Given the age ( M = 19.83, SD = 3.07) of participants attending a kickboxing class, please answer the following:

a. Between what values do 68% of the data lie? _______________

b. Between what values do 95% of the data lie? _______________

Suppose we are studying the effect of diet on height of children, and we have two diets to compare: diet A (a well-balanced diet with lots of broccoli) and diet B (a diet rich in potato chips and candy bars). We wish to find the diet that helps children grow faster. We have decided to use 20 children in the experiment, and we are contemplating the following methods for matching children with diets: i. Let them choose. ii. Take the first 10 for A, the second 10 for B. iii. Alternate A, B, A, B. iv. Toss a coin for each child in the study: heads→A, tails→B. v. Get 20 children; choose 10 at random for A, the rest for B. Describe the benefits and risks of using these five methods

Find the mean and standard deviation of the times and icicle lengths for the data on run 8903 in data data93.dat. Find the correlation between the two variables. Use these five numbers to find the equation of the regression line for predicting length from time. Use the same five numbers to find the equation of the regression line for predicting the time an icicle has been growing from its length. (Round your answers to three decimal places.) ATTACHED BELOW IS THE DATA SET

time    length
10      .9
20      2.7
30      4.9
40      6.5
50      6.8
60      7.5
70      8.9
80      11.2
90      13.5
100     15.4
110     15
120     19.7
130     20.4
140     23
150     24.8
160     27.1
170     28
180     30.1

What is the SST for this set of figures

120.08 30.75 75.86 54.05 81.9 61.83 37.88 38.82 78.79 26.4 68.73 36.85 63.83 53.84 51.08 32.83 79.77 72.3 50.21 52.94 47.94 53.09 58.47 34.13 79.88 27.67 86.29 69.37 48.63 52.46 62.9 78.52 55.43 10.64 44.84 55.95 64.06 53.5 64.17 49.61 64.99 37.28 50.68 66.4 53.82 34.31 47.97 76.06 62.43 66 60.57 11.37 65.07 8.99 58.37 83.51 81.02 29.75 30.4 39.17

A) A missile launched from a submarine hits a target with a probability of 0.5. If hits from three missiles are required to destroy the target,  What is the probability that 5 missiles will be launched before the target is destroyed?  What is the expected number of missiles launched before the target is destroyed?

B) The time in days between consecutive breakdowns of a machine is exponentially distributed with a mean of 5 days. What is the probability that exactly two repairs are needed during a given week? 2

C) You arrive at a bus stop at 10:00 am, knowing that the bus will arrive at some time uniformly distributed between 10:00 and 10:30 am. What is the probability that you will have to wait longer than 5 minutes for the bus to arrive?

D) The time taken to complete an exam follows a normal probability distribution with a mean of 60 minutes and standard deviation of 10 minutes. What is the probability that a randomly chosen student will complete the exam in 50 minutes or longer?

STEP BY STEP

Please try to type your solution for this question, so I can read it without a problem. I truly appreciate you for typing in advance.

The Question:

An FBI survey shows that about 80% of all property crimes go unsolved. Suppose that in your town 3 such crimes are committed and they are each deemed independent of each other. X is the number of crimes will be solved in your town. Complete the table below for the probability mass function and cumulative probability function of the random variable X using the probabilities listed below.

1. (5 points) The values missing from the table are:

2. (5 points) Find the expected number of crimes will be solved in your town.

Perform a linear correlation study on the highest paid basketball players for the 2018-19 season. What is the correlation between the average points they score per game and their salary?

1) what is the linear regression equation and line and interpret the slope and y-intercept

2) Find the correlation coefficient of the data and interpret the correlation coefficient

3) Summarize the statistical results

Assume one has estimated a regression equation of salary (dependent variable) against years of education independent variable). How would one go about expanding the regression model to also estimate the gender effect (i.e., the average difference between male and female salary, given the same level of education? Explain how you would construct the additional variable and how you would interpret it, given how you construct it. How would you construct a hypothesis test to determine if the estimated gender-based differential is statistically significant?

The data below show sport preference and age of participant from a random sample of members of a sports club. Test if sport preference is independent of age at the 0.05 significant level.

H₀: Sport preference is independent of age
Ha: Sport preference is dependent on age

a. Complete the table: Give all answers as decimals rounded to 4 places.

(b) What is the chi-square test-statistic for this data?
      Test Statistic: χ2=χ2=  

(d) The p-value is...

• less than (or equal to) αα
• greater than αα

(e) The p-value leads to a decision to...

• reject the null
• fail to reject the null

(f) What is the final conclusion?

• There is sufficient evidence to conclude sport preference is dependent on age.
• There is not sufficient evidence to conclude sport preference is dependent on age.

Never forget that even small effects can be statistically significant if the samples are large. To illustrate this fact, consider a sample of 104 small businesses. During a three-year period, 10 of the 71 headed by men and 6 of the 33 headed by women failed.

(a) Find the proportions of failures for businesses headed by women and businesses headed by men. These sample proportions are quite close to each other. Give the P-value for the test of the hypothesis that the same proportion of women's and men's businesses fail. (Use the two-sided alternative). What can we conclude (Use α=0.05α=0.05)?
The P-value was so we conclude that
Choose a conclusion.The test showed strong evidence of a significant difference.The test showed no significant difference.

(b) Now suppose that the same sample proportion came from a sample 30 times as large. That is, 180 out of 990 businesses headed by women and 300 out of 2130 businesses headed by men fail. Verify that the proportions of failures are exactly the same as in part (a). Repeat the test for the new data. What can we conclude?
The P-value was so we conclude that
Choose a conclusion.The test showed strong evidence of a significant difference.The test showed no significant difference.

(c) It is wise to use a confidence interval to estimate the size of an effect rather than just giving a P-value. Give 95% confidence intervals for the difference between proportions of men's and women's businesses (men minus women) that fail for the settings of both (a) and (b). (Be sure to check that the conditions are met. If the conditions aren't met for one of the intervals, use the same type of interval for both)
Interval for smaller samples:___ to ___
Interval for larger samples: ___to ___

What is the effect of larger samples on the confidence interval?
Choose an effect.The confidence interval is unchanged.The confidence interval's margin of error is reduced.The confidence interval's margin of error is increased.

A company manufactures x units of Product A and y units of Product B, on two machines, I and II. It has been determined that the company will realize a profit of $3/unit of Product A and a profit of $6/unit of Product B. To manufacture a unit of Product A requires 6 min on Machine I and 5 min on Machine II. To manufacture a unit of Product B requires 9 min on Machine I and 4 min on Machine II. There are 5 hr of machine time available on Machine I and 3 hr of machine time available on Machine II in each work shift. How many units of each product should be produced in each shift to maximize the company's profit?

a). (x,y) =

b). What is the optimal profit? (Round your answer to the nearest whole number.)

The average number of cocktails that residents of my nursing home drink weekly is normally distributed with mean of 12 cocktails with a standard deviation of 3 cocktails. What is the percentile rank of a resident who drinks 11.2 cocktails weekly?

about 32% about 11% about 61% about 5% about 95% about 18% about 39% about 82%

Please Estimate the regression line for the model. The two data sets are below. Thanks.

The following data were obtained from a two-factor independent-measures experiment with  n = 5 participants in each treatment condition. ​What is the F-value for the interaction/AxB effect
​
                                               B1                       B2                     B3