binge` drinking? Your answer should be a one row data frame with the state,
# location, and column of interest (diff_2012).
# Write this dataframe to `biggest_abs_diff_2012.csv`.

# Which location has the smallest *absolute* difference between `any` and
# `binge` drinking? Your answer should be a one row data frame with the state,
# location, and column of interest (diff_2012).
# Write this dataframe to `smallest_abs_diff_2012.csv`.

############## Write a function to ask your own question(s) ####################
# Even in an entry level data analyst role, people are expected to come up with
# their own questions of interest (not just answer the questions that other
# people have). For this section, you should *write a function* that allows you
# to ask the same question on different subsets of data. For example, you may
# want to ask about the highest/lowest drinking level given a state or year.
# The purpose of your function should be evident given the input parameters and
# function name. After writing your function, *demonstrate* that the function
# works by passing in different parameters to your function.

################################### Challenge ##################################

# Using your function from part 1 that wrote a .csv file given a state name,
# write a separate file for each of the 51 states (including Washington D.C.)
# The challenge is to do this in a *single line of (very concise) code*

# Write a function that allows you to pass in a *dataframe* (i.e., in the format
# of binge_drinking or any_drinking) *year*, and *state* of interest. The
# function should saves a .csv file with observations from that state's counties
# (and the state itself). It should only write the columns `state`, `location`,
# and data from the specified year. Before writing the .csv file, you should
# *sort* the data.frame in descending order by the both_sexes drinking rate in
# the specified year. The file name should have the format:
# `DRINKING_STATE_YEAR.csv` (i.e. `any_Utah_2005.csv`).
# To write this function, you will either have to use a combination of dplyr
# and base R, or confront how dplyr uses *non-standard evaluation*
# Hint: https://github.com/tidyverse/dplyr/blob/34423af89703b0772d59edcd0f3485295b629ab0/vignettes/nse.Rmd
# Hint: https://www.r-bloggers.com/non-standard-evaluation-and-standard-evaluation-in-dplyr/

# Create the file `binge_Colorado_2007.csv` using your function.

PLEASE TYPE THE ANSWER

As simply as you can but with complete and comprehensive detail, state and explain each of Newtons Three Fundamental Laws of Motion. After you have explained all Three Laws look through the example problems given in the book and come up with and explain an example problem of your own. It must include an explanation of how at least one of Newton's laws is used if not more than one.

A study of the effect of exposure to color (red or blue) on the ability to solve puzzles used 42 subjects. Half the subjects (21) were asked to solve a series of puzzles while in a red-colored environment. The other half were asked to solve the same series of puzzles while in a bluecolored environment. The time taken to solve the puzzles was recorded for each subject. Due to the association of urgency with red, researchers believe those exposed to red will solve the puzzles faster than those exposed to blue. The 21 subjects in the red-colored environment had a mean time for solving the puzzles of 9.64 seconds with standard deviation 3.43; the 21 subjects in the blue-colored environment had a mean time of 15.84 seconds with standard deviation 8.65.

(a) State explicitly what population 1 and what population 2 are.

(b) State the null and alternative hypothesis.

(c) Find the two-sample t-statistic and p-value.

(d) State a conclusion at the 0.005 significance level.

You are expecting your first baby and are thinking about sleeping arrangements. You have heard of the concept of "the family bed" and are considering having the baby sleep with you and your spouse. Elaborate on the scenario by writing a letter to a relative or close friend or a diary/journal entry from the perspective of a parent writing about his or her child. Your letter/diary should explain what happened in detail, identify the issues you will need to consider and research in coming up with a solution, and a plan of action for how you will deal with the situation. 4-6 pages

In part of a study reported by Perotta and Finch (1972), the blood films of 16 patients with severe renal (kidney) anemia and 10 patients with functional heart disease were measured for red blood cell counts. The percentage changes in red blood cell counts appear below:

• Renal anemia: 2.20, 1.52, 1.54, 0.77, 0.34, 0.45, 0.39, 0.29, 0.18, 0.16, 0.23, 0.24, 0.17, 0.08, 0.02, 0.02.

• Heart disease: 1.84, 0.44, 0.30, 0.06, 0.20, 0.14, 0.10, 0.09, 0.06, 0.04.

Because of non-normality, we’ll do our analysis on the logs of the data.

Suppose we wish to show that the mean log percentage change is greater for renal patients than for heart patients.

(a) Write down mathematical null and alternative hypotheses for such a test. Carefully define the parameters you use.

(b) Calculate the observed test statistic and the degrees of freedom for an appropriate test.

(c) Find a P-value for your test. What does it tell you about severe renal anemia, functional heart disease, and red blood cell counts?

(d) Find a a 95% confidence interval for the difference in the means of the log percentage changes. Transform the confidence interval back to the original scale. What does your interval tell you quantitatively about the difference in the distributions of red blood cell count changes between the two types of patients?

Kidneys and hearts - R code

In part of a study reported by Perotta and Finch (1972), the blood films of 16 patients with severe renal (kidney) anemia and 10 patients with functional heart disease were measured for red blood cell counts. The percentage changes in red blood cell counts appear below:

Renal anemia: 2.20, 1.52, 1.54, 0.77, 0.34, 0.45, 0.39, 0.29, 0.18, 0.16, 0.23, 0.24, 0.17, 0.08,0.02, 0.02.

Heart disease: 1.84, 0.44, 0.30, 0.06, 0.20, 0.14, 0.10, 0.09, 0.06, 0.04.

Because of non-normality, we'll do our analysis on the logs of the data. Suppose we wish to show that the mean log percentage change is greater for renal patients than for heart patients.

(a) Write down mathematical null and alternative hypotheses for such a test. Carefully define the parameters you use.

(b) Calculate the observed test statistic and the degrees of freedom for an appropriate test.

(c) Find a P-value for your test. What does it tell you about severe renal anemia, functional heart disease, and red blood cell counts?

(d) Find a 95% confidence interval for the difference in the means of the log percentage changes.

Transform the confidence interval back to the original scale. What does your interval tell you quantitatively about the difference in the distributions of red blood cell count changes between the two types of patients?

Positive and Negative Control Questions:

Please complete the following table to describe which bacterial species should be used as positive and negative controls for each test. The table at the bottom of this page will help you complete this task. Thenexplains about

What can you learn about a microbe by doing the Phenol Red test, how do you conduct the Phenol Red test?

What does a positive Phenol Red test look like? (mention acid formation and gas formation)

What does a negative Phenol Red test look like?(mention acid formation and gas formation)

The table below describes the cultures we will have available to use as positive and negative controls.