
To divide: The 28 storms into 4 subgroups.
To select: A random sample of 3 storms from each group.
To compute: The means for maximum wind speeds.
To compare: The means to the population

Explanation of Solution
Given info:
In the 2005 Atlantic hurricane season the maximum wind speed and classifications for different storms are given in the data.
Calculation:
First arrange the data according to the serial number,
Serial No. | Name | Max. wind | Classification |
1 | Arlene | 70 | Storm |
2 | Bret | 40 | S |
3 | Cindy | 75 | Hurricane |
4 | Dennis | 150 | H |
5 | Emily | 160 | H |
6 | Franklin | 70 | S |
7 | Gert | 45 | S |
8 | Harvey | 65 | S |
9 | Irene | 105 | H |
10 | Jose | 50 | S |
11 | Katrina | 175 | H |
12 | Lee | 40 | S |
13 | Maria | 115 | H |
14 | Nate | 90 | H |
15 | Ophelia | 85 | H |
16 | Philippe | 70 | H |
17 | Rita | 175 | H |
18 | Stan | 80 | H |
19 | Unnamed | 50 | S |
20 | Tammy | 50 | S |
21 | Vince | 75 | H |
22 | Wilma | 175 | H |
23 | Alpha | 50 | S |
24 | Beta | 115 | H |
25 | Gamma | 55 | S |
26 | Delta | 70 | S |
27 | Epsilon | 85 | H |
28 | Zeta | 65 | S |
Procedure for dividing the 28 storms into 4 subgroups:
Step1:
Divide the population into two groups. From the 28 storms, divide the observations according to classification- Storm and Hurricane. There are 15 Hurricanes and 13 Storms:
Group 1 (H) | Group 2 (S) | ||
Name | Max. wind | Name | Max. wind |
Cindy | 75 | Arlene | 70 |
Dennis | 150 | Bret | 40 |
Emily | 160 | Franklin | 70 |
Irene | 105 | Gert | 45 |
Katrina | 175 | Harvey | 65 |
Maria | 115 | Jose | 50 |
Nate | 90 | Lee | 40 |
Ophelia | 85 | Unnamed | 50 |
Philippe | 70 | Tammy | 50 |
Rita | 175 | Alpha | 50 |
Stan | 80 | Gamma | 55 |
Vince | 75 | Delta | 70 |
Wilma | 175 | Zeta | 65 |
Beta | 115 | ||
Epsilon | 85 |
Step2:
Divide each group into two subgroups, based on the
For Group 1 (H), the median value of max. wind is 105. Thus, the 1st subgroup will contain all storms with max. wind less than or equal to 105 and the 2nd subgroup will contain all storms with max. wind greater than 105.
For Group 2 (S), the median value of max. wind is 50. Thus, the 3rd subgroup will contain all storms with max. wind less than or equal to 50 and the 4th subgroup will contain all storms with max. wind greater than 50.
The subgroups are:
SNO | Subgroup1 | Subgroup2 | Subgroup3 | Subgroup4 |
1 | Cindy | Dennis | Bret | Arlene |
2 | Irene | Emily | Gert | Franklin |
3 | Nate | Katrina | Jose | Harvey |
4 | Ophelia | Maria | Lee | Gamma |
5 | Philippe | Rita | Unnamed | Delta |
6 | Stan | Wilma | Tammy | Zeta |
7 | Vince | Beta | Alpha | |
8 | Epsilon |
From the s select a random sample of 3 storms by using random numbers.
Procedure for selecting 3 random samples from subgroup 1:
- From the figure 14.1(Table of random numbers), select a starting point.
- Here, the selected starting point is ‘7’ which is present in 1st row and 1st column. There are 8 names of storms in subgroup 1. Hence, select the random numbers in the
range of ‘1’ to ‘8. Avoid repetition of numbers and 0. - The random numbers starting from ‘7’ are:
7, 2, 1.
Thus, the random numbers are and corresponding names of the wind are,
Serial No. | Name |
7 | Vince |
2 | Irene |
1 | Cindy |
The average maximum wind of above selected sample is calculated by using the following formula.
Thus, the sample mean for subgroup 1 is 85.
Procedure for selecting 3 random samples from subgroup 2:
- From the figure 14.1(Table of random numbers), select a starting point.
- Here, the selected starting point is ‘4’ which is present in 1st row and 2nd column. There are 7 names of storms in subgroup 2. Hence, select the random numbers in the range of ‘1’ to ‘7’. Avoid repetition of numbers and 0.
- The random numbers starting from ‘4’ are:
4, 5, 1.
Thus, the random numbers are and corresponding names of the wind are,
Serial No. | Name |
4 | Maria |
5 | Rita |
1 | Dennis |
The average maximum wind of above selected sample is calculated by using the following formula.
Thus, the sample mean for subgroup 2 is 146.67.
Procedure for selecting 3 random samples from subgroup 3:
- From the figure 14.1(Table of random numbers), select a starting point.
- Here, the selected starting point is ‘7’ which is present in 5th row and 13th column. There are 7 names of storms in subgroup 3. Hence, select the random numbers in the range of ‘1’ to ‘7’. Avoid repetition of numbers and 0.
- The random numbers starting from ‘7’ are:
7, 4, 2.
Thus, the random numbers are and corresponding names of the wind are,
Serial No. | Name |
4 | Alpha |
5 | Lee |
1 | Gret |
The average maximum wind of above selected sample is calculated by using the following formula.
Thus, the sample mean for subgroup 3 is 45.
Procedure for selecting 3 random samples from subgroup 4:
- From the figure 14.1(Table of random numbers), select a starting point.
- Here, the selected starting point is ‘5’ which is present in 6th row and 9th column. There are 6 names of storms in subgroup 4. Hence, select the random numbers in the range of ‘1’ to ‘6’. Avoid repetition of numbers and 0.
- The random numbers starting from ‘5’ are:
5, 2, 6.
Thus, the random numbers are and corresponding names of the wind are,
Serial No. | Name |
5 | Delta |
2 | Franklin |
6 | Zeta |
The average maximum wind of above selected sample is calculated by using the following formula.
Thus, the sample mean for subgroup 4 is 68.3.
The population mean of maximum wind is,
Thus, the population mean is 87.5.
Comparison of means:
The sample mean for subgroup 1 is 85 and the population mean of maximum wind speed is 87.5.
The sample mean for subgroup 2 is 146.67 and the population mean of maximum wind speed is 87.5.
The sample mean for subgroup 3 is 45 and the population mean of maximum wind speed is 87.5.
The sample mean for subgroup 4 is 68.3 and the population mean of maximum wind speed is 87.5.
That implies that the sample means are smaller than the population mean for subgroup 3 an 4 whereas the sample mean is greater than the population mean for subgroup 2. But, for subgroup 1 sample mean is slightly smaller than population mean.
It is clear that there is some difference between the sample mean and the population mean.
Want to see more full solutions like this?
Chapter 14 Solutions
Elementary Statistics: A Step-by-Step Approach with Formula Card
- ian income of $50,000. erty rate of 13. Using data from 50 workers, a researcher estimates Wage = Bo+B,Education + B₂Experience + B3Age+e, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. A portion of the regression results is shown in the following table. ni ogolloo bash 1 Standard Coefficients error t stat p-value Intercept 7.87 4.09 1.93 0.0603 Education 1.44 0.34 4.24 0.0001 Experience 0.45 0.14 3.16 0.0028 Age -0.01 0.08 -0.14 0.8920 a. Interpret the estimated coefficients for Education and Experience. b. Predict the hourly wage rate for a 30-year-old worker with four years of higher education and three years of experience.arrow_forward1. If a firm spends more on advertising, is it likely to increase sales? Data on annual sales (in $100,000s) and advertising expenditures (in $10,000s) were collected for 20 firms in order to estimate the model Sales = Po + B₁Advertising + ε. A portion of the regression results is shown in the accompanying table. Intercept Advertising Standard Coefficients Error t Stat p-value -7.42 1.46 -5.09 7.66E-05 0.42 0.05 8.70 7.26E-08 a. Interpret the estimated slope coefficient. b. What is the sample regression equation? C. Predict the sales for a firm that spends $500,000 annually on advertising.arrow_forwardCan you help me solve problem 38 with steps im stuck.arrow_forward
- How do the samples hold up to the efficiency test? What percentages of the samples pass or fail the test? What would be the likelihood of having the following specific number of efficiency test failures in the next 300 processors tested? 1 failures, 5 failures, 10 failures and 20 failures.arrow_forwardThe battery temperatures are a major concern for us. Can you analyze and describe the sample data? What are the average and median temperatures? How much variability is there in the temperatures? Is there anything that stands out? Our engineers’ assumption is that the temperature data is normally distributed. If that is the case, what would be the likelihood that the Safety Zone temperature will exceed 5.15 degrees? What is the probability that the Safety Zone temperature will be less than 4.65 degrees? What is the actual percentage of samples that exceed 5.25 degrees or are less than 4.75 degrees? Is the manufacturing process producing units with stable Safety Zone temperatures? Can you check if there are any apparent changes in the temperature pattern? Are there any outliers? A closer look at the Z-scores should help you in this regard.arrow_forwardNeed help pleasearrow_forward
- Please conduct a step by step of these statistical tests on separate sheets of Microsoft Excel. If the calculations in Microsoft Excel are incorrect, the null and alternative hypotheses, as well as the conclusions drawn from them, will be meaningless and will not receive any points. 4. One-Way ANOVA: Analyze the customer satisfaction scores across four different product categories to determine if there is a significant difference in means. (Hints: The null can be about maintaining status-quo or no difference among groups) H0 = H1=arrow_forwardPlease conduct a step by step of these statistical tests on separate sheets of Microsoft Excel. If the calculations in Microsoft Excel are incorrect, the null and alternative hypotheses, as well as the conclusions drawn from them, will be meaningless and will not receive any points 2. Two-Sample T-Test: Compare the average sales revenue of two different regions to determine if there is a significant difference. (Hints: The null can be about maintaining status-quo or no difference among groups; if alternative hypothesis is non-directional use the two-tailed p-value from excel file to make a decision about rejecting or not rejecting null) H0 = H1=arrow_forwardPlease conduct a step by step of these statistical tests on separate sheets of Microsoft Excel. If the calculations in Microsoft Excel are incorrect, the null and alternative hypotheses, as well as the conclusions drawn from them, will be meaningless and will not receive any points 3. Paired T-Test: A company implemented a training program to improve employee performance. To evaluate the effectiveness of the program, the company recorded the test scores of 25 employees before and after the training. Determine if the training program is effective in terms of scores of participants before and after the training. (Hints: The null can be about maintaining status-quo or no difference among groups; if alternative hypothesis is non-directional, use the two-tailed p-value from excel file to make a decision about rejecting or not rejecting the null) H0 = H1= Conclusion:arrow_forward
- Please conduct a step by step of these statistical tests on separate sheets of Microsoft Excel. If the calculations in Microsoft Excel are incorrect, the null and alternative hypotheses, as well as the conclusions drawn from them, will be meaningless and will not receive any points. The data for the following questions is provided in Microsoft Excel file on 4 separate sheets. Please conduct these statistical tests on separate sheets of Microsoft Excel. If the calculations in Microsoft Excel are incorrect, the null and alternative hypotheses, as well as the conclusions drawn from them, will be meaningless and will not receive any points. 1. One Sample T-Test: Determine whether the average satisfaction rating of customers for a product is significantly different from a hypothetical mean of 75. (Hints: The null can be about maintaining status-quo or no difference; If your alternative hypothesis is non-directional (e.g., μ≠75), you should use the two-tailed p-value from excel file to…arrow_forwardPlease conduct a step by step of these statistical tests on separate sheets of Microsoft Excel. If the calculations in Microsoft Excel are incorrect, the null and alternative hypotheses, as well as the conclusions drawn from them, will be meaningless and will not receive any points. 1. One Sample T-Test: Determine whether the average satisfaction rating of customers for a product is significantly different from a hypothetical mean of 75. (Hints: The null can be about maintaining status-quo or no difference; If your alternative hypothesis is non-directional (e.g., μ≠75), you should use the two-tailed p-value from excel file to make a decision about rejecting or not rejecting null. If alternative is directional (e.g., μ < 75), you should use the lower-tailed p-value. For alternative hypothesis μ > 75, you should use the upper-tailed p-value.) H0 = H1= Conclusion: The p value from one sample t-test is _______. Since the two-tailed p-value is _______ 2. Two-Sample T-Test:…arrow_forwardPlease conduct a step by step of these statistical tests on separate sheets of Microsoft Excel. If the calculations in Microsoft Excel are incorrect, the null and alternative hypotheses, as well as the conclusions drawn from them, will be meaningless and will not receive any points. What is one sample T-test? Give an example of business application of this test? What is Two-Sample T-Test. Give an example of business application of this test? .What is paired T-test. Give an example of business application of this test? What is one way ANOVA test. Give an example of business application of this test? 1. One Sample T-Test: Determine whether the average satisfaction rating of customers for a product is significantly different from a hypothetical mean of 75. (Hints: The null can be about maintaining status-quo or no difference; If your alternative hypothesis is non-directional (e.g., μ≠75), you should use the two-tailed p-value from excel file to make a decision about rejecting or not…arrow_forward
- MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning
- Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman





