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Problem #42 Statistics Grade:
In a large section of a statistics class, the points for the final exam are normally distributed, with a mean of 72 and a standard deviation of 9. Grades are to be assigned according to the following rule:
- The top 10% receive A's.
- The next 20% receive B's.
- The middle 40% receive C's.
- The next 20% received D's.
- The bottom 10% receive F's.
Find the lowest score on the final exam that would qualify a student for an A, a B, a C, and a D. (Round your answers to two decimal places.)
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Res 710 discussion posts
PART 1. Frankfort-Nachmias and Leon-Guerrero (2018) noted that the precision of a confidence interval can be enhanced by ...
Res 710 discussion posts
PART 1. Frankfort-Nachmias and Leon-Guerrero (2018) noted that the precision of a confidence interval can be enhanced by increasing the sample size. Write a 250- to 300-word response to the following: What is the role of sample size in the calculation of confidence intervals? What factors might determine the size of an ideal sample in your dissertation study? Reference: Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: SAGE Publications, Inc. PART 2. Reply in 150 or more words Here is something I learned recently. I had a student who used a second party data collection cite. He received the data and sample size he needed but most of the data was filled out as all zeros or all 10s as the participant did not actually take the survey and just filled it out quickly. Do you think this data should count and what can we do if this occurs? PART 3 Write a 250- to 300-word response to the following: How can estimation errors threaten the validity of a study? Cite specific examples from your experience to support your response. PART 4 Reply In at least 150 words. When we look at estimating errors, this can be a key reflection of the validity of the sample data. For example, as systematic estimation errors increase validity decreases. Likewise, as systematic estimation errors decrease, validity increases. Why do you think that is?
South Western College Weight IAT Variables Questions
(9) Weight IAT Variables
Quantitative Variable
IAT-Weight-Score: Score on the Weight IAT
Categorical Variable (choose one) ...
South Western College Weight IAT Variables Questions
(9) Weight IAT Variables
Quantitative Variable
IAT-Weight-Score: Score on the Weight IAT
Categorical Variable (choose one)
Race: 1=American Indian/Alaska Native; 2=East Asian; 3=South Asian; 4=Native Hawaiian or other Pacific Islander; 5=Black or African American; 6=White; 7=Other or Unknown; 8=Multiracial
Political-ID: 1=strongly conservative; 2=moderately conservative; 3=slightly conservative; 4=neutral; 5=slightly liberal; 6=moderately liberal; 7=strongly liberal
Religiosity: 1=Not at all; 2=Slightly; 3=Moderately; 4=Very; 5=Extremely
Prefers: Subject reports: 1=Strong preference for fat people; 2=Moderate preference for fat people; 3=Slight preference for fat people; 4=Likes thin people and fat people equally; 5=Slight preference for thin people; 6=Moderate preference for thin people; 7=Strong preference for thin people
Most-Prefer: Subject’s perception of what most people prefer: 1=Strong preference for fat people; 2=Moderate preference for fat people; 3=Slight preference for fat people; 4=Likes thin people and fat people equally; 5=Slight preference for thin people; 6=Moderate preference for thin people; 7=Strong preference for thin people
Body-Image: Subject’s reported body image: 1=Very underweight; 2=Moderately underweight; 3=Slightly underweight; 4=Neither underweight nor overweight; 5=Slightly overweight; 6=Moderately overweight; 7=Very overweight
Important: Importance of weight to subject’s sense of self: 1=Not at all important; 2=Moderately unimportant; 3=Somewhat unimportant; 4=Neither unimportant nor important; 5=Somewhat important; 6=Moderately important; 7=Very important
Weight IAT variable descriptions (opens in a new tab).
Prompt
Work through each of the following items to conduct an ANOVA F-test using the variables listed above for your unique IAT sample.
What is the explanatory variable, and what is the response variable?
What are the populations for the F-test?
State your hypotheses.
Create side-by-side (or stacked) boxplots for the quantitative variable (IAT Score) grouped by your chosen categorical variable. Select the option to display the mean within the boxplots (directions) .
Download the StatCrunch output window (your boxplots) and embed the .png file with your response.
Do the boxplots suggest that the samples come from populations with different means? Briefly explain.
Next, we need to create a table with these summary statistics: sample size, mean, and standard deviation for each of the populations you listed above. To do this, use StatCrunch to create a table of the indicated summary statistics for the quantitative variable (IAT Score) grouped by your chosen categorical variable. The summary statistics should be listed in the order given with no other statistics in your table.
Copy the table in the StatCrunch output window and paste it into your response.
To make your table readily understood by any reader, complete each of the following.
Enter a descriptive title above your table.
In your table, each group from your chosen categorical variable is labeled with a number. A reader will not understand what the number represents. Replace the numeric labels with descriptive words for each group of your selected categorical variable (see the Variables section above for your data set).
Determine whether conditions are met to use the ANOVA F-test. For each condition explain why the condition is met or not met.
Conducting the ANOVA F-test at the 5% significance level:
If conditions are met, use StatCrunch to conduct the ANOVA F-test (copy and paste the contents of the StatCrunch output window into your response). Identify the F-statistic and the P-value. Then state your conclusion in context.
If conditions are not met for your selected categorical variable, start over and use the list of categorical variables provided in the Variables section above to select a different categorical variable for which conditions are met.
Exercise 26
Exercise 261. Plot the frequency distribution for “Age at Enrollment” by hand or by using SPSS. 2. How would you cha ...
Exercise 26
Exercise 261. Plot the frequency distribution for “Age at Enrollment” by hand or by using SPSS. 2. How would you characterize the skewness of the distribution in Question 1—positively skewed, negatively skewed, or approximately normal? Provide a rationale for your answer. 3. Compare the original skewness statistic and Shapiro-Wilk statistic with those of the smaller dataset ( n = 15) for the variable “Age at First Arrest.” How did the statistics change, and how would you explain these differences? 4. Plot the frequency distribution for “Years of Education” by hand or by using SPSS. 5. How would you characterize the kurtosis of the distribution in Question 4—leptokurtic, mesokur-tic, or platykurtic? Provide a rationale for your answer. 6. What is the skewness statistic for “Age at Enrollment”? How would you characterize the magnitude of the skewness statistic for “Age at Enrollment”? 7. What is the kurtosis statistic for “Years of Education”? How would you characterize the magnitude of the kurtosis statistic for “Years of Education”? 8. Using SPSS, compute the Shapiro-Wilk statistic for “Number of Times Fired from Job.” What would you conclude from the results? 9. In the SPSS output table titled “Tests of Normality,” the Shapiro-Wilk statistic is reported along with the Kolmogorov-Smirnov statistic. Why is the Kolmogorov-Smirnov statistic inappropriate to report for these example data? 10. How would you explain the skewness statistic for a particular frequency distribution being low and the Shapiro-Wilk statistic still being significant at p < 0.05?
11 pages
M Confidence Intervals For The Population Mean Start
The present study shows data for delivery times of international shipments for a logistics company. The company promotes i ...
M Confidence Intervals For The Population Mean Start
The present study shows data for delivery times of international shipments for a logistics company. The company promotes its international delivery as ...
MAT 210 SU Week 4 Foundation of Data Driven Decisions Questions
Problem Solving - Data Analysis (Mathematical Reasoning)
You’ll practice your problem solving skill by ident ...
MAT 210 SU Week 4 Foundation of Data Driven Decisions Questions
Problem Solving - Data Analysis (Mathematical Reasoning)
You’ll practice your problem solving skill by identifying foundational statistical concepts and explaining how data-driven decision making can help inform real world problems. This will provide you with a solid foundation that will help you to not only be successful in this course, but to learn to make smarter, data-driven decisions in your personal and professional life.
Practice your problem solving skill by answering questions about statistical concepts and the benefits and uses of data-driven decision making.
Steps to Complete:
STEP 1: Answer the questions below in a Word document.
STEP 2: Save and submit your Word document in the Assignment link in the Week 3 Submit page in BlackBoard.
1. Explain the difference between descriptive and inferential statistical methods and give an example of how each could help you draw a conclusion in the real world.
2. You would like to determine whether eating before bed influences sleep patterns. List each step you would take to conduct a statistical study on this topic and explain what you would do to complete each step. Then, answer the questions below.
What is your hypothesis on this issue?
What type of data will you be looking for?
What methods would you use to gather information?
How would the results of the data influence decisions you might make about eating and sleeping?
3. A company that sells tea and coffee claims that drinking two cups of green tea daily has been shown to increase mood and well-being. This claim is based on surveys asking customers to rate their mood on a scale of 1–10 after days they drink/do not drink different types of tea. Based on this information, answer the following questions:
How would we know if this data is valid and reliable?
What questions would you ask to find out more about the quality of the data?
Why is it important to gather and report valid and reliable data?
4. Identify two examples of real-world problems that you have observed in your personal, academic, or professional life that could benefit from data driven solutions. Explain how you would use data/statistics and the steps you would take to analyze each problem. You may also choose topics below (or examples from the weekly content) to help support your response:
Productivity at work.
Financial decisions and budgeting.
Health and nutrition.
Political campaigns.
Quality testing in products.
Human resource policies.
Algorithms for programming/coding.
Accounting & financial policies.
Crime reduction and trends.
Environmental protection / Emergency preparedness.
5. How does analyzing data on these real-world problems aid in problem-solving and drawing conclusions? Be sure to note the value and benefits of data-driven decision-making.
msn601 homework 1
Directions:(a) Enter all your answers into this Word document in the space immediately following each question and submi ...
msn601 homework 1
Directions:(a) Enter all your answers into this Word document in the space immediately following each question and submit the entire document.IMPORTANT:The name of your Word document MUST begin with your LAST NAME.(b) When you use a template to calculate an answer, copy and paste the results into this document.(c) While putting your answers in a different color font is helpful, please DO NOT use red font since this is the color used to provide feedback on your answers.
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Most Popular Content
Res 710 discussion posts
PART 1. Frankfort-Nachmias and Leon-Guerrero (2018) noted that the precision of a confidence interval can be enhanced by ...
Res 710 discussion posts
PART 1. Frankfort-Nachmias and Leon-Guerrero (2018) noted that the precision of a confidence interval can be enhanced by increasing the sample size. Write a 250- to 300-word response to the following: What is the role of sample size in the calculation of confidence intervals? What factors might determine the size of an ideal sample in your dissertation study? Reference: Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: SAGE Publications, Inc. PART 2. Reply in 150 or more words Here is something I learned recently. I had a student who used a second party data collection cite. He received the data and sample size he needed but most of the data was filled out as all zeros or all 10s as the participant did not actually take the survey and just filled it out quickly. Do you think this data should count and what can we do if this occurs? PART 3 Write a 250- to 300-word response to the following: How can estimation errors threaten the validity of a study? Cite specific examples from your experience to support your response. PART 4 Reply In at least 150 words. When we look at estimating errors, this can be a key reflection of the validity of the sample data. For example, as systematic estimation errors increase validity decreases. Likewise, as systematic estimation errors decrease, validity increases. Why do you think that is?
South Western College Weight IAT Variables Questions
(9) Weight IAT Variables
Quantitative Variable
IAT-Weight-Score: Score on the Weight IAT
Categorical Variable (choose one) ...
South Western College Weight IAT Variables Questions
(9) Weight IAT Variables
Quantitative Variable
IAT-Weight-Score: Score on the Weight IAT
Categorical Variable (choose one)
Race: 1=American Indian/Alaska Native; 2=East Asian; 3=South Asian; 4=Native Hawaiian or other Pacific Islander; 5=Black or African American; 6=White; 7=Other or Unknown; 8=Multiracial
Political-ID: 1=strongly conservative; 2=moderately conservative; 3=slightly conservative; 4=neutral; 5=slightly liberal; 6=moderately liberal; 7=strongly liberal
Religiosity: 1=Not at all; 2=Slightly; 3=Moderately; 4=Very; 5=Extremely
Prefers: Subject reports: 1=Strong preference for fat people; 2=Moderate preference for fat people; 3=Slight preference for fat people; 4=Likes thin people and fat people equally; 5=Slight preference for thin people; 6=Moderate preference for thin people; 7=Strong preference for thin people
Most-Prefer: Subject’s perception of what most people prefer: 1=Strong preference for fat people; 2=Moderate preference for fat people; 3=Slight preference for fat people; 4=Likes thin people and fat people equally; 5=Slight preference for thin people; 6=Moderate preference for thin people; 7=Strong preference for thin people
Body-Image: Subject’s reported body image: 1=Very underweight; 2=Moderately underweight; 3=Slightly underweight; 4=Neither underweight nor overweight; 5=Slightly overweight; 6=Moderately overweight; 7=Very overweight
Important: Importance of weight to subject’s sense of self: 1=Not at all important; 2=Moderately unimportant; 3=Somewhat unimportant; 4=Neither unimportant nor important; 5=Somewhat important; 6=Moderately important; 7=Very important
Weight IAT variable descriptions (opens in a new tab).
Prompt
Work through each of the following items to conduct an ANOVA F-test using the variables listed above for your unique IAT sample.
What is the explanatory variable, and what is the response variable?
What are the populations for the F-test?
State your hypotheses.
Create side-by-side (or stacked) boxplots for the quantitative variable (IAT Score) grouped by your chosen categorical variable. Select the option to display the mean within the boxplots (directions) .
Download the StatCrunch output window (your boxplots) and embed the .png file with your response.
Do the boxplots suggest that the samples come from populations with different means? Briefly explain.
Next, we need to create a table with these summary statistics: sample size, mean, and standard deviation for each of the populations you listed above. To do this, use StatCrunch to create a table of the indicated summary statistics for the quantitative variable (IAT Score) grouped by your chosen categorical variable. The summary statistics should be listed in the order given with no other statistics in your table.
Copy the table in the StatCrunch output window and paste it into your response.
To make your table readily understood by any reader, complete each of the following.
Enter a descriptive title above your table.
In your table, each group from your chosen categorical variable is labeled with a number. A reader will not understand what the number represents. Replace the numeric labels with descriptive words for each group of your selected categorical variable (see the Variables section above for your data set).
Determine whether conditions are met to use the ANOVA F-test. For each condition explain why the condition is met or not met.
Conducting the ANOVA F-test at the 5% significance level:
If conditions are met, use StatCrunch to conduct the ANOVA F-test (copy and paste the contents of the StatCrunch output window into your response). Identify the F-statistic and the P-value. Then state your conclusion in context.
If conditions are not met for your selected categorical variable, start over and use the list of categorical variables provided in the Variables section above to select a different categorical variable for which conditions are met.
Exercise 26
Exercise 261. Plot the frequency distribution for “Age at Enrollment” by hand or by using SPSS. 2. How would you cha ...
Exercise 26
Exercise 261. Plot the frequency distribution for “Age at Enrollment” by hand or by using SPSS. 2. How would you characterize the skewness of the distribution in Question 1—positively skewed, negatively skewed, or approximately normal? Provide a rationale for your answer. 3. Compare the original skewness statistic and Shapiro-Wilk statistic with those of the smaller dataset ( n = 15) for the variable “Age at First Arrest.” How did the statistics change, and how would you explain these differences? 4. Plot the frequency distribution for “Years of Education” by hand or by using SPSS. 5. How would you characterize the kurtosis of the distribution in Question 4—leptokurtic, mesokur-tic, or platykurtic? Provide a rationale for your answer. 6. What is the skewness statistic for “Age at Enrollment”? How would you characterize the magnitude of the skewness statistic for “Age at Enrollment”? 7. What is the kurtosis statistic for “Years of Education”? How would you characterize the magnitude of the kurtosis statistic for “Years of Education”? 8. Using SPSS, compute the Shapiro-Wilk statistic for “Number of Times Fired from Job.” What would you conclude from the results? 9. In the SPSS output table titled “Tests of Normality,” the Shapiro-Wilk statistic is reported along with the Kolmogorov-Smirnov statistic. Why is the Kolmogorov-Smirnov statistic inappropriate to report for these example data? 10. How would you explain the skewness statistic for a particular frequency distribution being low and the Shapiro-Wilk statistic still being significant at p < 0.05?
11 pages
M Confidence Intervals For The Population Mean Start
The present study shows data for delivery times of international shipments for a logistics company. The company promotes i ...
M Confidence Intervals For The Population Mean Start
The present study shows data for delivery times of international shipments for a logistics company. The company promotes its international delivery as ...
MAT 210 SU Week 4 Foundation of Data Driven Decisions Questions
Problem Solving - Data Analysis (Mathematical Reasoning)
You’ll practice your problem solving skill by ident ...
MAT 210 SU Week 4 Foundation of Data Driven Decisions Questions
Problem Solving - Data Analysis (Mathematical Reasoning)
You’ll practice your problem solving skill by identifying foundational statistical concepts and explaining how data-driven decision making can help inform real world problems. This will provide you with a solid foundation that will help you to not only be successful in this course, but to learn to make smarter, data-driven decisions in your personal and professional life.
Practice your problem solving skill by answering questions about statistical concepts and the benefits and uses of data-driven decision making.
Steps to Complete:
STEP 1: Answer the questions below in a Word document.
STEP 2: Save and submit your Word document in the Assignment link in the Week 3 Submit page in BlackBoard.
1. Explain the difference between descriptive and inferential statistical methods and give an example of how each could help you draw a conclusion in the real world.
2. You would like to determine whether eating before bed influences sleep patterns. List each step you would take to conduct a statistical study on this topic and explain what you would do to complete each step. Then, answer the questions below.
What is your hypothesis on this issue?
What type of data will you be looking for?
What methods would you use to gather information?
How would the results of the data influence decisions you might make about eating and sleeping?
3. A company that sells tea and coffee claims that drinking two cups of green tea daily has been shown to increase mood and well-being. This claim is based on surveys asking customers to rate their mood on a scale of 1–10 after days they drink/do not drink different types of tea. Based on this information, answer the following questions:
How would we know if this data is valid and reliable?
What questions would you ask to find out more about the quality of the data?
Why is it important to gather and report valid and reliable data?
4. Identify two examples of real-world problems that you have observed in your personal, academic, or professional life that could benefit from data driven solutions. Explain how you would use data/statistics and the steps you would take to analyze each problem. You may also choose topics below (or examples from the weekly content) to help support your response:
Productivity at work.
Financial decisions and budgeting.
Health and nutrition.
Political campaigns.
Quality testing in products.
Human resource policies.
Algorithms for programming/coding.
Accounting & financial policies.
Crime reduction and trends.
Environmental protection / Emergency preparedness.
5. How does analyzing data on these real-world problems aid in problem-solving and drawing conclusions? Be sure to note the value and benefits of data-driven decision-making.
msn601 homework 1
Directions:(a) Enter all your answers into this Word document in the space immediately following each question and submi ...
msn601 homework 1
Directions:(a) Enter all your answers into this Word document in the space immediately following each question and submit the entire document.IMPORTANT:The name of your Word document MUST begin with your LAST NAME.(b) When you use a template to calculate an answer, copy and paste the results into this document.(c) While putting your answers in a different color font is helpful, please DO NOT use red font since this is the color used to provide feedback on your answers.
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