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Multivariate Analysis of Variance, manova spss help
PLEASE FOLLOW THE INSTRUCTIONS LISTED AND USE THE SAME DATASET AND TEMPLATE FROM THE LAST ASSIGNMENT AND INCLUDE THE SAV F ...
Multivariate Analysis of Variance, manova spss help
PLEASE FOLLOW THE INSTRUCTIONS LISTED AND USE THE SAME DATASET AND TEMPLATE FROM THE LAST ASSIGNMENT AND INCLUDE THE SAV FILE:Multivariate Analysis
of Variance (MANOVA)This week, your
exploration of analysis of variance continues. MANOVA is yet another extension
of comparison of means tests. This week, you will be exposed to foundational,
one-way MANOVA models.MANOVA is similar to a
one-way ANOVA but with multiple outcome/dependent variables. The statistics
professor who is interested in different teaching methods could have three
different course types, each with a unique approach. As an outcome variable,
the professor may wish to examine mean differences in grade point average
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could be performed in a single operation in statistical software.This week you explore
MANOVA, to see how it can serve as a basic building block to extend your
statistical testing to other applications. For example, just like adding a
covariate built on the one-way ANOVA to create an ANCOVA, adding a covariate
will create a MANCOVA model. You can also add additional factors, which will
give you a factorial MANOVA. MANOVA in SPSS Earlier this week, you practiced using MANOVA
models with SPSS and, ideally, used the Collaboration Lab to ask, answer, and
otherwise address any questions you had. In this Assignment, you apply what you
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Review the dataset provided.
Construct a research question based on one of those
datasets.
Pay attention to the assumptions of this test, and ask,
“Does it make sense to interpret the mean of this dependent variable?”
Use SPSS to answer the
research question you constructed. Then, compose a 1- to 2-paragraph analysis
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For this assignment you will implement a project involving linear curve-fitting and interpretation. You will assess the ap ...
Linear Project, math homework help
For this assignment you will implement a project involving linear curve-fitting and interpretation. You will assess the appropriateness of a linear model, and explore the predictive power of the model. You will use appropriate technology to perform the modeling tasks.For 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.Here are some possible topics: Choose an Olympic sport -- an event that interests you. Go to http://www.databaseolympics.com/ and collect data for winners in the event for at least 8 Olympic games (dating back to at least 1980). (Example: Winning times in Men's 400 m dash). Make a quick plot for yourself to "eyeball" whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a different event.) After you find the line of best fit, use your line to make a prediction for the next Olympics (2014 for a winter event, 2016 for a summer event ).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.Choose a sport that particularly interests you and find two variables that may exhibit a linear relationship. For instance, for each team for a particular season in baseball, find the total runs scored and the number of wins. Excellent websites: http://www.databasesports.com/ and http://www.baseball-reference.com/
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Submission instructions: compute and handwrite solutions submit all work/setup which 1a-c. Suppose we have a new type of M ...
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MTH 311 Pima Medical Institute Tucson Statistics Article Summary
Search the Scientific American website to find a recent article (within the last 10 years) about a statistical study on a ...
MTH 311 Pima Medical Institute Tucson Statistics Article Summary
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Multivariate Analysis of Variance, manova spss help
PLEASE FOLLOW THE INSTRUCTIONS LISTED AND USE THE SAME DATASET AND TEMPLATE FROM THE LAST ASSIGNMENT AND INCLUDE THE SAV F ...
Multivariate Analysis of Variance, manova spss help
PLEASE FOLLOW THE INSTRUCTIONS LISTED AND USE THE SAME DATASET AND TEMPLATE FROM THE LAST ASSIGNMENT AND INCLUDE THE SAV FILE:Multivariate Analysis
of Variance (MANOVA)This week, your
exploration of analysis of variance continues. MANOVA is yet another extension
of comparison of means tests. This week, you will be exposed to foundational,
one-way MANOVA models.MANOVA is similar to a
one-way ANOVA but with multiple outcome/dependent variables. The statistics
professor who is interested in different teaching methods could have three
different course types, each with a unique approach. As an outcome variable,
the professor may wish to examine mean differences in grade point average
(GPA), final test scores, and student anxiety. One could run three different
one-way ANOVA models but that would be cumbersome and could contribute to the
possibility of a type I error. With a one-way MANOVA, the comparison of means
could be performed in a single operation in statistical software.This week you explore
MANOVA, to see how it can serve as a basic building block to extend your
statistical testing to other applications. For example, just like adding a
covariate built on the one-way ANOVA to create an ANCOVA, adding a covariate
will create a MANCOVA model. You can also add additional factors, which will
give you a factorial MANOVA. MANOVA in SPSS Earlier this week, you practiced using MANOVA
models with SPSS and, ideally, used the Collaboration Lab to ask, answer, and
otherwise address any questions you had. In this Assignment, you apply what you
learned to answer a social research question using MANOVA.To prepare
Review the dataset provided.
Construct a research question based on one of those
datasets.
Pay attention to the assumptions of this test, and ask,
“Does it make sense to interpret the mean of this dependent variable?”
Use SPSS to answer the
research question you constructed. Then, compose a 1- to 2-paragraph analysis
in APA format in which you answer the following questions:1.What is the null hypothesis for your question?2.What research design(s) would align with this question?3.What dependent variable was used and how is it measured?4.What independent variable is used and how is it measured?5.If you found significance, what is the strength of the effect?6.What is the answer to your research question?7.What are the possible implications of social change?Be sure to include your
data output with your analysis.
Linear Project, math homework help
For this assignment you will implement a project involving linear curve-fitting and interpretation. You will assess the ap ...
Linear Project, math homework help
For this assignment you will implement a project involving linear curve-fitting and interpretation. You will assess the appropriateness of a linear model, and explore the predictive power of the model. You will use appropriate technology to perform the modeling tasks.For 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.Here are some possible topics: Choose an Olympic sport -- an event that interests you. Go to http://www.databaseolympics.com/ and collect data for winners in the event for at least 8 Olympic games (dating back to at least 1980). (Example: Winning times in Men's 400 m dash). Make a quick plot for yourself to "eyeball" whether the data points exhibit a relatively linear trend. (If so, proceed. If not, try a different event.) After you find the line of best fit, use your line to make a prediction for the next Olympics (2014 for a winter event, 2016 for a summer event ).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.Choose a sport that particularly interests you and find two variables that may exhibit a linear relationship. For instance, for each team for a particular season in baseball, find the total runs scored and the number of wins. Excellent websites: http://www.databasesports.com/ and http://www.baseball-reference.com/
5 pages
Tutor 1 1
Submission instructions: compute and handwrite solutions submit all work/setup which 1a-c. Suppose we have a new type of M ...
Tutor 1 1
Submission instructions: compute and handwrite solutions submit all work/setup which 1a-c. Suppose we have a new type of MBS to accommodate the ...
MTH 311 Pima Medical Institute Tucson Statistics Article Summary
Search the Scientific American website to find a recent article (within the last 10 years) about a statistical study on a ...
MTH 311 Pima Medical Institute Tucson Statistics Article Summary
Search the Scientific American website to find a recent article (within the last 10 years) about a statistical study on a topic that you find interesting related to business, government, or health care. Searching on the word “statistics” will give you a good list of articles to consider.(Note: The study CANNOT reference the Nurses’ Health Study!)This assignment requires Level 1 Writing Guidelines.Provide the full website address of the article. (2 points)In your own words, summarize the article, and identify whether the study was observational or an experiment. (3 points.)State the goal or purpose of the study, i.e. what did the researchers want to find out? (2 points)Identify the population, sample, and variables of interest.(3 points)State what conclusions were drawn from the sample (4 points)Identify any flaws or potential flaws in this study (3 points) Points Possible: 20Content: 17 ptsWriting Guidelines: 3 ptsReference:http://www.scientificamerican.com/search/
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