Natural gas providers, like Scana Energy, can create switching costs for its product. This means,

a. it is difficult or costly for customers to switch to another seller of the product.

b.it increases its costs by producing several versions of the product so customers can switch between versions.

c.customers will find it more costly to stay with the company than to switch to another seller.

d.it is difficult or costly for the seller to switch to producing a different product.

In a recent study, the Centers for Disease Control and Prevention reported that diastolic blood pressures of adult women in the United States are approximately normally distributed with mean 80.3 and standard deviation 10.1. (a) What proportion of women have blood pressures lower than 73? (b) What proportion of women have blood pressures between 74 and 90? (c) A diastolic blood pressure greater than 90 is classified as hypertension (high blood pressure). What proportion of women have hypertension? (d) Is it unusual for a woman to have a blood pressure lower than 66? Round the answers to four decimal places.

The relationship between "strength" and "fineness" of cotton fibers was the subject of a study that produced the following data. (Give your answers correct to two decimal places.)

(b) Find the 98% confidence interval for the mean measurement of fineness for fibers with a strength of 70.

(c) Find the 98% prediction interval for an individual measurement of fineness for fibers with a strength of 70.

A student uses the given set of data to compute a least‑squares regression line and a correlation coefficient: ? 0.7 0.8 1.7 1.7 1.3 2.6 8.0 ? 1 2 2 1 0 1 5 The student claims that the regression line does an excellent job of explaining the relationship between the explanatory variable ? and the response variable ? . Is the student correct?

a. Yes, because ?2=0.74 means that 74% of the variation in ? is explained by the least‑squares regression of ? on ? . b. No, because the outlier is inflating the correlation coefficient. c. Yes, because the correlation coefficient is ?=0.86 , which is close to 1. d. No, because the slope of the regression line is only 0.54.

Sixteen laboratory animals were fed a special diet from birth through age 12 weeks. Their

weight gain (in grams) were as follows:

63 68 79 65 64 63 65 64 76 74 66 66 67 73 69 76

Can we conclude from these data that the diet results in a mean weight gain of less than 70

grams? Let α = 0.05.

Note: There are two possible ways to analyze this data. Use the statistical procedure

that makes use of the magnitudes of the differences between measures and a

hypothesized location parameter rather than just the signs of the differences.

Please write in Python code please:

Write a program that asks the user to enter 5 test scores between 0 and 100. The program should display a letter grade for each score and the average test score. You will need to write the following functions, including main:

calc_average – The function should accept a list of 5 test scores as an input argument, and return the average of the scores

determine_grade – The function should accept a test score as an argument, and return a letter grade for the score based on the grading scale below:

85 or over HD
75-84 D
65-74 C
50-64 P
<50 F

"Black Friday" is the day after Thanksgiving and the traditional first day of the Christmas shopping season. Suppose a recent poll suggested that 66% of Black Friday shoppers are actually buying for themselves. A random sample of 130 Black Friday shoppers is obtained. Answer each problem using the normal approximation to the binomial distribution.

(a)

Find the approximate probability that fewer than 73 Black Friday shoppers are buying for themselves. (Round your answer to four decimal places.)

(b)

Find the approximate probability that between 74 and 84 (inclusive) Black Friday shoppers are buying for themselves. (Round your answer to four decimal places.)

Once a pinnacle of luxury clothing found only in high-end fashion stores, by 2006 cashmere sweaters, which typically sold for hundreds of dollars, could be found in big box stores for as little as $20. The reason for this substantial price drop: increased production and competition from China. The cashmere industry has been around for centuries. Historically, however, Chinese and Mongolian herders exported the raw fiber to Europe, where it was spun and converted into clothing. Beginning in the 1980’s, China made a charge toward industrialization and the market economy. One area of rapid growth was the textile industry.

To increase production of cashmere wool, the number of wool-producing goats in Inner Mongolia, home to a vast grassland that the animals can graze on, increased tenfold, from 2.4 million in 1949 to 25.8 million in 2004. This dramatically increased the production of cashmere in China, but not without its consequences. One of the biggest problems, however, is that goats are devastating to the topsoil. The combination of pointy hooves and a voracious appetite leads to a rise in desertification. That is, turning grassland to desert. Over a 5-year period, “the Gobi Desert expanded in size by an area larger than the Netherlands.”

A consequence of this desertification is an increase in dust storms. Over the last several decades, the number and size of dust storms originating in China has grown dramatically. These storms impose a tremendous external cost on the regions through which they travel. One storm, “forced 1.8 million South Koreans to seek medical help and cost the country $7.8 billion in damage to industries such as airlines and semiconductors.” Another storm was so large it traveled around the entire world, causing damage in the US, Europe, and Africa.

1. There are several methods for reducing the costs of negative externalities. Why might some of these be difficult to implement at the global level? and what policies could the Chinese government implement to prevent the Tragedy of the Commons outcome of desertification?

Use the data and Excel to answer this question. It contains the United States Census Bureau’s estimates for World Population from 1950 to 2014. You will find a column of dates and a column of data on the World Population for these years. Generate the time variable t. Then run a regression with the Population data as a dependent variable and time as the dependent variable. Have Excel report the residuals.

(a) Based on the ANOVA table and t-statistics, does the regression appear significant?

(b) Calculate the Durbin-Watson Test statistic. Is there a serial correlation problem with the data? Explain.

(d) What affect might your answer in part (b) have on your conclusions in part (a)?

Can you please give detailed steps to do on excel?

Program Requirements:

Write a C++ program according to the following requirements:

1.   Open the data file Electricity.txt and read each column into an array (8 arrays total).

2.   Also create 2 arrays for the following:

• Total Fossil Fuel Energy (sum of all fossil fuels)
• Total Renewable Energy (sum of all renewable sources)

Electricity.txt:

Net generation United States all sectors monthly
https://www.eia.gov/electricity/data/browser/
Source: U.S. Energy Information Administration
All values in thousands of megawatthours
Year   all fuels   coal       natural gas   nuclear       hydroelectric   wind       solar
2018   347576.0   95496.8       122393.9   67257.0       24377.0       22720.8       7780.4
2017   336189.2   100486.3   108034.6   67079.1       25027.8       21191.9       6439.7
2016   339722.9   103262.4   114858.9   67141.2       22317.7       18916.0       4572.2
2015   339800.1   112699.8   111123.5   66431.5       20756.7       15893.2       3252.7
2014   341133.8   131809.2   93884.1       66430.5       21613.9       15137.9       2410.3
2013   338830.3   131759.6   93736.3       65751.4       22380.4       13986.6  
2012   337313.8   126170.2   102157.8   64110.9       23020.0       11735.1  
2011   341678.4   144452.5   84474.1       65850.4       26612.9       10014.7  
2010   343755.0   153940.9   82308.1       67247.4       21683.6       7887.7  
2009   329194.2   146325.4   76748.2       66571.2       22787.1       6157.2  
2008   343282.3   165483.4   73581.7       67184.0       21235.9       4613.6  
2007   346395.4   168038.0   74715.8       67202.1       20625.8       2870.8  
2006   338725.2   165875.9   68036.7       65601.6       24103.9       2215.8  
2005   337951.9   167739.4   63413.4       65165.5       22526.8       1484.2  
2004   330879.6   164858.4   59175.0       65710.7       22368.1       1178.6  
2003   323598.8   164478.1   54159.0       63644.4       22983.9       932.3  
2002   321537.7   161094.2   57583.8       65005.3       22027.4       862.9  
2001   311387.0   158663.0   53260.8       64068.9       18080.1       561.4