3. The strength of the correlation motivates further examination.

a. Insert Scatter (X,Y) plot linked to the data on this s heet with Age on the horizontal (X) axis.

b. Add to your chart: the chart name, vertical axis label, and horizontal axis label

c. Complete the chart by adding Trendline and checking boxes: Display Equation on chart & Display R-squared value on chart

4. Read directly from the chart:

a. Intercept =

b. Slope =

c. R² =

Perform Data > Data Analysis > Regression

5. Highlight the Y-Intercept with yellow. Highlight the X variable in blue. Highlight the R Square in orange.

A professor's commute is normally distributed with a mean of 40 minutes and a standard deviation of 10 minutes. (a) What is the probability that the professor gets to work in 30 min or less? (Round your answer to three decimal places.) . (b) If the professor has a 9 A.M. class and leaves home at 8 A.M., how often is the professor late for class? (Round your answer to one decimal place.) - % of the time

Let x be a random variable that represents the level of glucose in the blood (milligrams per deciliter of blood) after a 12 hour fast. Assume that for people under 50 years old, x has a distribution that is approximately normal, with mean μ = 72 and estimated standard deviation σ = 24. A test result x < 40 is an indication of severe excess insulin, and medication is usually prescribed.

(a) What is the probability that, on a single test, x < 40? (Round your answer to four decimal places.)


(b) Suppose a doctor uses the average x for two tests taken about a week apart. What can we say about the probability distribution of x? Hint: See Theorem 6.1.

The probability distribution of x is not normal.The probability distribution of x is approximately normal with μ_x = 72 and σ_x = 12.00.    The probability distribution of x is approximately normal with μ_x = 72 and σ_x = 24.The probability distribution of x is approximately normal with μ_x = 72 and σ_x = 16.97.

What is the probability that x < 40? (Round your answer to four decimal places.)


(c) Repeat part (b) for n = 3 tests taken a week apart. (Round your answer to four decimal places.)


(d) Repeat part (b) for n = 5 tests taken a week apart. (Round your answer to four decimal places.)


(e) Compare your answers to parts (a), (b), (c), and (d). Did the probabilities decrease as n increased?

YesNo    

Explain what this might imply if you were a doctor or a nurse.

The more tests a patient completes, the weaker is the evidence for lack of insulin.The more tests a patient completes, the weaker is the evidence for excess insulin.    The more tests a patient completes, the stronger is the evidence for lack of insulin.The more tests a patient completes, the stronger is the evidence for excess insulin.

Heights for group of people are normally distributed with mean = 63 inches and standard deviation = 4.0 inches. Find the proportion, P, of people in the group whose heights fall into the following ranges. (Round your answers to four decimal places.)

(a) Between 60 inches and 63 inches.

(b) Between 57 inches and 69 inches.

(c) Less than 69 inches.

(d) Greater than 57 inches.

(e) Either less than 57 inches or greater than 69 inches.

Here are summary statistics for randomly selected weights of newborn​ girls: n =191​, x̅ = 33.2 ​hg, s = 6.1 hg. Construct a confidence interval estimate of the mean. Use a 90​%

confidence level. Are these results very different from the confidence interval 32.9 hg < μ < 34.3 hg with only 19 sample​ values, x̅ = 33.6 ​hg, and s = 1.7 ​hg?

A. What is the confidence interval for the population mean μ​?

____ hg < ​​​μ​ < ____ hg (Round to one decimal place as​ needed.)

B. Are the results between the two confidence intervals very​ different?

a) Yes, because the confidence interval limits are not similar.

b) No, because each confidence interval contains the mean of the other confidence interval.

c) ​No, because the confidence interval limits are similar.

d) Yes, because one confidence interval does not contain the mean of the other confidence interval.

Question 1
Seven baseballs are randomly selected from the production line to see if their stitching is straight. Over time, the company has found that 89.4% of all their baseballs have straight stitching. If exactly five of the seven have straight stitching, should the company stop the production line?
   Yes, the probability of five or less having straight stitching is unusual
   No, the probability of five or less having straight stitching is not unusual
   No, the probability of exactly five have straight stitching is not unusual
   Yes, the probability of exactly five having straight stitching is unusual
  
Question 2

A soup company puts 12 ounces of soup in each can. The company has determined that 97% of cans have the correct amount. Which of the following describes a binomial experiment that would determine the probability that a case of 36 cans has all cans that are properly filled?
   n=36, p=0.97, x=36
   n=36, p=0.97, x=1
   n=12, p=0.36, x=97
   n=12, p=0.97, x=0

Question 3

A supplier must create metal rods that are 2.3 inches width to fit into the next step of production. Can a binomial experiment be used to determine the probability that the rods are the correct width or an incorrect width?
   No, as the probability of being about right could be different for each rod selected
   Yes, all production line quality questions are answered with binomial experiments
   No, as there are three possible outcomes, rather than two possible outcomes
   Yes, as each rod measured would have two outcomes: correct or incorrect
  
Question 4

In a box of 12 pens, there is one that does not work. Employees take pens as needed. The pens are returned once employees are done with them. You are the 5th employee to take a pen. Is this a binomial experiment?
   No, binomial does not include systematic selection such as “fifth”
   No, the probability of getting the broken pen changes as there is no replacement
   Yes, you are finding the probability of exactly 5 not being broken
   Yes, with replacement, the probability of getting the one that does not work is the same
  
Question 5

Sixty-eight percent of products come off the line within product specifications. Your quality control department selects 15 products randomly from the line each hour. Looking at the binomial distribution, if fewer than how many are within specifications would require that the production line be shut down (unusual) and repaired?
   Fewer than 8
   Fewer than 9
   Fewer than 11
   Fewer than 10
  
Question 6

The probability of a potential employee passing a drug test is 86%. If you selected 12 potential employees and gave them a drug test, how many would you expect to pass the test?
   8 employees
   9 employees
   10 employees
   11 employees
  
Question 7

The probability of a potential employee passing a training course is 86%. If you selected 15 potential employees and gave them the training course, what is the probability that 12 or less will pass the test?
   0.862
   0.148
   0.100
   0.852
  
Question 8
Off the production line, there is a 3.7% chance that a candle is defective. If the company selected 45 candles off the line, what is the probability that fewer than 3 would be defective?
   0.975
   0.916
   0.768
   0.037

Rothamsted Experimental Station (England) has studied wheat production since 1852. Each year, many small plots of equal size but different soil/fertilizer conditions are planted with wheat. At the end of the growing season, the yield (in pounds) of the wheat on the plot is measured. For a random sample of years, one plot gave the following annual wheat production (in pounds).

Use a calculator to verify that, for this plot, the sample variance is s² ≈ 0.305.

Another random sample of years for a second plot gave the following annual wheat production (in pounds).

Use a calculator to verify that the sample variance for this plot is s² ≈ 0.095.

Test the claim that the population variance of annual wheat production for the first plot is larger than that for the second plot. Use a 1% level of significance.

(a) What is the level of significance?

State the null and alternate hypotheses.

H_o: σ₁² = σ₂²; H₁: σ₁² > σ₂²

H_o: σ₁² > σ₂²; H₁: σ₁² = σ₂²    

H_o: σ₂² = σ₁²; H₁: σ₂² > σ₁²

H_o: σ₁² = σ₂²; H₁: σ₁² ≠ σ₂²

(b) Find the value of the sample F statistic. (Use 2 decimal places.)

What are the degrees of freedom?

What assumptions are you making about the original distribution?

The populations follow dependent normal distributions. We have random samples from each population.

The populations follow independent normal distributions. We have random samples from each population.    

The populations follow independent normal distributions.

The populations follow independent chi-square distributions. We have random samples from each population.

(c) Find or estimate the P-value of the sample test statistic. (Use 4 decimal places.)

p-value > 0.100

0.050 < p-value < 0.100    

0.025 < p-value < 0.050

0.010 < p-value < 0.025

0.001 < p-value < 0.010

p-value < 0.001

(d) Based on your answers in parts (a) to (c), will you reject or fail to reject the null hypothesis?

At the α = 0.01 level, we reject the null hypothesis and conclude the data are not statistically significant.

At the α = 0.01 level, we reject the null hypothesis and conclude the data are statistically significant.    

At the α = 0.01 level, we fail to reject the null hypothesis and conclude the data are not statistically significant.

At the α = 0.01 level, we fail to reject the null hypothesis and conclude the data are statistically significant.

(e) Interpret your conclusion in the context of the application.

Fail to reject the null hypothesis, there is sufficient evidence that the variance in annual wheat production is greater in the first plot.

Reject the null hypothesis, there is insufficient evidence that the variance in annual wheat production is greater in the first plot.    

Reject the null hypothesis, there is sufficient evidence that the variance in annual wheat production is greater in the first plot.

Fail to reject the null hypothesis, there is insufficient evidence that the variance in annual wheat production is greater in the first plot.

Please be very clear in part b) show all steps please. I have trouble understanding that part. Thank you.

Whencoin1isflipped,itlandsonheadswithprob- ability .4; when coin 2 is flipped, it lands on heads with probability .7. One of these coins is randomly chosen and flipped 10 times.
(a) What is the probability that the coin lands on heads on exactly 7 of the 10 flips?
(b) Given that the first of these ten flips lands heads, what is the conditional probability that exactly 7 of the 10 flips land on heads?

A survey of 300 U.S. online shoppers was conducted. In response to the question of what would influence the shopper to spend more money online in 2012, 18% said free shipping, 13% said offering discounts while shopping, and 9% said product reviews. (Data extracted from “2012 Consumer Shopping Trends and Insights,” Steelhouse, Inc., 2012.)

a) Construct 95% confidence intervals of the population proportion of online shoppers who would be influenced to spend more money online in 2012 for “Free shipping”, “Discounts offered while shopping”, “Product reviews” respectively.

b) You have been asked to update the results of this study. Determine the sample size necessary to estimate the population proportions in part (a) within ±0.02, with 95% confidence.

Saeko owns a yarn shop and want to expands her color selection. Before she expands her colors, she wants to find out if her customers prefer one brand over another brand. Specifically, she is interested in three different types of bison yarn. She randomly selected 21 different days and recorded sales of each brand. 0.10 significance level, can she conclude that there is a difference in preference between brands?

What is the null hypothesis, alternative hyp, level of significance.

Use ANOVA single factor to find the F statistic

The following data represents a random sample of birth weignts (in kgs) of male babies born to mothers on a special vitamin supplement.

3.73
3.02
4.37
4.09
3.73
2.47
4.33
4.13
3.39
4.47
3.68
3.22
4.68
3.43

(a) Do the data follow a normal distribution?  ? Yes No
Report the P-value of the normality test:

(b) Do the data support the claim that the mean birth weight of male babies that have been subjected to the vitamin supplement is at least 3.39 kgs? Use the p-value approach, and regulate the probability of committing Type I error to 5%5% (α=0.05α=0.05).

The p-value is:

Use three decimals.
Does this support the claim  ? Yes No

An automotive parts supplier assesses the usability and quality of the door locks that they provide. The locks are manufactured at three different plants. The production manager wants to determine whether the plant affects the final product. The production manager collects data on locks from each plant, and gives a usability and quality rating. Data are found in the file Car Lock Ratings.

a) State the null and alternate hypothesis we would run to determine if the Usability rating across all three manufacturing plants is the same.

b) Run a one-way ANOVA on these data. Show output.

c) What conclusions can you make based on the p-value of this test?

d) Obtain boxplot, residuals scatter plot, and individual residual Normal probability plots.

e) Have all assumptions been met? Explain using your plots to illustrate your answer.

in Pennsylvania Cash 5 lottery balls are numbered 1 to 43 right balls are selected does not replacement the order in which the balls are selected does not matter the term your probability of winning Pennsylvania Cash 5 with one ticket write your answers in fractions

An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,700 450 5,700 550 6,100 600 6,600 700 7,100 750 7,700 Compute b1 and b0 (to 1 decimal). b1 b0 Complete the estimated regression equation (to 1 decimal). = + x What is the variable cost per unit produced (to 1 decimal)? $ Compute the coefficient of determination (to 3 decimals). Note: report r2 between 0 and 1. r2 = What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? % The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)? $