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5. The authors of the article "Accommodating Persons with AIDS: Acceptance and Rejection in Rental
Situations" (Journal of Applied Social Psychology [1999]: 261-270) stated that, even though landlords
participating in a telephone survey indicated that they would generally be willing to rent to persons with
AIDS, they wondered whether this was true in practice. To investigate the researches independently
selected two random samples of 80 advertisements for rooms for rent from newspapers advertisements
in three large cities. An adult male caller responded to each ad in the first sample of 80 and inquired
about the availability of the room and was told that the room was still available in 61 of these calls. The
same caller also responded to each ad in the second sample. In these calls, the caller indicated that he
was currently receiving some treatment for AIDS and was about to be released from the hospital and
would require a place to live. The caller was told that a room was available in 32 of these calls. Based on
this information, the authors concluded that "reference to AIDS substantially decreased the likelihood of
a room being described as available". Do the data support this conclusion? (Use a = 0.01)
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MATH 160 Cuyamaca College Statistics Treating Depression Lab Part 1 & Excel Sheet
Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this ac ...
MATH 160 Cuyamaca College Statistics Treating Depression Lab Part 1 & Excel Sheet
Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this activity on a computer.
Use the rubric at the bottom of this page as a guide for completing this assignment.
A list of StatCrunch directions is provided after the Prompt section below.
Directions
Submit your work:
Carefully read all sections below (beginning with the Context section and ending with the Prompt section).
Commit a good-faith effort to address all items in the Prompt section below. Please be sure to number your responses.
If directed to do so, embed all required StatCrunch output in your initial submission. Please do not submit StatCrunch output as an attachment.
Complete your assigned peer reviews:
After you submit your initial good-faith attempt, continue to the ANSWER(S) page and review your instructor's response. But please do not submit your corrected work yet.
Within three days after the due date, return to this assignment and complete your assigned peer reviews (directions (Links to an external site.)).
Submit your corrected work:
We all learn from mistakes (our own and our classmates' mistakes). So please do not immediately correct your own mistakes. If possible, wait until you receive feedback from at least one of your peers.
If necessary, correct your work and resubmit the entire assignment - including any required StatCrunch output. Your instructor will only review and grade your most recent submission, so please do not refer to a previous submission.
Context
Clinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who have an episode of major depression will have a recurrence within 2-3 years. Being able to prevent the recurrence of depression in people who are at risk for the disease would go a long way to alleviate the pain and suffering of patients.
During the 1980's the federal government, through the National Institutes of Health (NIH), sponsored a large clinical trial to evaluate two drugs for depression. There were 3 treatment groups. Patients received either Imipramine (Imip), Lithium (Li), or a Placebo (Pl). Researchers randomly assigned patients to one of the 3 treatment groups and followed them for 2-4 years to track any recurrences of depression.
(Prien et al., Archives of General Psychiatry, 1984).
Variables
Hospt: Which hospital the patient was from: Labeled 1, 2, 3, 5 or 6
Treat: 0=Lithium; 1=Imipramine; 2=Placebo
Outcome: 0=Success 1=Failure (recurrence of depression)
Time: Number of weeks until a recurrence (if outcome=1) or until study ended (if outcome=0)
AcuteT: How long the patient was depressed before the start of the current study, measured in days
Age: Age in years
Gender: 1=Female 2=Male
Data
If you have not done so already, download the depression datafile (as always, if a login box opens, just close it and then download the file). Then upload the file in StatCrunch.
Prompt
We will analyze the data to answer the second research question: Which of the drugs (if either) delayed the recurrence of depression longer relative to the placebo?
In the previous lab-preparation activity, we identified Treat as the explanatory variable and Time as the response variable. We also determined that we will analyze the data using side-by-side boxplots and descriptive statistics (i.e. 5-number summaries since the graphs are boxplots).
Make graphs and tables.
Use StatCrunch to produce side-by-side boxplots. (directions)
Embed your graphs with your initial post.
Use StatCrunch to produce the descriptive statistics (a single table containing the 5-number summaries for each comparison group). (directions)
Copy and paste the StatCrunch output table into your initial post.
Analyze the data: Compare the distributions for the treatment groups as demonstrated in Unit 2. For example, compare medians and intervals of typical values. Describe the shape and any outliers. Be sure to write your comparisons so the reader can understand the context of the numbers. For example, don't just say the median is 30; instead, say something like this: on average patients taking the placebo relapsed in 30 days (Q2=30 days).
Draw a conclusion: What can we conclude from your analysis? Did one drug successfully delay a relapse of depression better than the others? What evidence supports your conclusion?
Summarize your conclusions in response to both research questions: In this lab you compared three treatments (two drugs and the placebo) using two different variables. In Part 1 you compared whether or not a relapse into depression occurred for each of the two drugs and the placebo. In Part 2 you compared the length of time until the next relapse for the two drugs and the placebo. What can you conclude in light of both analyses? Is one treatment better than the other? How does the data support your conclusion?
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory � ...
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory “scenario” of just two to three sentences that describes the data file for your project and why you (the ?????? Corporation/Group) are building a regression model to predict based on the set of possible independent variables
As you learned in class in Week 2, first develop a simple linear regression model using one of the above predictors of .
Cut and paste into your report the scatter plot and the Minitab Express printout for this simple linear regression model.
Write the sample regression equation.
Interpret the meaning of the intercept and slope for your fitted model.
Interpret the meaning of the coefficient of determination .
Interpret the meaning of the standard error of the estimate .
Obtain the residual plots and cut and paste them into the report. Briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 2G.
If the linearity or normality assumptions are problematic state this but continue to Step 2G with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption appears to be seriously violated contact me.
Comment on the statistical significance of your fitted model. (Note: Every team should have a fitted model that is statistically significant so contact me immediately if this is not so).
Select a value for your independent variable in its relevant range:
Predict .
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variable has the particular value you selected.
Determine the 95% prediction interval estimate of for an individual occasion when the independent variable has the particular value you selected.
As you learned in class in Weeks 3 and 4, you will be using the set of potentially meaningful numerical independent variables and one selected “two-category” dummy variable in your study to develop a “best” multiple regression model for predicting your numerical dependent variable . Follow the “9-step modeling process” described in the Powerpoints at the end of Module 4.
Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing their respective scatter plots and paste these into your report.
Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable.
Then review Slides 6 through 16 of the Module 4 Powerpoints and assess collinearity until you are satisfied that you have a final set of possible predictors that are “independent,” i.e., not unduly correlated with each other.
Use both stepwise regression approaches and best subsets regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable).
Based on the stepwise modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the forward selection modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the backward elimination modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the adjusted criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Minitab’s “predicted” criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the smallest criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Mallows’ criterion determine which numerical independent variable or variables should be included in your regression model.
Comment on the consistency of your findings in Step 3D (1)-(7).
Cut and paste the Minitab Express printouts from Step 3D into your report.
Based on Step 3D (along with the principle of parsimony if necessary) select a “best” multiple regression model.
Using the predictor variables from your selected “best” multiple regression model, rerun the multiple regression model in order to assess its assumptions.
Look at the set of residual plots, cut and pasted them into the report, and briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 3J.
If the linearity or normality assumptions are problematic state this but continue to Step 3J with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption is violated either transform the dependent variable to log or transform particular independent variables (discuss this with me) and rerun the multiple regression model as in Step 3H.
Assess the significance of the overall fitted model.
Assess the contribution of each predictor variable.
Write the sample multiple regression equation for the “final best” model you have developed.
Interpret the meaning of the intercept and interpret the meaning of all the slopes for your fitted model (but do this in whatever units you used for Y to build the model).
Interpret the meaning of the coefficient of multiple determination .
Very briefly comment on how much has changed from the simple regression model in Step 2D to the “final” multiple regression model in Step 4B.
Interpret the meaning of the standard error of the estimate (in the units you used to build the model).
Select one value for each of your independent variables in their respective relevant ranges:
Predict . (If you used log Y take the antilog so you are back in units of Y).
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variables have the particular values you selected.
(If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
Determine the 95% prediction interval estimate of for an individual occasion when the independent variables have the particular values you selected. (If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
For your “final best” model, as per Module 1, prepare a brief descriptive analysis highlighting the key measures of central tendency, variation, and shape for your dependent variable Y and for each of the predictor variables. Show the individual histograms and boxplots for these variables. If a dummy variable was included as a predictor in your “final best” model show its summary table and bar chart.
Specific instructions for the written team project report follows.Writing the Team ReportEach team report has a title page followed by an Introduction section describing the study “scenario” and mentioning the possible predictor variables and the dependent variable. A section on the Simple Linear Regression Model is then followed by a section on the “final best” Multiple Regression Model. The final section of the report is a Discussion section assessing the gains (if any) by using the “best” multiple regression model in lieu of the simple linear regression model. All discussed Minitab Express printouts should be “cut and pasted” into the report. These should be placed either in the body of the report or in an Appendix to the report. If the latter approach is taken, be sure to number and reference these printouts when discussing them in the body of the report. ** you should do it withe Minitab Expiress(((The INFO 561 course will be using Minitab Express, the educational version of a professional statistical software package that you can rent for $30 (for six months) at the website www.OntheHub.com Minitab Express works on all PCs and also on a Mac.Come to the first class session with Minitab Express loaded on your PC or Mac))my project is about BOUND FUNDS STUDYthe file is with attachment thanks.
MTH 156 Hypothesis testing: Null and Alternative Hypothesis Discussion
Share with your peers the null and alternative hypotheses for a decision that is relevant to your personal or professional ...
MTH 156 Hypothesis testing: Null and Alternative Hypothesis Discussion
Share with your peers the null and alternative hypotheses for a decision that is relevant to your personal or professional life. Remember in hypothesis testing the "equals" part will be with the null hypothesis, so you can have less than or equal to, greater than or equal to, or just equal to when defining the null hypothesis. The alternative hypothesis will be, then, either greater than, less than, or not equal to in relation to the above null criteria. See below for how it looks symbolically for the three possible setups.I. H0: μ ≥ μ0 Ha: μ < μ0II. H0: μ ≤ μ0 Ha: μ > μ0III. H0: μ = μ0 Ha: μ ≠ μ0Note that a hypothesis test needs to be set up to be testable, so be sure to have it presented in a manner where you are testing the μ0 value. Additionally, identify the Type I and Type II errors that could occur with your decision‐making process.
2 pages
Psy 260 Week 6 Quiz
1. True or false: Statistically, we cannot conclude that the proportions of people who do and do not feel safe walking in ...
Psy 260 Week 6 Quiz
1. True or false: Statistically, we cannot conclude that the proportions of people who do and do not feel safe walking in their neighborhood at night ...
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MATH 160 Cuyamaca College Statistics Treating Depression Lab Part 1 & Excel Sheet
Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this ac ...
MATH 160 Cuyamaca College Statistics Treating Depression Lab Part 1 & Excel Sheet
Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this activity on a computer.
Use the rubric at the bottom of this page as a guide for completing this assignment.
A list of StatCrunch directions is provided after the Prompt section below.
Directions
Submit your work:
Carefully read all sections below (beginning with the Context section and ending with the Prompt section).
Commit a good-faith effort to address all items in the Prompt section below. Please be sure to number your responses.
If directed to do so, embed all required StatCrunch output in your initial submission. Please do not submit StatCrunch output as an attachment.
Complete your assigned peer reviews:
After you submit your initial good-faith attempt, continue to the ANSWER(S) page and review your instructor's response. But please do not submit your corrected work yet.
Within three days after the due date, return to this assignment and complete your assigned peer reviews (directions (Links to an external site.)).
Submit your corrected work:
We all learn from mistakes (our own and our classmates' mistakes). So please do not immediately correct your own mistakes. If possible, wait until you receive feedback from at least one of your peers.
If necessary, correct your work and resubmit the entire assignment - including any required StatCrunch output. Your instructor will only review and grade your most recent submission, so please do not refer to a previous submission.
Context
Clinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who have an episode of major depression will have a recurrence within 2-3 years. Being able to prevent the recurrence of depression in people who are at risk for the disease would go a long way to alleviate the pain and suffering of patients.
During the 1980's the federal government, through the National Institutes of Health (NIH), sponsored a large clinical trial to evaluate two drugs for depression. There were 3 treatment groups. Patients received either Imipramine (Imip), Lithium (Li), or a Placebo (Pl). Researchers randomly assigned patients to one of the 3 treatment groups and followed them for 2-4 years to track any recurrences of depression.
(Prien et al., Archives of General Psychiatry, 1984).
Variables
Hospt: Which hospital the patient was from: Labeled 1, 2, 3, 5 or 6
Treat: 0=Lithium; 1=Imipramine; 2=Placebo
Outcome: 0=Success 1=Failure (recurrence of depression)
Time: Number of weeks until a recurrence (if outcome=1) or until study ended (if outcome=0)
AcuteT: How long the patient was depressed before the start of the current study, measured in days
Age: Age in years
Gender: 1=Female 2=Male
Data
If you have not done so already, download the depression datafile (as always, if a login box opens, just close it and then download the file). Then upload the file in StatCrunch.
Prompt
We will analyze the data to answer the second research question: Which of the drugs (if either) delayed the recurrence of depression longer relative to the placebo?
In the previous lab-preparation activity, we identified Treat as the explanatory variable and Time as the response variable. We also determined that we will analyze the data using side-by-side boxplots and descriptive statistics (i.e. 5-number summaries since the graphs are boxplots).
Make graphs and tables.
Use StatCrunch to produce side-by-side boxplots. (directions)
Embed your graphs with your initial post.
Use StatCrunch to produce the descriptive statistics (a single table containing the 5-number summaries for each comparison group). (directions)
Copy and paste the StatCrunch output table into your initial post.
Analyze the data: Compare the distributions for the treatment groups as demonstrated in Unit 2. For example, compare medians and intervals of typical values. Describe the shape and any outliers. Be sure to write your comparisons so the reader can understand the context of the numbers. For example, don't just say the median is 30; instead, say something like this: on average patients taking the placebo relapsed in 30 days (Q2=30 days).
Draw a conclusion: What can we conclude from your analysis? Did one drug successfully delay a relapse of depression better than the others? What evidence supports your conclusion?
Summarize your conclusions in response to both research questions: In this lab you compared three treatments (two drugs and the placebo) using two different variables. In Part 1 you compared whether or not a relapse into depression occurred for each of the two drugs and the placebo. In Part 2 you compared the length of time until the next relapse for the two drugs and the placebo. What can you conclude in light of both analyses? Is one treatment better than the other? How does the data support your conclusion?
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory � ...
INFO 561 Team Projects on Regression Model Building, statistics homework help
To develop your project report (to be submitted for grade in hard copy) follow the steps below.
Create an introductory “scenario” of just two to three sentences that describes the data file for your project and why you (the ?????? Corporation/Group) are building a regression model to predict based on the set of possible independent variables
As you learned in class in Week 2, first develop a simple linear regression model using one of the above predictors of .
Cut and paste into your report the scatter plot and the Minitab Express printout for this simple linear regression model.
Write the sample regression equation.
Interpret the meaning of the intercept and slope for your fitted model.
Interpret the meaning of the coefficient of determination .
Interpret the meaning of the standard error of the estimate .
Obtain the residual plots and cut and paste them into the report. Briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 2G.
If the linearity or normality assumptions are problematic state this but continue to Step 2G with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption appears to be seriously violated contact me.
Comment on the statistical significance of your fitted model. (Note: Every team should have a fitted model that is statistically significant so contact me immediately if this is not so).
Select a value for your independent variable in its relevant range:
Predict .
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variable has the particular value you selected.
Determine the 95% prediction interval estimate of for an individual occasion when the independent variable has the particular value you selected.
As you learned in class in Weeks 3 and 4, you will be using the set of potentially meaningful numerical independent variables and one selected “two-category” dummy variable in your study to develop a “best” multiple regression model for predicting your numerical dependent variable . Follow the “9-step modeling process” described in the Powerpoints at the end of Module 4.
Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing their respective scatter plots and paste these into your report.
Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable.
Then review Slides 6 through 16 of the Module 4 Powerpoints and assess collinearity until you are satisfied that you have a final set of possible predictors that are “independent,” i.e., not unduly correlated with each other.
Use both stepwise regression approaches and best subsets regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable).
Based on the stepwise modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the forward selection modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the backward elimination modeling criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the adjusted criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Minitab’s “predicted” criterion determine which numerical independent variable or variables should be included in your regression model.
Based on the smallest criterion determine which numerical independent variable or variables should be included in your regression model.
Based on Mallows’ criterion determine which numerical independent variable or variables should be included in your regression model.
Comment on the consistency of your findings in Step 3D (1)-(7).
Cut and paste the Minitab Express printouts from Step 3D into your report.
Based on Step 3D (along with the principle of parsimony if necessary) select a “best” multiple regression model.
Using the predictor variables from your selected “best” multiple regression model, rerun the multiple regression model in order to assess its assumptions.
Look at the set of residual plots, cut and pasted them into the report, and briefly comment on the appropriateness of your fitted model.
If the assumptions are met and the fitted model is appropriate continue to Step 3J.
If the linearity or normality assumptions are problematic state this but continue to Step 3J with caution. You do not need to check the assumption of independence in your project – that assumption is met.
If the equality of variance assumption is violated either transform the dependent variable to log or transform particular independent variables (discuss this with me) and rerun the multiple regression model as in Step 3H.
Assess the significance of the overall fitted model.
Assess the contribution of each predictor variable.
Write the sample multiple regression equation for the “final best” model you have developed.
Interpret the meaning of the intercept and interpret the meaning of all the slopes for your fitted model (but do this in whatever units you used for Y to build the model).
Interpret the meaning of the coefficient of multiple determination .
Very briefly comment on how much has changed from the simple regression model in Step 2D to the “final” multiple regression model in Step 4B.
Interpret the meaning of the standard error of the estimate (in the units you used to build the model).
Select one value for each of your independent variables in their respective relevant ranges:
Predict . (If you used log Y take the antilog so you are back in units of Y).
Determine the 95% confidence interval estimate of the average value of for all occasions when the independent variables have the particular values you selected.
(If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
Determine the 95% prediction interval estimate of for an individual occasion when the independent variables have the particular values you selected. (If your lower and upper boundaries are in units of log convert back to by taking the antilogs).
For your “final best” model, as per Module 1, prepare a brief descriptive analysis highlighting the key measures of central tendency, variation, and shape for your dependent variable Y and for each of the predictor variables. Show the individual histograms and boxplots for these variables. If a dummy variable was included as a predictor in your “final best” model show its summary table and bar chart.
Specific instructions for the written team project report follows.Writing the Team ReportEach team report has a title page followed by an Introduction section describing the study “scenario” and mentioning the possible predictor variables and the dependent variable. A section on the Simple Linear Regression Model is then followed by a section on the “final best” Multiple Regression Model. The final section of the report is a Discussion section assessing the gains (if any) by using the “best” multiple regression model in lieu of the simple linear regression model. All discussed Minitab Express printouts should be “cut and pasted” into the report. These should be placed either in the body of the report or in an Appendix to the report. If the latter approach is taken, be sure to number and reference these printouts when discussing them in the body of the report. ** you should do it withe Minitab Expiress(((The INFO 561 course will be using Minitab Express, the educational version of a professional statistical software package that you can rent for $30 (for six months) at the website www.OntheHub.com Minitab Express works on all PCs and also on a Mac.Come to the first class session with Minitab Express loaded on your PC or Mac))my project is about BOUND FUNDS STUDYthe file is with attachment thanks.
MTH 156 Hypothesis testing: Null and Alternative Hypothesis Discussion
Share with your peers the null and alternative hypotheses for a decision that is relevant to your personal or professional ...
MTH 156 Hypothesis testing: Null and Alternative Hypothesis Discussion
Share with your peers the null and alternative hypotheses for a decision that is relevant to your personal or professional life. Remember in hypothesis testing the "equals" part will be with the null hypothesis, so you can have less than or equal to, greater than or equal to, or just equal to when defining the null hypothesis. The alternative hypothesis will be, then, either greater than, less than, or not equal to in relation to the above null criteria. See below for how it looks symbolically for the three possible setups.I. H0: μ ≥ μ0 Ha: μ < μ0II. H0: μ ≤ μ0 Ha: μ > μ0III. H0: μ = μ0 Ha: μ ≠ μ0Note that a hypothesis test needs to be set up to be testable, so be sure to have it presented in a manner where you are testing the μ0 value. Additionally, identify the Type I and Type II errors that could occur with your decision‐making process.
2 pages
Psy 260 Week 6 Quiz
1. True or false: Statistically, we cannot conclude that the proportions of people who do and do not feel safe walking in ...
Psy 260 Week 6 Quiz
1. True or false: Statistically, we cannot conclude that the proportions of people who do and do not feel safe walking in their neighborhood at night ...
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