applied calc derivatives HW28(11)
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14 W' is the rate of growth of a child in pounds per year, what does /sw' (t)dt represent?
A. The increase in the child's weight (in pounds) during his/her first five years of life.
B. The increase in the child's weight (in pounds) between the ages of 5 and 10.
C. The rate of growth of the child (in Ibs/year) during his/her first five years of life.
D. The rate of growth of the child (in Ibs/year) between the ages of 5 and 10.
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Hialeah High School Normal Distribution Statistics Discussion
Please explain the questions below:
Chapter 5:
What can you say about the probability of a certain event if (a) the ...
Hialeah High School Normal Distribution Statistics Discussion
Please explain the questions below:
Chapter 5:
What can you say about the probability of a certain event if (a) the probability is 1, (b) the probability is 0.
Chapter 6:
Explain how a nonstandard normal distribution differs from the standard normal distribution. Describe the process for finding probabilities for nonstandard normal distributions. Illustrate with examples
1) Directly and completely post the requested information.
2) Respond to another student's post explaining the reason for your agreement or disagreement or why you think the post is important.
3) Respond to two or more posts explaining the reason for your agreement or disagreement or why you think the post is important.
Student réponses 2
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Chapter 6: STUDENT REPONSE
Both are bell-shaped distributions. The mean of a standard distribution is zero, while the standard deviation is one. The mean and standard deviation of a nonstandard distribution might have different values. (SD numbers are, of course, positive) (Das & Geisler, 2021). Sketch the plot, feature the x-score of the plot, ascertain the fitting z-score, conceal the comparing area of interest, find in the table the region relating to the z-score (region among mean and z - result) and utilize this region to track down the area of interest which are concealed which ought to be important for the cycle.
The mean and variance are the two parameters that define a normal distribution. We regularly utilize the expression "arbitrary random distribution" in measurements to allude to information gathered from a normal distribution to gauge this perimeter. The standard normal distribution has a mean of 0 and a variance of 1. This is the dispersion used to make the normal distribution table.If X has a normal distribution with mean M and variance S2, we can easily define
Z=X - M/s.
The usual normal distribution is then applied to Z. So, using the tables for Z, we may compute probabilities of the type P [a< X < b] for any specified normal distribution. This is due to the fact that we may write X= sZ+m. In comparison to the overall family of normal distributions, the standard normal performs a unique function.
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Chapter 6:
A nonstandard normal distribution has two parameters: the mean and the variance. Now the standard normal distribution with a specific mean 0 and variance 1. This is the distribution that is used to construct probability tables of the normal distribution. If a random variable X follow nonstandard normal distribution with mean "m" and standard deviation "s" then, follow standard normal distribution.
MATH 107 UOM Linear Algebra Project Paper
Curve-fitting Project - Linear Model InstructionsFor this assignment, collect data exhibiting a relatively linear trend, f ...
MATH 107 UOM Linear Algebra Project Paper
Curve-fitting Project - Linear Model InstructionsFor this assignment, collect data exhibiting a relatively linear trend, find the line of best fit, plot the data and the line, interpret the slope, and use the linear equation to make a prediction. Also, find r2 (coefficient of determination) and r (correlation coefficient). Discuss your findings. Your topic may be that is related to sports, your work, a hobby, or something you find interesting. If you choose, you may use the suggestions described below. A Linear Model Example and Technology Tips are provided in separate documents.Tasks for Linear Regression Model (LR)(LR-1) Describe your topic, provide your data, and cite your source. Collect at least 8 data points. Label appropriately. (Highly recommended: Post this information in the Linear Model Project discussion as well as in your completed project. Include a brief informative description in the title of your posting. Each student must use different data.) The idea with the discussion posting is two-fold: (1) To share your interesting project idea with your classmates, and (2) To give me a chance to give you a brief thumbs-up or thumbs-down about your proposed topic and data. Sometimes students get off on the wrong foot or misunderstand the intent of the project, and your posting provides an opportunity for some feedback. Remark: Students may choose similar topics, but must have different data sets. For example, several students may be interested in a particular Olympic sport, and that is fine, but they must collect different data, perhaps from different events or different gender.(LR-2) Plot the points (x, y) to obtain a scatterplot. Use an appropriate scale on the horizontal and vertical axes and be sure to label carefully. Visually judge whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a different topic or data set.)(LR-3) Find the line of best fit (regression line) and graph it on the scatterplot. State the equation of the line.(LR-4) State the slope of the line of best fit. Carefully interpret the meaning of the slope in a sentence or two.(LR-5) Find and state the value of r2, the coefficient of determination, and r, the correlation coefficient. Discuss your findings in a few sentences. Is r positive or negative? Why? Is a line a good curve to fit to this data? Why or why not? Is the linear relationship very strong, moderately strong, weak, or nonexistent?(LR-6) Choose a value of interest and use the line of best fit to make an estimate or prediction. Show calculation work.(LR-7) Write a brief narrative of a paragraph or two. Summarize your findings and be sure to mention any aspect of the linear model project (topic, data, scatterplot, line, r, or estimate, etc.) that you found particularly important or interesting. You may submit all of your project in one document or a combination of documents, which may consist of word processing documents or spreadsheets or scanned handwritten work, provided it is clearly labeled where each task can be found. Be sure to include your name. Projects are graded on the basis of completeness, correctness, ease in locating all of the checklist items, and strength of the narrative portions.Topic: Choose a particular type of food. (Examples: Fish sandwich at fast-food chains, cheese pizza, breakfast cereal) For at least 8 brands, look up the fat content and the associated calorie total per serving. Make a quick plot for yourself to "eyeball" whether the data exhibit a relatively linear trend. (If so, proceed. If not, try a different type of food.) After you find the line of best fit, use your line to make a prediction corresponding to a fat amount not occurring in your data set.) Alternative: Look up carbohydrate content and associated calorie total per serving.I have also attached an sample.
American InterContinental University Training of Statisticians Presentation
This is a two part assignment.Discussion:Create a comprehensive explanation of a future analysis Teton Grand could run at ...
American InterContinental University Training of Statisticians Presentation
This is a two part assignment.Discussion:Create a comprehensive explanation of a future analysis Teton Grand could run at some point inthe future and how significance testing, confidence intervals and standard error of measurementwould help them understand the results. Your post should be in a client-ready format, usingprofessional and language appropriate for talking to an organization.The assignment should be posted as a video (must include narration) of you describing the futureanalysis for Teton Grand. Assignment:Teton Grand is interested in hiring a statistician intern for the summer to help them analyze data. Yourrole is to explain statistical concepts to the intern so that he/she has a good foundation for running theseanalyses moving forward.You must explain the purpose and use of significance testing, confidence intervals and standard error ofmeasurement using examples for how each concept could be used in relation to Teton Grand’s data.You are not expected to run any analyses this week but this exercise should highlight your ability to talkcredibly about the value of each statistical concept. You must go beyond simply defining the concepts totalking about their relevance to Teton Grand.This can be in a video format, presentation, or step-by-step handout. It must be client-ready,professionally formatted, and easy to understand. If creating a video, length should be approximately 5-10 minutes. If creating a presentation, the length should be approximately 10-15 slides. Your submissionshould adhere to the APA format and guidelines.Resources:Technical Information: This assignment may require a video submission. Click here(https://resources.adler.edu/knowledgebase/my-media-and-media-gallery/) for directions. (https://adler.instructure.com/courses/12203/modules/items/366613) (https://adler.instructure.com/courses/12203/modules/items/366615) (https://adler.instructure.com/courses/12203/modules/items/366616) (https://adler.instructure.com/courses/12203/modules/items/366617) (https://adler.instructure.com/courses/12203/modules/items/366618) (https://adler.instructure.com/courses/12203/modules/items/366619) (https://adler.instructure.com/courses/12203/modules/items/366620) (https://adler.instructure.com/courses/12203/modules/items/366623) (https://adler.instructure.com/courses/12203/modules/items/366622)1/7/2021 [4.1] Statistician Intern Assignmenthttps://adler.instructure.com/courses/12203/assign... 2/4Your presentation can be created using PowerPoint, Prezi or any other multimedia tool, but it must beviewable by anyone in the class. As your instructor and classmates will most likely be a mix of Windowsand Macs user, you must export your presentation as a video and upload it to your media. If you usePrezi, you can post a link to the presentation.PowerPoint (Windows Users): https://support.office.com/en-za/article/Turn-your-presentation-into-a-video-c140551f-cb37-4818-b5d4-3e30815c3e83 (https://support.office.com/en-za/article/Turn-your-presentation-into-a-video-c140551f-cb37-4818-b5d4-3e30815c3e83)Keynote (Mac Users): https://support.apple.com/en-us/HT202220(https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsupport.apple.com%2Fen-us%2FHT202220&data=02%7C01%7Cjbailey%40adler.edu%7C40cbb75f1d8343b3f54108d76868e085%7C9(https://support.apple.com/kb/PH16971?viewlocale=en_US&locale=en_US)Sharing with Prezi: https://prezi.com/support/article/sharing/sharing-...(https://prezi.com/support/article/sharing/sharing-...
statistics ques
10.1.2Table
#10.1.6 contains the value of the house and the amount of rental income
in a year that the house brings in ...
statistics ques
10.1.2Table
#10.1.6 contains the value of the house and the amount of rental income
in a year that the house brings in ("Capital and rental," 2013). Create
a scatter plot and find a regression equation between house value and
rental income. Then use the regression equation to find the rental
income a house worth $230,000 and for a house worth $400,000. Which
rental income that you calculated do you think is closer to the true
rental income? Why?Table #10.1.6: Data of House Value versus Rental Value Rental Value Rental Value Rental Value Rental 81000 6656 77000 4576 75000 7280 67500 6864 95000 7904 94000 8736 90000 6240 85000 7072 121000 12064 115000 7904 110000 7072 104000 7904 135000 8320 130000 9776 126000 6240 125000 7904 145000 8320 140000 9568 140000 9152 135000 7488 165000 13312 165000 8528 155000 7488 148000 8320 178000 11856 174000 10400 170000 9568 170000 12688 200000 12272 200000 10608 194000 11232 190000 8320 214000 8528 208000 10400 200000 10400 200000 8320 240000 10192 240000 12064 240000 11648 225000 12480 289000 11648 270000 12896 262000 10192 244500 11232 325000 12480 310000 12480 303000 12272 300000 12480 10.1.4The
World Bank collected data on the percentage of GDP that a country
spends on health expenditures ("Health expenditure," 2013) and also the
percentage of woman receiving prenatal care ("Pregnant woman receiving,"
2013). The data for the countries where this information are available
for the year 2011 is in table #10.1.8. Create a scatter plot of the data
and find a regression equation between percentage spent on health
expenditure and the percentage of woman receiving prenatal care. Then
use the regression equation to find the percent of woman receiving
prenatal care for a country that spends 5.0% of GDP on health
expenditure and for a country that spends 12.0% of GDP. Which prenatal
care percentage that you calculated do you think is closer to the true
percentage? Why? Table #10.1.8: Data of Heath Expenditure versus Prenatal Care HealthExpenditure(% of GDP) PrenatalCare (%) 9.6 47.9 3.7 54.6 5.2 93.7 5.2 84.7 10.0 100.0 4.7 42.5 4.8 96.4 6.0 77.1 5.4 58.3 4.8 95.4 4.1 78.0 6.0 93.3 9.5 93.3 6.8 93.7 6.1 89.8 For
each problem, state the random variables. Also, look to see if there
are any outliers that need to be removed. Do the correlation analysis
with and without the suspected outlier points to determine if their
removal affects the correlation. The data sets in this section are in
section 10.1.10.2.2Table
#10.1.6 (from problem 10.1.2) contains the value of the house and the
amount of rental income in a year that the house brings in ("Capital and
rental," 2013). Find the correlation coefficient and coefficient of
determination and then interpret both.10.2.4The
World Bank collected data on the percentage of GDP that a country
spends on health expenditures ("Health expenditure," 2013) and also the
percentage of woman receiving prenatal care ("Pregnant woman receiving,"
2013). The data for the countries where this information is available
for the year 2011 are in table #10.1.8 (from problem 10.1.4). Find the
correlation coefficient and coefficient of determination and then
interpret both.For each problem, state the random
variables. The data sets in this section are in the homework for section
10.1 and were also used in section 10.2. If you removed any data points
as outliers in the other sections, remove them in this sections
homework too.10.3.2Table
#10.1.6 (from problem 10.1.2) contains the value of the house and the
amount of rental income in a year that the house brings in ("Capital and
rental," 2013).a.) Test at the 5% level for a positive correlation between house value and rental amount.b.) Find the standard error of the estimate.c.) Compute a 95% prediction interval for the rental income on a house worth $230,000.10.3.4The
World Bank collected data on the percentage of GDP that a country
spends on health expenditures ("Health expenditure," 2013) and also the
percentage of woman receiving prenatal care ("Pregnant woman receiving,"
2013). The data for the countries where this information is available
for the year 2011 are in table #10.1.8 (from problem 10.1.4).a.)
Test at the 5% level for a correlation between percentage spent on
health expenditure and the percentage of woman receiving prenatal care.b.) Find the standard error of the estimate.c.)
Compute a 95% prediction interval for the percentage of woman receiving
prenatal care for a country that spends 5.0 % of GDP on health
expenditure.In each problem show all steps of the
hypothesis test. If some of the assumptions are not met, note that the
results of the test may not be correct and then continue the process of
the hypothesis test.11.1.2Researchers
watched groups of dolphins off the coast of Ireland in 1998 to
determine what activities the dolphins partake in at certain times of
the day ("Activities of dolphin," 2013). The numbers in table #11.1.6
represent the number of groups of dolphins that were partaking in an
activity at certain times of days. Is there enough evidence to show that
the activity and the time period are independent for dolphins? Test at
the 1% level. Table #11.1.6: Dolphin Activity Activity Period RowTotal Morning Noon Afternoon Evening Travel 6 6 14 13 39 Feed 28 4 0 56 88 Social 38 5 9 10 62 ColumnTotal 72 15 23 79 189 11.1.4A
person’s educational attainment and age group was collected by the U.S.
Census Bureau in 1984 to see if age group and educational attainment
are related. The counts in thousands are in table #11.1.8 ("Education by
age," 2013). Do the data show that educational attainment and age are
independent? Test at the 5% level.Table #11.1.8: Educational Attainment and Age Group Education Age Group RowTotal 25-34 35-44 45-54 55-64 >64 Did not completeHS 5416 5030 5777 7606 13746 37575 CompletedHS 16431 1855 9435 8795 7558 44074 College 1-3year 8555 5576 3124 2524 2503 22282 College 4 or more years 9771 7596 3904 3109 2483 26863 ColumnTotal 40173 20057 22240 22034 26290 130794 In
each problem show all steps of the hypothesis test. If some of the
assumptions are not met, note that the results of the test may not be
correct and then continue the process of the hypothesis test.11.2.4In
Africa in 2011, the number of deaths of a female from cardiovascular
disease for different age groups are in table #11.2.6 ("Global health
observatory," 2013). In addition, the proportion of deaths of females
from all causes for the same age groups are also in table #11.2.6. Do
the data show that the death from cardiovascular disease are in the same
proportion as all deaths for the different age groups? Test at the 5%
level.Table #11.2.6: Deaths of Females for Different Age Groups Age 5-14 15-29 30-49 50-69 Total Cardiovascular Frequency 8 16 56 433 513 All Cause Proportion 0.10 0.12 0.26 0.52 11.2.6A
project conducted by the Australian Federal Office of Road Safety asked
people many questions about their cars. One question was the reason
that a person chooses a given car, and that data is in table #11.2.8
("Car preferences," 2013).Table #11.2.8: Reason for Choosing a Car Safety Reliability Cost Performance Comfort Looks 84 62 46 34 47 27 Do
the data show that the frequencies observed substantiate the claim that
the reason for choosing a car are equally likely? Test at the 5% level.
Statistics Testing Differences Between Means Variances & Proportions Task
A town official claims that the average vehicle in their area sells for more than the 40th percentile of your data set. Us ...
Statistics Testing Differences Between Means Variances & Proportions Task
A town official claims that the average vehicle in their area sells for more than the 40th percentile of your data set. Using the data, you obtained in week 1, as well as the summary statistics you found for the original data set, run a hypothesis test to determine if the claim can be supported. Make sure you state all the important values, so your fellow classmates can use them to run a hypothesis test as well. Use the descriptive statistics you found during Week 2 NOT the new SD you found during Week 4. Because again, we are using the original 10 sample data set NOT a new smaller sample size. Use alpha = .05 to test your claim.
(Note: You will want to use the function =PERCENTILE.INC in Excel to find the 40th percentile of your data set. Hopefully this Excel function looks familiar to you from Week 2.)
First determine if you are using a z or t-test and explain why. Then conduct a four-step hypothesis test including a sentence at the end justifying the support or lack of support for the claim and why you made that choice.
UMGC Agency Leadership Worksheet
A federal government agency that is responsible for setting vehicle fuel economy standards for automobile manufacturers is ...
UMGC Agency Leadership Worksheet
A federal government agency that is responsible for setting vehicle fuel economy standards for automobile manufacturers is conducting research in order to update its fuel economy standards for the year 2030. Automobile manufacturers, and consumers, are highly interested in what the agency's findings and determinations will be as this will affect every vehicle in the United States. The federal government agency is very interested in the relationship between the weight of a vehicle and the vehicle's fuel economy (average miles per gallon (MPG)). Specifically, the agency is concerned that if the current trend of automobile manufacturers producing heavier new vehicles continues that its fuel economy targets will not be met. The agency's research department recently collected data for analysis in order to support the agency's upcoming discussion with the industry regarding its proposed 2030 fuel economy standards. The average MPG from a random sample of 750 vehicles was recently calculated by the agency. The research division also collected the vehicle weight of these 750 randomly sampled vehicles. The Vehicle Number, Type, Vehicle Weight, Average MPG, Fuel Tank Size (Gallons), Engine Size (Liters), and Meet or Not Meet Current Standards data were collected for these 750 vehicles1. Agency leadership wants to create a simple linear regression model to predict a vehicle's average fuel economy based on the weight of the vehicle.Create a scatterplot using Vehicle Weight as the independent variable and Average MPG as the dependent variable. Perform a visual analysis of the data: describe the trend, strength, and shape of the relationship between these two variables. Choose the correct answer below.A. The scatterplot indicates there is a positive, strong, linear relationship between Vehicle Weight and Average MPG.B. The scatterplot indicates there is a positive, weak, linear relationship between Vehicle Weight and Average MPG.C. The scatterplot indicates there is a negative, strong, linear relationship between Vehicle Weight and Average MPG D. The scatterplot indicates there is a negative, weak, linear relationship between Vehicle Weight and Average MPG.E.The scatterplot indicates there is no relationship between Vehicle Weight and Average MPG.1.1. Include the scatterplot image as part of your answer. Save the image as a .jpg or .png file, then click "Show Work" and use the "Insert Image" button to upload and save your scatterplot.1.2. Based upon your visual analysis of the scatterplot, is simple linear regression appropriate? Why or why not?2. What is the correlation coefficient between Vehicle Weight and Average MPG? (Round to two decimal places)2.1. Interpret the correlation coefficient in this context.3. Using your simple linear regression model, what is the value for the slope for this regression model? (Round to decimal places)3.1. Interpret the slope in this context.4. Using your simple linear regression model, what is the value for the y-intercept for this regression model? (Round to two decimal places)4.1. Interpret the y-intercept in this context.5. Using your simple linear regression model, for each one pound increase in vehicle weight, how much would one predict the vehicle's average fuel economy to decrease? (Round to three decimal places)5.1. Using your simple linear regression model, what is the predicted Average MPG of a vehicle when the Vehicle Weight is 2700 pounds (round the slope to 3 decimal places and y-intercept to 2 decimal places)?5.2. Based upon your simple linear regression, should the agency's leadership be concerned that if new vehicles continue to be produced with heavier weights that its goal of better fuel economy will be jeopardized?6. Another facet of the weight of the car is the question of whether or not weight differs for cars that meet standards and those that do not. Create a side-by-side boxplot of the weight of cars, distinguishing between cars that meet standards and those that do not.Include the side-by-side boxplot image as part of your answer. Save the image as a .jpg or .png file, then click "Show Work" and use the "Insert Image" button to upload and save your boxplot.6.1. Describe and interpret the shapes of the two boxplots.7. One typically expects that e.g. trucks have a different fuel efficiency than passenger cars. That could be tested by a two-sample t-test, but we do not have two types of cars, but more than two. Our two-sample test won't help to compare passenger cars, SUVs, and trucks. As such, conduct an ANOVA F-test to investigate if the average fuel efficiency of cars differs between the three types of cars.Now run the One Way ANOVA. What is the value of the test statistic of this ANOVA test?Test statistic = (Round to two decimal places)7.1. What are the degrees of freedom of the test statistic of the ANOVA test?Degrees of Freedom (Total)= (Type a whole number)7.2. What is the P-value of the test statistic of the ANOVA test?P-value= (Round to three decimal places)7.3. Describe in your own words what you conclude from the outcome of this ANOVA test. Is it in line with the expectations you had?8. One typically expects that e.g. trucks have a different fuel efficiency than passenger cars. That could be tested by a two-sample t-test, but we do not have two types of cars, but more than two. Our two-sample test won't help to compare passenger cars, SUVs, and trucks.If we wish to investigate if the average fuel efficiency of cars differs between the three types of cars, what type of analysis do we need?A.Two-sample t-testB.χ2 test for independenceC. ANOVA F-testD. χ2 test for homogeneity8.1. What is the null hypothesis of the test that there is no difference in fuel efficiency between the types of cars?A. All medians of fuel efficiency are equal for the three types of carsB. All means of fuel efficiency are equal for the three types of carsC. At least one means of fuel efficiency differs from the means of the other two types of carD. All three means of fuel efficiency are different for the three types of cars8.2. What is the alternative hypothesis of the test that there is no difference in fuel efficiency between the types of cars?A. All means of fuel efficiency are equal for the three types of carsB. All medians of fuel efficiency are equal for the three types of carsC. All three means of fuel efficiency are different for the three types of carsD. At least one means of fuel efficiency differs from the means of the other two types of cars8.3. Create side-by-side boxplots in StatCrunch. What type of car has the highest variability in fuel efficiency (judge by the graph, do not calculate)?A.TrucksB. Passenger carsC. SUVs8.4. And again looking at the side-by-side boxplot only, without yet doing any calculations: do you expect to find significant differences in mean fuel efficiency between the types of cars?A. No, because differences in medians are small.B. No, because differences in medians are small relative to the amount of variation within each type.C. Yes, because differences in medians are small relative to the amount of variation within each type.D. Yes, because differences in medians are small.
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Hialeah High School Normal Distribution Statistics Discussion
Please explain the questions below:
Chapter 5:
What can you say about the probability of a certain event if (a) the ...
Hialeah High School Normal Distribution Statistics Discussion
Please explain the questions below:
Chapter 5:
What can you say about the probability of a certain event if (a) the probability is 1, (b) the probability is 0.
Chapter 6:
Explain how a nonstandard normal distribution differs from the standard normal distribution. Describe the process for finding probabilities for nonstandard normal distributions. Illustrate with examples
1) Directly and completely post the requested information.
2) Respond to another student's post explaining the reason for your agreement or disagreement or why you think the post is important.
3) Respond to two or more posts explaining the reason for your agreement or disagreement or why you think the post is important.
Student réponses 2
------------------------------------------------------------------------------------------------------------------------------------
Chapter 6: STUDENT REPONSE
Both are bell-shaped distributions. The mean of a standard distribution is zero, while the standard deviation is one. The mean and standard deviation of a nonstandard distribution might have different values. (SD numbers are, of course, positive) (Das & Geisler, 2021). Sketch the plot, feature the x-score of the plot, ascertain the fitting z-score, conceal the comparing area of interest, find in the table the region relating to the z-score (region among mean and z - result) and utilize this region to track down the area of interest which are concealed which ought to be important for the cycle.
The mean and variance are the two parameters that define a normal distribution. We regularly utilize the expression "arbitrary random distribution" in measurements to allude to information gathered from a normal distribution to gauge this perimeter. The standard normal distribution has a mean of 0 and a variance of 1. This is the dispersion used to make the normal distribution table.If X has a normal distribution with mean M and variance S2, we can easily define
Z=X - M/s.
The usual normal distribution is then applied to Z. So, using the tables for Z, we may compute probabilities of the type P [a< X < b] for any specified normal distribution. This is due to the fact that we may write X= sZ+m. In comparison to the overall family of normal distributions, the standard normal performs a unique function.
----------------------------------------------------------------------------------------------------------------------------------------------
Chapter 6:
A nonstandard normal distribution has two parameters: the mean and the variance. Now the standard normal distribution with a specific mean 0 and variance 1. This is the distribution that is used to construct probability tables of the normal distribution. If a random variable X follow nonstandard normal distribution with mean "m" and standard deviation "s" then, follow standard normal distribution.
MATH 107 UOM Linear Algebra Project Paper
Curve-fitting Project - Linear Model InstructionsFor this assignment, collect data exhibiting a relatively linear trend, f ...
MATH 107 UOM Linear Algebra Project Paper
Curve-fitting Project - Linear Model InstructionsFor this assignment, collect data exhibiting a relatively linear trend, find the line of best fit, plot the data and the line, interpret the slope, and use the linear equation to make a prediction. Also, find r2 (coefficient of determination) and r (correlation coefficient). Discuss your findings. Your topic may be that is related to sports, your work, a hobby, or something you find interesting. If you choose, you may use the suggestions described below. A Linear Model Example and Technology Tips are provided in separate documents.Tasks for Linear Regression Model (LR)(LR-1) Describe your topic, provide your data, and cite your source. Collect at least 8 data points. Label appropriately. (Highly recommended: Post this information in the Linear Model Project discussion as well as in your completed project. Include a brief informative description in the title of your posting. Each student must use different data.) The idea with the discussion posting is two-fold: (1) To share your interesting project idea with your classmates, and (2) To give me a chance to give you a brief thumbs-up or thumbs-down about your proposed topic and data. Sometimes students get off on the wrong foot or misunderstand the intent of the project, and your posting provides an opportunity for some feedback. Remark: Students may choose similar topics, but must have different data sets. For example, several students may be interested in a particular Olympic sport, and that is fine, but they must collect different data, perhaps from different events or different gender.(LR-2) Plot the points (x, y) to obtain a scatterplot. Use an appropriate scale on the horizontal and vertical axes and be sure to label carefully. Visually judge whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a different topic or data set.)(LR-3) Find the line of best fit (regression line) and graph it on the scatterplot. State the equation of the line.(LR-4) State the slope of the line of best fit. Carefully interpret the meaning of the slope in a sentence or two.(LR-5) Find and state the value of r2, the coefficient of determination, and r, the correlation coefficient. Discuss your findings in a few sentences. Is r positive or negative? Why? Is a line a good curve to fit to this data? Why or why not? Is the linear relationship very strong, moderately strong, weak, or nonexistent?(LR-6) Choose a value of interest and use the line of best fit to make an estimate or prediction. Show calculation work.(LR-7) Write a brief narrative of a paragraph or two. Summarize your findings and be sure to mention any aspect of the linear model project (topic, data, scatterplot, line, r, or estimate, etc.) that you found particularly important or interesting. You may submit all of your project in one document or a combination of documents, which may consist of word processing documents or spreadsheets or scanned handwritten work, provided it is clearly labeled where each task can be found. Be sure to include your name. Projects are graded on the basis of completeness, correctness, ease in locating all of the checklist items, and strength of the narrative portions.Topic: Choose a particular type of food. (Examples: Fish sandwich at fast-food chains, cheese pizza, breakfast cereal) For at least 8 brands, look up the fat content and the associated calorie total per serving. Make a quick plot for yourself to "eyeball" whether the data exhibit a relatively linear trend. (If so, proceed. If not, try a different type of food.) After you find the line of best fit, use your line to make a prediction corresponding to a fat amount not occurring in your data set.) Alternative: Look up carbohydrate content and associated calorie total per serving.I have also attached an sample.
American InterContinental University Training of Statisticians Presentation
This is a two part assignment.Discussion:Create a comprehensive explanation of a future analysis Teton Grand could run at ...
American InterContinental University Training of Statisticians Presentation
This is a two part assignment.Discussion:Create a comprehensive explanation of a future analysis Teton Grand could run at some point inthe future and how significance testing, confidence intervals and standard error of measurementwould help them understand the results. Your post should be in a client-ready format, usingprofessional and language appropriate for talking to an organization.The assignment should be posted as a video (must include narration) of you describing the futureanalysis for Teton Grand. Assignment:Teton Grand is interested in hiring a statistician intern for the summer to help them analyze data. Yourrole is to explain statistical concepts to the intern so that he/she has a good foundation for running theseanalyses moving forward.You must explain the purpose and use of significance testing, confidence intervals and standard error ofmeasurement using examples for how each concept could be used in relation to Teton Grand’s data.You are not expected to run any analyses this week but this exercise should highlight your ability to talkcredibly about the value of each statistical concept. You must go beyond simply defining the concepts totalking about their relevance to Teton Grand.This can be in a video format, presentation, or step-by-step handout. It must be client-ready,professionally formatted, and easy to understand. If creating a video, length should be approximately 5-10 minutes. If creating a presentation, the length should be approximately 10-15 slides. Your submissionshould adhere to the APA format and guidelines.Resources:Technical Information: This assignment may require a video submission. Click here(https://resources.adler.edu/knowledgebase/my-media-and-media-gallery/) for directions. (https://adler.instructure.com/courses/12203/modules/items/366613) (https://adler.instructure.com/courses/12203/modules/items/366615) (https://adler.instructure.com/courses/12203/modules/items/366616) (https://adler.instructure.com/courses/12203/modules/items/366617) (https://adler.instructure.com/courses/12203/modules/items/366618) (https://adler.instructure.com/courses/12203/modules/items/366619) (https://adler.instructure.com/courses/12203/modules/items/366620) (https://adler.instructure.com/courses/12203/modules/items/366623) (https://adler.instructure.com/courses/12203/modules/items/366622)1/7/2021 [4.1] Statistician Intern Assignmenthttps://adler.instructure.com/courses/12203/assign... 2/4Your presentation can be created using PowerPoint, Prezi or any other multimedia tool, but it must beviewable by anyone in the class. As your instructor and classmates will most likely be a mix of Windowsand Macs user, you must export your presentation as a video and upload it to your media. If you usePrezi, you can post a link to the presentation.PowerPoint (Windows Users): https://support.office.com/en-za/article/Turn-your-presentation-into-a-video-c140551f-cb37-4818-b5d4-3e30815c3e83 (https://support.office.com/en-za/article/Turn-your-presentation-into-a-video-c140551f-cb37-4818-b5d4-3e30815c3e83)Keynote (Mac Users): https://support.apple.com/en-us/HT202220(https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsupport.apple.com%2Fen-us%2FHT202220&data=02%7C01%7Cjbailey%40adler.edu%7C40cbb75f1d8343b3f54108d76868e085%7C9(https://support.apple.com/kb/PH16971?viewlocale=en_US&locale=en_US)Sharing with Prezi: https://prezi.com/support/article/sharing/sharing-...(https://prezi.com/support/article/sharing/sharing-...
statistics ques
10.1.2Table
#10.1.6 contains the value of the house and the amount of rental income
in a year that the house brings in ...
statistics ques
10.1.2Table
#10.1.6 contains the value of the house and the amount of rental income
in a year that the house brings in ("Capital and rental," 2013). Create
a scatter plot and find a regression equation between house value and
rental income. Then use the regression equation to find the rental
income a house worth $230,000 and for a house worth $400,000. Which
rental income that you calculated do you think is closer to the true
rental income? Why?Table #10.1.6: Data of House Value versus Rental Value Rental Value Rental Value Rental Value Rental 81000 6656 77000 4576 75000 7280 67500 6864 95000 7904 94000 8736 90000 6240 85000 7072 121000 12064 115000 7904 110000 7072 104000 7904 135000 8320 130000 9776 126000 6240 125000 7904 145000 8320 140000 9568 140000 9152 135000 7488 165000 13312 165000 8528 155000 7488 148000 8320 178000 11856 174000 10400 170000 9568 170000 12688 200000 12272 200000 10608 194000 11232 190000 8320 214000 8528 208000 10400 200000 10400 200000 8320 240000 10192 240000 12064 240000 11648 225000 12480 289000 11648 270000 12896 262000 10192 244500 11232 325000 12480 310000 12480 303000 12272 300000 12480 10.1.4The
World Bank collected data on the percentage of GDP that a country
spends on health expenditures ("Health expenditure," 2013) and also the
percentage of woman receiving prenatal care ("Pregnant woman receiving,"
2013). The data for the countries where this information are available
for the year 2011 is in table #10.1.8. Create a scatter plot of the data
and find a regression equation between percentage spent on health
expenditure and the percentage of woman receiving prenatal care. Then
use the regression equation to find the percent of woman receiving
prenatal care for a country that spends 5.0% of GDP on health
expenditure and for a country that spends 12.0% of GDP. Which prenatal
care percentage that you calculated do you think is closer to the true
percentage? Why? Table #10.1.8: Data of Heath Expenditure versus Prenatal Care HealthExpenditure(% of GDP) PrenatalCare (%) 9.6 47.9 3.7 54.6 5.2 93.7 5.2 84.7 10.0 100.0 4.7 42.5 4.8 96.4 6.0 77.1 5.4 58.3 4.8 95.4 4.1 78.0 6.0 93.3 9.5 93.3 6.8 93.7 6.1 89.8 For
each problem, state the random variables. Also, look to see if there
are any outliers that need to be removed. Do the correlation analysis
with and without the suspected outlier points to determine if their
removal affects the correlation. The data sets in this section are in
section 10.1.10.2.2Table
#10.1.6 (from problem 10.1.2) contains the value of the house and the
amount of rental income in a year that the house brings in ("Capital and
rental," 2013). Find the correlation coefficient and coefficient of
determination and then interpret both.10.2.4The
World Bank collected data on the percentage of GDP that a country
spends on health expenditures ("Health expenditure," 2013) and also the
percentage of woman receiving prenatal care ("Pregnant woman receiving,"
2013). The data for the countries where this information is available
for the year 2011 are in table #10.1.8 (from problem 10.1.4). Find the
correlation coefficient and coefficient of determination and then
interpret both.For each problem, state the random
variables. The data sets in this section are in the homework for section
10.1 and were also used in section 10.2. If you removed any data points
as outliers in the other sections, remove them in this sections
homework too.10.3.2Table
#10.1.6 (from problem 10.1.2) contains the value of the house and the
amount of rental income in a year that the house brings in ("Capital and
rental," 2013).a.) Test at the 5% level for a positive correlation between house value and rental amount.b.) Find the standard error of the estimate.c.) Compute a 95% prediction interval for the rental income on a house worth $230,000.10.3.4The
World Bank collected data on the percentage of GDP that a country
spends on health expenditures ("Health expenditure," 2013) and also the
percentage of woman receiving prenatal care ("Pregnant woman receiving,"
2013). The data for the countries where this information is available
for the year 2011 are in table #10.1.8 (from problem 10.1.4).a.)
Test at the 5% level for a correlation between percentage spent on
health expenditure and the percentage of woman receiving prenatal care.b.) Find the standard error of the estimate.c.)
Compute a 95% prediction interval for the percentage of woman receiving
prenatal care for a country that spends 5.0 % of GDP on health
expenditure.In each problem show all steps of the
hypothesis test. If some of the assumptions are not met, note that the
results of the test may not be correct and then continue the process of
the hypothesis test.11.1.2Researchers
watched groups of dolphins off the coast of Ireland in 1998 to
determine what activities the dolphins partake in at certain times of
the day ("Activities of dolphin," 2013). The numbers in table #11.1.6
represent the number of groups of dolphins that were partaking in an
activity at certain times of days. Is there enough evidence to show that
the activity and the time period are independent for dolphins? Test at
the 1% level. Table #11.1.6: Dolphin Activity Activity Period RowTotal Morning Noon Afternoon Evening Travel 6 6 14 13 39 Feed 28 4 0 56 88 Social 38 5 9 10 62 ColumnTotal 72 15 23 79 189 11.1.4A
person’s educational attainment and age group was collected by the U.S.
Census Bureau in 1984 to see if age group and educational attainment
are related. The counts in thousands are in table #11.1.8 ("Education by
age," 2013). Do the data show that educational attainment and age are
independent? Test at the 5% level.Table #11.1.8: Educational Attainment and Age Group Education Age Group RowTotal 25-34 35-44 45-54 55-64 >64 Did not completeHS 5416 5030 5777 7606 13746 37575 CompletedHS 16431 1855 9435 8795 7558 44074 College 1-3year 8555 5576 3124 2524 2503 22282 College 4 or more years 9771 7596 3904 3109 2483 26863 ColumnTotal 40173 20057 22240 22034 26290 130794 In
each problem show all steps of the hypothesis test. If some of the
assumptions are not met, note that the results of the test may not be
correct and then continue the process of the hypothesis test.11.2.4In
Africa in 2011, the number of deaths of a female from cardiovascular
disease for different age groups are in table #11.2.6 ("Global health
observatory," 2013). In addition, the proportion of deaths of females
from all causes for the same age groups are also in table #11.2.6. Do
the data show that the death from cardiovascular disease are in the same
proportion as all deaths for the different age groups? Test at the 5%
level.Table #11.2.6: Deaths of Females for Different Age Groups Age 5-14 15-29 30-49 50-69 Total Cardiovascular Frequency 8 16 56 433 513 All Cause Proportion 0.10 0.12 0.26 0.52 11.2.6A
project conducted by the Australian Federal Office of Road Safety asked
people many questions about their cars. One question was the reason
that a person chooses a given car, and that data is in table #11.2.8
("Car preferences," 2013).Table #11.2.8: Reason for Choosing a Car Safety Reliability Cost Performance Comfort Looks 84 62 46 34 47 27 Do
the data show that the frequencies observed substantiate the claim that
the reason for choosing a car are equally likely? Test at the 5% level.
Statistics Testing Differences Between Means Variances & Proportions Task
A town official claims that the average vehicle in their area sells for more than the 40th percentile of your data set. Us ...
Statistics Testing Differences Between Means Variances & Proportions Task
A town official claims that the average vehicle in their area sells for more than the 40th percentile of your data set. Using the data, you obtained in week 1, as well as the summary statistics you found for the original data set, run a hypothesis test to determine if the claim can be supported. Make sure you state all the important values, so your fellow classmates can use them to run a hypothesis test as well. Use the descriptive statistics you found during Week 2 NOT the new SD you found during Week 4. Because again, we are using the original 10 sample data set NOT a new smaller sample size. Use alpha = .05 to test your claim.
(Note: You will want to use the function =PERCENTILE.INC in Excel to find the 40th percentile of your data set. Hopefully this Excel function looks familiar to you from Week 2.)
First determine if you are using a z or t-test and explain why. Then conduct a four-step hypothesis test including a sentence at the end justifying the support or lack of support for the claim and why you made that choice.
UMGC Agency Leadership Worksheet
A federal government agency that is responsible for setting vehicle fuel economy standards for automobile manufacturers is ...
UMGC Agency Leadership Worksheet
A federal government agency that is responsible for setting vehicle fuel economy standards for automobile manufacturers is conducting research in order to update its fuel economy standards for the year 2030. Automobile manufacturers, and consumers, are highly interested in what the agency's findings and determinations will be as this will affect every vehicle in the United States. The federal government agency is very interested in the relationship between the weight of a vehicle and the vehicle's fuel economy (average miles per gallon (MPG)). Specifically, the agency is concerned that if the current trend of automobile manufacturers producing heavier new vehicles continues that its fuel economy targets will not be met. The agency's research department recently collected data for analysis in order to support the agency's upcoming discussion with the industry regarding its proposed 2030 fuel economy standards. The average MPG from a random sample of 750 vehicles was recently calculated by the agency. The research division also collected the vehicle weight of these 750 randomly sampled vehicles. The Vehicle Number, Type, Vehicle Weight, Average MPG, Fuel Tank Size (Gallons), Engine Size (Liters), and Meet or Not Meet Current Standards data were collected for these 750 vehicles1. Agency leadership wants to create a simple linear regression model to predict a vehicle's average fuel economy based on the weight of the vehicle.Create a scatterplot using Vehicle Weight as the independent variable and Average MPG as the dependent variable. Perform a visual analysis of the data: describe the trend, strength, and shape of the relationship between these two variables. Choose the correct answer below.A. The scatterplot indicates there is a positive, strong, linear relationship between Vehicle Weight and Average MPG.B. The scatterplot indicates there is a positive, weak, linear relationship between Vehicle Weight and Average MPG.C. The scatterplot indicates there is a negative, strong, linear relationship between Vehicle Weight and Average MPG D. The scatterplot indicates there is a negative, weak, linear relationship between Vehicle Weight and Average MPG.E.The scatterplot indicates there is no relationship between Vehicle Weight and Average MPG.1.1. Include the scatterplot image as part of your answer. Save the image as a .jpg or .png file, then click "Show Work" and use the "Insert Image" button to upload and save your scatterplot.1.2. Based upon your visual analysis of the scatterplot, is simple linear regression appropriate? Why or why not?2. What is the correlation coefficient between Vehicle Weight and Average MPG? (Round to two decimal places)2.1. Interpret the correlation coefficient in this context.3. Using your simple linear regression model, what is the value for the slope for this regression model? (Round to decimal places)3.1. Interpret the slope in this context.4. Using your simple linear regression model, what is the value for the y-intercept for this regression model? (Round to two decimal places)4.1. Interpret the y-intercept in this context.5. Using your simple linear regression model, for each one pound increase in vehicle weight, how much would one predict the vehicle's average fuel economy to decrease? (Round to three decimal places)5.1. Using your simple linear regression model, what is the predicted Average MPG of a vehicle when the Vehicle Weight is 2700 pounds (round the slope to 3 decimal places and y-intercept to 2 decimal places)?5.2. Based upon your simple linear regression, should the agency's leadership be concerned that if new vehicles continue to be produced with heavier weights that its goal of better fuel economy will be jeopardized?6. Another facet of the weight of the car is the question of whether or not weight differs for cars that meet standards and those that do not. Create a side-by-side boxplot of the weight of cars, distinguishing between cars that meet standards and those that do not.Include the side-by-side boxplot image as part of your answer. Save the image as a .jpg or .png file, then click "Show Work" and use the "Insert Image" button to upload and save your boxplot.6.1. Describe and interpret the shapes of the two boxplots.7. One typically expects that e.g. trucks have a different fuel efficiency than passenger cars. That could be tested by a two-sample t-test, but we do not have two types of cars, but more than two. Our two-sample test won't help to compare passenger cars, SUVs, and trucks. As such, conduct an ANOVA F-test to investigate if the average fuel efficiency of cars differs between the three types of cars.Now run the One Way ANOVA. What is the value of the test statistic of this ANOVA test?Test statistic = (Round to two decimal places)7.1. What are the degrees of freedom of the test statistic of the ANOVA test?Degrees of Freedom (Total)= (Type a whole number)7.2. What is the P-value of the test statistic of the ANOVA test?P-value= (Round to three decimal places)7.3. Describe in your own words what you conclude from the outcome of this ANOVA test. Is it in line with the expectations you had?8. One typically expects that e.g. trucks have a different fuel efficiency than passenger cars. That could be tested by a two-sample t-test, but we do not have two types of cars, but more than two. Our two-sample test won't help to compare passenger cars, SUVs, and trucks.If we wish to investigate if the average fuel efficiency of cars differs between the three types of cars, what type of analysis do we need?A.Two-sample t-testB.χ2 test for independenceC. ANOVA F-testD. χ2 test for homogeneity8.1. What is the null hypothesis of the test that there is no difference in fuel efficiency between the types of cars?A. All medians of fuel efficiency are equal for the three types of carsB. All means of fuel efficiency are equal for the three types of carsC. At least one means of fuel efficiency differs from the means of the other two types of carD. All three means of fuel efficiency are different for the three types of cars8.2. What is the alternative hypothesis of the test that there is no difference in fuel efficiency between the types of cars?A. All means of fuel efficiency are equal for the three types of carsB. All medians of fuel efficiency are equal for the three types of carsC. All three means of fuel efficiency are different for the three types of carsD. At least one means of fuel efficiency differs from the means of the other two types of cars8.3. Create side-by-side boxplots in StatCrunch. What type of car has the highest variability in fuel efficiency (judge by the graph, do not calculate)?A.TrucksB. Passenger carsC. SUVs8.4. And again looking at the side-by-side boxplot only, without yet doing any calculations: do you expect to find significant differences in mean fuel efficiency between the types of cars?A. No, because differences in medians are small.B. No, because differences in medians are small relative to the amount of variation within each type.C. Yes, because differences in medians are small relative to the amount of variation within each type.D. Yes, because differences in medians are small.
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