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STAT 250 Confidence Intervals
Instructions on how to complete this lab is in the zip file and you can also see what requirements for this lab is like be ...
STAT 250 Confidence Intervals
Instructions on how to complete this lab is in the zip file and you can also see what requirements for this lab is like below. Thank you.## Skills* To recognize and pull out key information from a research scenario * Given a study objective, determine whether significance testing is appropriate* Identify potential types of error given a significance test * Construct a confidence interval for a population mean * Given a confidence level ($1-\alpha$), determine the critical value (t*) needed to construct the confidence interval * Construct and interpret a one sample interval for the mean based on the t-distribution **Please make sure to show all R code and output after each question so that we can see your work.** Write a sentence for each numerical value produced describing its meaning **in context with the proper units**. Be sure to submit your full project as a .zip file that includes *at least* your edited .Rmd file to receive credit. Detailed instructions for procedural parts of labs are found in the "General Lab Directions" on your iLearn page; refer to it for how to download/upload/etc. If you don't recall how to do something, you should first refer to your lecture notes, in-class examples, and the primer from Lab #1 and #2. Your lecture notes and readings on these topics are also good resources.You may work in pairs from *within the same section*, but include both partner's names on the documents and write a short description of how each person contributed to the lab. Labs completed with pairs from different sections will not be graded. Each person is responsible for submitting the documents to the assignment (i.e. each person in the pair submits the same documents to iLearn).Here are some symbols that you might find useful: $H_0$ $H_1$ $\sigma$ $\mu$ $\mu_0$ $\mu_{word}$ $\neq$***```{r setup, include=FALSE}knitr::opts_chunk$set(echo = TRUE, comment = "", warning = FALSE, message = FALSE)require(rmarkdown, quietly=TRUE)```## Scenario 1: Revisiting our Advertising CampaignRecall: A non-profit that delivers clean water devices world-wide generally averages 11 thousand dollars in donations daily. In hopes of expanding, the non-profit recently ran a print campaign to raise awareness and drive traffic to their website, hopefully resulting in an increase in donations. The ads ran each Sunday. You work for the company and have been asked to see if the campaign has resulted in an increase in the true mean daily donation. You randomly sample 40 days from the six months following the campaign start and determine a mean daily donation of 11.4 thousand dollars with a sample standard deviation of 1.57 thousand dollars. 1.1. In the problem above, bold the parameter of interest and italicize the research question. You may need to use a combination of `*` and `_` if they overlap -- check the RMarkdown resources if you don't recall how to use either `*` or `_`. 1.2. Calculate the 95% confidence interval 'by hand' for the true mean daily donation levels after the campaign started. Modify the code chunk below to enter the appropriate values for the [placeholders] (remove the entire placeholder, including brackets, and replace with correct value/name). Add comments to the code, using `#`s to describe what each line of code is doing.```{r}xbar<-[placeholder] # describe heres<-[placeholder] # describe heren<-[placeholder] # describe herese<-[placeholder]/sqrt([placeholder]) # describe here(tcrit<-qt(1-[placeholder]/2, [placeholder]-1)) # describe here```Use the R objects created above to fill in the [placeholders] below to calculate the lower and upper bound of the confidence interval.```{r}(lower<-[placeholder]-[placeholder]*[placeholder]) # describe here(upper<-[placeholder]+[placeholder]*[placeholder]) # describe here```1.3. Write your final confidence interval below, using in-line code.> ([placeholder], [placeholder])1.4. Based on the confidence interval, do you think the true mean daily donation after the campaign could be the **same** as the value it was before the campaign stated, 11 thousand dollars? Why or why not? Possible words/ values to insert include: \$11.4k, 1.57, 40, $11k, 5%, 95%, true mean, sample mean, population, sample. Bold your inserted/selected words/phrases. > Our interval [DID/DID NOT] capture the null value (before-campaign mean of [placeholder]). Assuming our sample is one of the [placeholder] that would contain the [placeholder], then the mean daily donation after the ad campaign [WOULD BE THE SAME / WOULD NOT BE THE SAME] as the mean daily donation before the campaign started. Recall: You decide to take a second sample to revisit and revise your question. This time, instead of selecting *any* day in the six months of the campaign, you randomly select *only* from days that the print campaign ran (i.e., a random selection of Sundays from the six months). You randomly select 10 days on which the print advertisement appeared and found a sample mean daily donation of 11.94 dollars (in thousands) and a sample deviation of 1.57 thousand dollars. 1.5. Should you calculate a confidence interval for this sample with what you currently know for this scenario? Why or why not? > Insert answer here1.6. Assuming your sample is roughly symmetric, calculate the 95% confidence interval 'by hand' (using calculations in R) for this new sample. ```{r}[insert code needed to answer 1.6 here]```1.7. Interpret your confidence interval from 1.6. Edit the [placeholder]s to add in the specific values/context from this question, using in-line code where possible. The placeholders may stand in for a word, phrase, value, values, or mathematical notation. Bold your inserted answers. > Based on our [placeholder], we are [placeholder] confident that the [insert parameter of interest] is between [lower bound] and [upper bound] [units].1.8. Based on the **confidence level**, do you think that the non-profit has significantly increased their mean daily donation rate by running the ad campaign? Why or why not?> Insert answer here1.9. Does this conclusion agree with your hypothesis test from Lab #4? What about the tests for confidence intervals and hypothesis tests might differ that could give us different results?> Insert answer here***## Scenario 2: Run differentialBaseball is a game for statistics lovers. One particular statistic is the mean run differential, or the mean number of runs between the winning and losing teams' scores -- in other words, by how much a particular team wins or loses games, on average. You'd like to know if your team is, on average, winning games (having a run differential greater than zero). You recorded the run differential for your favorite team for a random sample of games through the season. We can read them into R as follows:```{r}run.diff<-c(3, 1, -1, -4, -2, 5, 2, 1, 1, -1, 3, -4, 4, 2, 1, 1, -1, 6, 10, 2, -1, -2, 3, 2, 1, -1, 3, 2, -1, -2, 1, -3, -1, 1, -4)```2.1. Can you perform a hypothesis test? > Insert answer here2.2. Calculate the appropriate descriptive statistics for `run.diff`. <Insert code chunk here>2.3. Conduct a hypothesis test, using the `t.test()` function. Modify the code below by replacing the [placeholder]s. ```{r}t.test([placeholder, mu=[placeholder], alternative="[placeholder]")```2.4. Calculate the confidence interval, using the `t.test()` function. Modify the code below by replacing the [placeholder]s```{r}t.test([placeholder], alternative="[placeholder]", conf.level = [placeholder)$conf.int```*Note: your confidence interval should be two actual values. If you are seeing something else, go back and review your notes.*2.5. What can you conclude from your hypothesis test and confidence interval about the true mean run differential for your favorite team?> Insert answer here***## Scenario 3: Range ShiftsAs the world warms, the geographic ranges of species might shift toward cooler areas. This could take the form of migration to higher latitudes or moving up in elevation from a species' native range. Chen et al. (2011) studied recent changes in the highest elevation at which species occur. Typically, higher elevations are cooler than lower elevations. The researchers want to know if species have shifted upwards towards cooler elevations, i.e. if their elevational range shift is greater than zero meters. Below are the changes in highest elevation for 31 taxa in a given location (e.g. one data point might be plants in Switzerland, etc.), in meters, over the late 1900s and early 2000s. (Many taxa were surveyed, including plants, vertebrates, and arthropods; the taxa included were selected in an unbiased way.) Positive numbers indicate upward shifts in elevation, and negative numbers indicate shifts to lower elevations. (Chen et al. 2011. Science)58.9, 7.8, 108.6, 44.8, 11.1, 19.2, 61.9, 30.5, 12.7, 35.8, 7.4, 39.3, 24.0, 62.1, 24.3, 55.3, 32.7, 65.3, -19.3, 7.6, -5.2, -2.1, 31.0, 69.0, 88.6, 39.5, 20.7, 89.0, 69.0, 64.9, 64.83.1. In the text above, bold the null value and italicize the research question. 3.2. Calculate the relevant summary statistics.< Insert code chunk here; annotate your code to describe what descriptive statistic each line calculates >Now we will want to conduct a hypothesis test. 3.3. Have the conditions been met to perform a hypothesis test? > Insert answer, and, if needed, code chunk here3.4. State the population of interest and the parameter of interest.> Insert answer here3.5. State the null and alternative hypotheses (verbal and symbolic).> Insert answer here.3.6. Execute your hypothesis test and then fill in the appropriate values below. <Inserted code chunk>> $\alpha = $ > $s/\sqrt{n} = $ > $t_{statistic} = $ > $p$-value $= $ > [Reject/Fail to Reject] $H_0$ 3.7. Interpret your decision (reject or fail to reject), in the context of the question. (i.e., what was your decision and how does that relate back to the central research question?)> Insert answer here3.8. Calculate a 95% confidence interval for the mean elevational shift, then write your interval below, using in-line code.<Insert code chunk>> Insert answer here3.9. Interpret your confidence interval. > Insert answer here 3.10. Why would we want to calculate a confidence interval? (What can a confidence interval tell us that a hypothesis test cannot?) Explain.> Insert answer here3.11. Based on the confidence interval and your one-sample t-test result, do you think the mean elevational shift could be above 0 m?> Insert answer here.3.12. The expected range shift, based on tracking temperature, for Tsaratanana Massif in Madagascar is 43.6 m. Would you expect that the taxa in Tsaratanana Massif have tracked the temperature shift? Explain your reasoning. > Insert answer here.3.13. The mean expected range shift, based on tracking temperature perfectly, is 126.97 m. Based on your confidence interval, do you think that taxa are tracking temperature well, or lagging behind? Explain your reasoning. > Insert answer here. 3.14. For each of the following, indicate if it is a correct or incorrect interpretation of the confidence interval. Provide an explanation for each.a. 95% of the sample mean elevational range shifts will be between the upper and lower bounds of the confidence interval. > Correct or Incorrect? > Explain.b. There is a 95% chance that the mean elevational range shift is between the upper and lower bounds of the confidence interval.> Correct or Incorrect? > Explain.c. 95% of the time, when we calculate a confidence interval in this way, the true mean elevational range shift will actually be between the upper and lower bounds of the confidence interval. 5% of the time, it will not. > Correct or incorrect? > Explain. 3.15. All else being equal, how do the following influence a confidence interval? (Choose one answer, and bold it.)a. Increased confidence level (1-\alpha): [Wider, Narrower, No Influence] b. Smaller sample size (n): [Wider, Narrower, No Influence] c. Larger standard deviation: [Wider, Narrower, No Influence]
ALU Correlation Between Resting and After Exercise Heartbeat Rates Questions
In this lesson, you will run a correlation between the resting and after exercising heart rates to determine if a linear r ...
ALU Correlation Between Resting and After Exercise Heartbeat Rates Questions
In this lesson, you will run a correlation between the resting and after exercising heart rates to determine if a linear relationship exists between the two variables and how strong the relationship is. Steps Open the Heart Rate Dataset in Excel and identify the X-variable (resting heart rates for all 200 participants) and the Y-variable (after exercise heart rates for all 200 participants). Use the Scatter Plot function in the Insert Charts section of Excel to create a scatter plot of the X and Y variables Add a trendline to the scatter plot Use the Data Analysis tools in Excel to find the regression equation. In a Word document, describe the relationship between the X-variable (resting heart rate)and the Y-variable (after exercise heart rate). Graph the scatter plot with a trend line. Does it appear there is a linear relationship? What does this mean? Calculate the correlation coefficient r using excel. Is this r (value) high compared to +1? Based on the correlation coefficient is the relationship strong? Use excel to find the regression equation. Identify the slope. What does it tell us in terms of our heart rate data?
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Easy Statistics Project Confidence Pricing Interval Exercise
The B&K Real Estate Company sells homes and is currently serving the Southeast region. It has recently expanded to cov ...
Easy Statistics Project Confidence Pricing Interval Exercise
The B&K Real Estate Company sells homes and is currently serving the Southeast region. It has recently expanded to cover the Northeast states. The B&K realtors are excited to now cover the entire East Coast and are working to prepare their southern agents to expand their reach to the Northeast.
B&K has hired your company to analyze the Northeast home listing prices in order to give information to their agents about the mean listing price at 95% confidence. Your company offers two analysis packages: one based on a sample size of 100 listings, and another based on a sample size of 1,000 listings. Because there is an additional cost for data collection, your company charges more for the package with 1,000 listings than for the package with 100 listings.
SAMPLE SIZE OF 100 LISTINGS:
95% confidence interval for the mean of the Northeast house listing price has a margin of error of $25,000
Cost for service to B&K: $2,000
SAMPLE SIZE OF 1,000 LISTINGS:
95% confidence interval for the mean of the Northeast house listing price has a margin of error of $5,000
Cost for service to B&K: $10,000
The B&K management team does not understand the tradeoff between confidence level, sample size, and margin of error. B&K would like you to come back with your recommendation of the sample size that would provide the sales agents with the best understanding of northeast home prices at the lowest cost for service to B&K.
In other words, which option is preferable?
Spending more on data collection and having a smaller margin of error
Spending less on data collection and having a larger margin of error
Choosing an option somewhere in the middle
For your initial post:
Formulate a recommendation and write a confidence statement in the context of this scenario. For the purposes of writing your confidence statement, assume the sample mean house listing price is $310,000 for both packages. “I am [#] % confident the true mean . . . [in context].”
Explain the factors that went into your recommendation, including a discussion of the margin of error
Grand Canyon University Patient Preference and Satisfaction Research
Search the GCU Library and find two new health care articles that use quantitative research. Do not use articles from a pr ...
Grand Canyon University Patient Preference and Satisfaction Research
Search the GCU Library and find two new health care articles that use quantitative research. Do not use articles from a previous assignment, or articles that appear in the Topic Materials or textbook.Complete an article analysis for each using the "Article Analysis: Part 2" template.Refer to the "Patient Preference and Satisfaction in Hospital-at-Home and Usual Hospital Care for COPD Exacerbations: Results of a Randomised Controlled Trial," in conjunction with the "Article Analysis Example 2," for an example of an article analysis.While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center.This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance.
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STAT 250 Confidence Intervals
Instructions on how to complete this lab is in the zip file and you can also see what requirements for this lab is like be ...
STAT 250 Confidence Intervals
Instructions on how to complete this lab is in the zip file and you can also see what requirements for this lab is like below. Thank you.## Skills* To recognize and pull out key information from a research scenario * Given a study objective, determine whether significance testing is appropriate* Identify potential types of error given a significance test * Construct a confidence interval for a population mean * Given a confidence level ($1-\alpha$), determine the critical value (t*) needed to construct the confidence interval * Construct and interpret a one sample interval for the mean based on the t-distribution **Please make sure to show all R code and output after each question so that we can see your work.** Write a sentence for each numerical value produced describing its meaning **in context with the proper units**. Be sure to submit your full project as a .zip file that includes *at least* your edited .Rmd file to receive credit. Detailed instructions for procedural parts of labs are found in the "General Lab Directions" on your iLearn page; refer to it for how to download/upload/etc. If you don't recall how to do something, you should first refer to your lecture notes, in-class examples, and the primer from Lab #1 and #2. Your lecture notes and readings on these topics are also good resources.You may work in pairs from *within the same section*, but include both partner's names on the documents and write a short description of how each person contributed to the lab. Labs completed with pairs from different sections will not be graded. Each person is responsible for submitting the documents to the assignment (i.e. each person in the pair submits the same documents to iLearn).Here are some symbols that you might find useful: $H_0$ $H_1$ $\sigma$ $\mu$ $\mu_0$ $\mu_{word}$ $\neq$***```{r setup, include=FALSE}knitr::opts_chunk$set(echo = TRUE, comment = "", warning = FALSE, message = FALSE)require(rmarkdown, quietly=TRUE)```## Scenario 1: Revisiting our Advertising CampaignRecall: A non-profit that delivers clean water devices world-wide generally averages 11 thousand dollars in donations daily. In hopes of expanding, the non-profit recently ran a print campaign to raise awareness and drive traffic to their website, hopefully resulting in an increase in donations. The ads ran each Sunday. You work for the company and have been asked to see if the campaign has resulted in an increase in the true mean daily donation. You randomly sample 40 days from the six months following the campaign start and determine a mean daily donation of 11.4 thousand dollars with a sample standard deviation of 1.57 thousand dollars. 1.1. In the problem above, bold the parameter of interest and italicize the research question. You may need to use a combination of `*` and `_` if they overlap -- check the RMarkdown resources if you don't recall how to use either `*` or `_`. 1.2. Calculate the 95% confidence interval 'by hand' for the true mean daily donation levels after the campaign started. Modify the code chunk below to enter the appropriate values for the [placeholders] (remove the entire placeholder, including brackets, and replace with correct value/name). Add comments to the code, using `#`s to describe what each line of code is doing.```{r}xbar<-[placeholder] # describe heres<-[placeholder] # describe heren<-[placeholder] # describe herese<-[placeholder]/sqrt([placeholder]) # describe here(tcrit<-qt(1-[placeholder]/2, [placeholder]-1)) # describe here```Use the R objects created above to fill in the [placeholders] below to calculate the lower and upper bound of the confidence interval.```{r}(lower<-[placeholder]-[placeholder]*[placeholder]) # describe here(upper<-[placeholder]+[placeholder]*[placeholder]) # describe here```1.3. Write your final confidence interval below, using in-line code.> ([placeholder], [placeholder])1.4. Based on the confidence interval, do you think the true mean daily donation after the campaign could be the **same** as the value it was before the campaign stated, 11 thousand dollars? Why or why not? Possible words/ values to insert include: \$11.4k, 1.57, 40, $11k, 5%, 95%, true mean, sample mean, population, sample. Bold your inserted/selected words/phrases. > Our interval [DID/DID NOT] capture the null value (before-campaign mean of [placeholder]). Assuming our sample is one of the [placeholder] that would contain the [placeholder], then the mean daily donation after the ad campaign [WOULD BE THE SAME / WOULD NOT BE THE SAME] as the mean daily donation before the campaign started. Recall: You decide to take a second sample to revisit and revise your question. This time, instead of selecting *any* day in the six months of the campaign, you randomly select *only* from days that the print campaign ran (i.e., a random selection of Sundays from the six months). You randomly select 10 days on which the print advertisement appeared and found a sample mean daily donation of 11.94 dollars (in thousands) and a sample deviation of 1.57 thousand dollars. 1.5. Should you calculate a confidence interval for this sample with what you currently know for this scenario? Why or why not? > Insert answer here1.6. Assuming your sample is roughly symmetric, calculate the 95% confidence interval 'by hand' (using calculations in R) for this new sample. ```{r}[insert code needed to answer 1.6 here]```1.7. Interpret your confidence interval from 1.6. Edit the [placeholder]s to add in the specific values/context from this question, using in-line code where possible. The placeholders may stand in for a word, phrase, value, values, or mathematical notation. Bold your inserted answers. > Based on our [placeholder], we are [placeholder] confident that the [insert parameter of interest] is between [lower bound] and [upper bound] [units].1.8. Based on the **confidence level**, do you think that the non-profit has significantly increased their mean daily donation rate by running the ad campaign? Why or why not?> Insert answer here1.9. Does this conclusion agree with your hypothesis test from Lab #4? What about the tests for confidence intervals and hypothesis tests might differ that could give us different results?> Insert answer here***## Scenario 2: Run differentialBaseball is a game for statistics lovers. One particular statistic is the mean run differential, or the mean number of runs between the winning and losing teams' scores -- in other words, by how much a particular team wins or loses games, on average. You'd like to know if your team is, on average, winning games (having a run differential greater than zero). You recorded the run differential for your favorite team for a random sample of games through the season. We can read them into R as follows:```{r}run.diff<-c(3, 1, -1, -4, -2, 5, 2, 1, 1, -1, 3, -4, 4, 2, 1, 1, -1, 6, 10, 2, -1, -2, 3, 2, 1, -1, 3, 2, -1, -2, 1, -3, -1, 1, -4)```2.1. Can you perform a hypothesis test? > Insert answer here2.2. Calculate the appropriate descriptive statistics for `run.diff`. <Insert code chunk here>2.3. Conduct a hypothesis test, using the `t.test()` function. Modify the code below by replacing the [placeholder]s. ```{r}t.test([placeholder, mu=[placeholder], alternative="[placeholder]")```2.4. Calculate the confidence interval, using the `t.test()` function. Modify the code below by replacing the [placeholder]s```{r}t.test([placeholder], alternative="[placeholder]", conf.level = [placeholder)$conf.int```*Note: your confidence interval should be two actual values. If you are seeing something else, go back and review your notes.*2.5. What can you conclude from your hypothesis test and confidence interval about the true mean run differential for your favorite team?> Insert answer here***## Scenario 3: Range ShiftsAs the world warms, the geographic ranges of species might shift toward cooler areas. This could take the form of migration to higher latitudes or moving up in elevation from a species' native range. Chen et al. (2011) studied recent changes in the highest elevation at which species occur. Typically, higher elevations are cooler than lower elevations. The researchers want to know if species have shifted upwards towards cooler elevations, i.e. if their elevational range shift is greater than zero meters. Below are the changes in highest elevation for 31 taxa in a given location (e.g. one data point might be plants in Switzerland, etc.), in meters, over the late 1900s and early 2000s. (Many taxa were surveyed, including plants, vertebrates, and arthropods; the taxa included were selected in an unbiased way.) Positive numbers indicate upward shifts in elevation, and negative numbers indicate shifts to lower elevations. (Chen et al. 2011. Science)58.9, 7.8, 108.6, 44.8, 11.1, 19.2, 61.9, 30.5, 12.7, 35.8, 7.4, 39.3, 24.0, 62.1, 24.3, 55.3, 32.7, 65.3, -19.3, 7.6, -5.2, -2.1, 31.0, 69.0, 88.6, 39.5, 20.7, 89.0, 69.0, 64.9, 64.83.1. In the text above, bold the null value and italicize the research question. 3.2. Calculate the relevant summary statistics.< Insert code chunk here; annotate your code to describe what descriptive statistic each line calculates >Now we will want to conduct a hypothesis test. 3.3. Have the conditions been met to perform a hypothesis test? > Insert answer, and, if needed, code chunk here3.4. State the population of interest and the parameter of interest.> Insert answer here3.5. State the null and alternative hypotheses (verbal and symbolic).> Insert answer here.3.6. Execute your hypothesis test and then fill in the appropriate values below. <Inserted code chunk>> $\alpha = $ > $s/\sqrt{n} = $ > $t_{statistic} = $ > $p$-value $= $ > [Reject/Fail to Reject] $H_0$ 3.7. Interpret your decision (reject or fail to reject), in the context of the question. (i.e., what was your decision and how does that relate back to the central research question?)> Insert answer here3.8. Calculate a 95% confidence interval for the mean elevational shift, then write your interval below, using in-line code.<Insert code chunk>> Insert answer here3.9. Interpret your confidence interval. > Insert answer here 3.10. Why would we want to calculate a confidence interval? (What can a confidence interval tell us that a hypothesis test cannot?) Explain.> Insert answer here3.11. Based on the confidence interval and your one-sample t-test result, do you think the mean elevational shift could be above 0 m?> Insert answer here.3.12. The expected range shift, based on tracking temperature, for Tsaratanana Massif in Madagascar is 43.6 m. Would you expect that the taxa in Tsaratanana Massif have tracked the temperature shift? Explain your reasoning. > Insert answer here.3.13. The mean expected range shift, based on tracking temperature perfectly, is 126.97 m. Based on your confidence interval, do you think that taxa are tracking temperature well, or lagging behind? Explain your reasoning. > Insert answer here. 3.14. For each of the following, indicate if it is a correct or incorrect interpretation of the confidence interval. Provide an explanation for each.a. 95% of the sample mean elevational range shifts will be between the upper and lower bounds of the confidence interval. > Correct or Incorrect? > Explain.b. There is a 95% chance that the mean elevational range shift is between the upper and lower bounds of the confidence interval.> Correct or Incorrect? > Explain.c. 95% of the time, when we calculate a confidence interval in this way, the true mean elevational range shift will actually be between the upper and lower bounds of the confidence interval. 5% of the time, it will not. > Correct or incorrect? > Explain. 3.15. All else being equal, how do the following influence a confidence interval? (Choose one answer, and bold it.)a. Increased confidence level (1-\alpha): [Wider, Narrower, No Influence] b. Smaller sample size (n): [Wider, Narrower, No Influence] c. Larger standard deviation: [Wider, Narrower, No Influence]
ALU Correlation Between Resting and After Exercise Heartbeat Rates Questions
In this lesson, you will run a correlation between the resting and after exercising heart rates to determine if a linear r ...
ALU Correlation Between Resting and After Exercise Heartbeat Rates Questions
In this lesson, you will run a correlation between the resting and after exercising heart rates to determine if a linear relationship exists between the two variables and how strong the relationship is. Steps Open the Heart Rate Dataset in Excel and identify the X-variable (resting heart rates for all 200 participants) and the Y-variable (after exercise heart rates for all 200 participants). Use the Scatter Plot function in the Insert Charts section of Excel to create a scatter plot of the X and Y variables Add a trendline to the scatter plot Use the Data Analysis tools in Excel to find the regression equation. In a Word document, describe the relationship between the X-variable (resting heart rate)and the Y-variable (after exercise heart rate). Graph the scatter plot with a trend line. Does it appear there is a linear relationship? What does this mean? Calculate the correlation coefficient r using excel. Is this r (value) high compared to +1? Based on the correlation coefficient is the relationship strong? Use excel to find the regression equation. Identify the slope. What does it tell us in terms of our heart rate data?
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Open the Project Instructions and follow them to complete The following slides will then ask you to complete various • M ...
Consumer Price Index
Open the Project Instructions and follow them to complete The following slides will then ask you to complete various • Most slides will require you ...
Easy Statistics Project Confidence Pricing Interval Exercise
The B&K Real Estate Company sells homes and is currently serving the Southeast region. It has recently expanded to cov ...
Easy Statistics Project Confidence Pricing Interval Exercise
The B&K Real Estate Company sells homes and is currently serving the Southeast region. It has recently expanded to cover the Northeast states. The B&K realtors are excited to now cover the entire East Coast and are working to prepare their southern agents to expand their reach to the Northeast.
B&K has hired your company to analyze the Northeast home listing prices in order to give information to their agents about the mean listing price at 95% confidence. Your company offers two analysis packages: one based on a sample size of 100 listings, and another based on a sample size of 1,000 listings. Because there is an additional cost for data collection, your company charges more for the package with 1,000 listings than for the package with 100 listings.
SAMPLE SIZE OF 100 LISTINGS:
95% confidence interval for the mean of the Northeast house listing price has a margin of error of $25,000
Cost for service to B&K: $2,000
SAMPLE SIZE OF 1,000 LISTINGS:
95% confidence interval for the mean of the Northeast house listing price has a margin of error of $5,000
Cost for service to B&K: $10,000
The B&K management team does not understand the tradeoff between confidence level, sample size, and margin of error. B&K would like you to come back with your recommendation of the sample size that would provide the sales agents with the best understanding of northeast home prices at the lowest cost for service to B&K.
In other words, which option is preferable?
Spending more on data collection and having a smaller margin of error
Spending less on data collection and having a larger margin of error
Choosing an option somewhere in the middle
For your initial post:
Formulate a recommendation and write a confidence statement in the context of this scenario. For the purposes of writing your confidence statement, assume the sample mean house listing price is $310,000 for both packages. “I am [#] % confident the true mean . . . [in context].”
Explain the factors that went into your recommendation, including a discussion of the margin of error
Grand Canyon University Patient Preference and Satisfaction Research
Search the GCU Library and find two new health care articles that use quantitative research. Do not use articles from a pr ...
Grand Canyon University Patient Preference and Satisfaction Research
Search the GCU Library and find two new health care articles that use quantitative research. Do not use articles from a previous assignment, or articles that appear in the Topic Materials or textbook.Complete an article analysis for each using the "Article Analysis: Part 2" template.Refer to the "Patient Preference and Satisfaction in Hospital-at-Home and Usual Hospital Care for COPD Exacerbations: Results of a Randomised Controlled Trial," in conjunction with the "Article Analysis Example 2," for an example of an article analysis.While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center.This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance.
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