Regression analysis is often used to provide a means to express the relationship between one or more input variables and a result. It is easy to plot in Excel (“add trendline”) so is found frequently in business presentations. Your company has made a model with 10 different factors measured from past years’ and states based upon the model, the company expects to make a 23 million dollar profit next year. Discuss possible concerns with banking on the 23 million dollar prediction, including concepts of

a. correlation

b. causation

c. single point prediction (that is, just plugging the values into the equation and saying that single number is the prediction of future performance)

d. confidence interval.  

e. prediction interval.

You would like to study the height of students at your university. Suppose the average for all university students is 68 inches with a SD of 20 inches, and that you take a sample of 17 students from your university.

a) What is the probability that the sample has a mean of 64 or more inches? probability = .204793 (is this answer correct or no? and I need help with part b too.)

b) What is the probability that the sample has a mean between 63 and 68 inches?

Safeco company produces two types of​ chainsaws: The Safecut and the Safecut Deluxe. The Safecut model requires 2 hours to assemble and 1 hour to​ paint, and the Deluxe model requires 4 hours to assemble and one half
hour to paint. The daily maximum number of hours available for assembly is 32​, and the daily maximum number of hours available for painting is 10. If the profit is ​$26 per unit on the Safecut model and ​$40 per unit on the Deluxe​ model, how many units of each type will maximize the daily profit and what will that profit​ be?

You have 120 mice lacking insulin receptors in their brain tissue. On average 15.2% of these types of mice will die within a month. What is the probability at least 100 mice live until next month? (use binomial approximation of normal)

1. Consider the data set below.

65 68 58 57 71 61 60 67 66 72

60 62 54 57 61 54 55 58 61 55

64 66 56 61 51 83 57 55 58 59

61 55 66 55 58 52 75 67 64 56

55 58 50 62 63 67 57 54 55 63

a.       Find the mean, median and mode.

b.       Find the range, variance, and standard deviation.

c.              Find the lower quartile, upper quartile, and inter-quartile range

2- For the same data set as Problem 1, are there outliers in the data? Justify your conclusion using;

1. Box plot. Use +1.5*interquartile range, (Lower inner fence and Upper inner fence)
2. Empirical rule method.

Life expectancy in the US varies depending on where an individual lives, reflecting social and health inequality by region. You are interested in comparing mean life expectancies in counties in California, specifically San Mateo County and San Francisco County. Given the data below, answer the following questions.

1.  Calculate the standard error of the mean difference in male life expectancy between the 2 counties, assuming nonequal variance.

2. Calculate a 99% confidence interval for the mean difference in male life expectancy between the two counties. Use the conservative approximation for degrees of freedom.

3.  Based on your confidence interval, would you expect the mean difference in male life expectancy to be statistically significant at the α=.01 level? EXPLAIN

Please show all work. Thank you!

The amount of money requested on home loan applications at America Bank follows normal distribution, with a mean of $180,000 and a standard deviation of $6,000. A loan application is received this morning.

a) What is the probability the amount requested is $190,000 or more?

b) What is the probability the amount requested is between $178,000 and $190,000?

c) What is the probability the amount requested in below $178,000?

d) How much is requested on the largest 5% of the loans?

e) How much is requested on the smallest 5% of the loans?

Respond to the following in a minimum of 175 words, please type response:

How can regression modeling be used to understand the association between two variables.

Respond to the following in a minimum of 175 words, please type response:

How can simple regression modeling be extended to understand the relationship among several variables.

The weight of male students at a certain university is normally distributed with a mean of 175 pounds with a standard deviation of 7.6 pounds. Find the probabilities.

1. A male student weighs at most 186 pounds.

2. A male students weighs at least 160 pounds.

3. A male student weighs between 165 and 180 pounds.

Please show work. Ideally excel commands would be helpful, but anything would be great!

Do larger universities tend to have more property crime? University crime statistics are affected by a variety of factors. The surrounding community, accessibility given to outside visitors, and many other factors influence crime rate. Let x be a variable that represents student enrollment (in thousands) on a university campus, and let y be a variable that represents the number of burglaries in a year on the university campus. A random sample of n = 8 universities in California gave the following information about enrollments and annual burglary incidents. x 12.9 30.4 24.5 14.3 7.5 27.7 16.2 20.1 y 27 71 39 23 15 30 15 25

(a) Make a scatter diagram of the data. Then visualize the line you think best fits the data. (Submit a file with a maximum size of 1 MB.) This answer has not been graded yet.

(b) Use a calculator to verify that Σ(x) = 153.6, Σ(x2) = 3385.30, Σ(y) = 245, Σ(y2) = 9795 and Σ(x y) = 5480.1. Compute r. (Enter a number. Round to 3 decimal places.) As x increases, does the value of r imply that y should tend to increase or decrease? Explain your answer. Given our value of r, y should tend to remain constant as x increases. Given our value of r, y should tend to decrease as x increases. Given our value of r, we can not draw any conclusions for the behavior of y as x increases. Given our value of r, y should tend to increase as x increases.

1. Tide Pods are concentrated laundry detergent packets, which have been sold since 2012. Their colorful appearance and small size have led to nearly 7,700 pod-related calls to poison centers for children under 5. In 2017, a series of memes featured Tide Pods as “forbidden fruit.” In January 2018 a (mostly) satirical Tide Pod Challenge appeared, that “encouraged” teenagers to eat tide pods, video the experience, and post it online.
   1. During the first 30 days of 2018, there were 93 calls to poison control centers for teenagers who intentionally ate laundry pods. Typically, the number of calls for teenagers ingesting laundry pods in a month has  = 37 and s = 12. What is the z-score for number of calls in January 2018? What does this imply?
   2. Public was associating increased sharing/view rate of videos tagged “Tide Pod Challenge” with increased calls to poison control centers for eating Tide Pods? For each day in the first 30 days of 2018, the number of YouTube views and poison center calls were measured.  The correlation was r = .41. Is this a significant correlation at the 95% confidence level? Should YouTube take the videos down?
   3. In response to the Tide Pod Challenge, P&G has added warnings about ingesting laundry pods. This may have reduced poisoning incidents involving children. Based on data from the first 15 weeks of 2018, weekly calls for children had an average of 119 and std. deviation of 20. Data from before 2018 had weekly calls of at least 148. Has the number of poisoning incidents decreased? Write the null and alternative hypothesis.
   4. Calculate the test statistic, and with 95% confidence, state your conclusion.

At one hospital there is some concern about the high turnover of nurses. A survey was done to determine how long (in months) nurses had been in their current positions. The responses (in months) of 20 nurses were as follows. 27 6 9 18 29 40 31 46 16 12 11 27 33 30 32 15 24 35 12 40 Make a box-and-whisker plot of the data. (Select the correct graph.) Find the interquartile range. (Enter an exact number.) IQR =

1. calculate the range for the expected true mean temperature with 95% confidence (2-sided confidence interval)
2. calculate the value the true mean temperature should be greater than with 95% confidence (1-sided confidence interval)

What is the difference between these two problems?

what equation do I use?

QUESTion 6

The association between the variables "golf score" and "golf skill" would be

QUESTION 7

If the correlation coefficient for a lnear regression is 0.987. there is sufficient evidence that a linear relationship exists between the x and y data

QUESTION 8

If the correlation coefficient for a lnear regression is -0.932. there is sufficient evidence that a linear relationship exists between the x and y data

QUESTION 9

A data point that lies statistically far from the regression line is a potential

QUESTION 10

1. term 3:
   
   In linear regression, the dependent variable is called the

QUESTION 11

If the correlation coefficient for a linear regression is 1.00. there is solid proof that a true cause-effect relationship exists between the x and y data

QUESTION 12

1. term 12:
   
   A linear regression analysis on some data yields a correlation coefficient of 0.003. Which of the following is the most correct statement?