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Keiser University Week 2 Sauerkraut Fermentation Discussion
Part 1: FermentationFermentation is an important process in biology. It is also an important process in which many foods w ...
Keiser University Week 2 Sauerkraut Fermentation Discussion
Part 1: FermentationFermentation is an important process in biology. It is also an important process in which many foods we eat and beverages we drink are made. Now that you have had a chance to study the various ways in which fermentation can be used, you will choose a topic from the table below that uses an organism that undergoes fermentation to produce the desired product and discuss. Find a credible and reliable, scientific resource that augments what you have already learned about fermentation and the product is produces and address the following questions.
Which food product did you choose?
What does this process involve (describe the method)?
What types of other foods can be prepared using this method?
What type of organism (living creature) is used for this product of fermentation?
NOTE: Neither open resources nor open blogs can be used as sources of information; see 'Researching Information' within the class for more information on appropriate credible/reliable sources of information.Do not use any websites associated with the following:
Encyclopedia/Wikipedia Articles
Course Descriptions
Sites posted by elementary and high school classes
Sites designed to "sell" you on a religious view, a philosophy or a product
Sites already posted by another student or the instructor. If the root web address is the same, don't use it. No duplicate websites are permitted.
Share the website with the class by writing a full paragraph summary (150 words minimum) of what you have learned or have found particularly helpful when reviewing the website. Do not copy and paste directly from the website. This blog should be written in your own words. Any content and paraphrased information from the resource (URL) must be properly cited per APA including the specific URL (website) you are recommending. The blog post must be directly posted in the classroom; please do not upload a Word document only. The initial blog post is strongly suggested to be posted by 11:59 p.m. ET Thursday of Week 2.
Part 2: Blog Response PostPeruse some of the Blog entries made by your fellow classmates. Choose at least one Fermentation blog) that you found useful and comment on how the website helped you with this week's content. The comment should be 100 words minimum.For Part 2 – posting the Blog Response Post to a fellow classmate, click the “Week 2 Assignment - Website Recommendation Blog” to enter the assignment. The screen will refresh and on the right side will be a black bar with “Blog details” on it and below that will be your name and a down arrow. Click the arrow and all the people who have posted blog posts will be listed. Read through the post by selecting the fellow student’s name and the screen will refresh – scroll to bottom and on the bottom left will be the student’s blog post. When ready to respond, click “Comment” at the end of the blog post and then a space will open below the blog post you are responding to. When done with the response blog post, click “Add”. Then the response blog post is posted.Both parts (1 & 2) of the Week 2 Assignment must be posted on separate days. The Week 2 Assignment (both Parts 1 & 2) are due and must be completed by 11:59 p.m. ET Sunday of Week 2.The Week 2 Assignment - Website Recommendation Blog is worth a potential total maximum of 100 points and there are two components:
Part 1: your initial blog post, 150 words minimum, discussing a credible and reliable, scientific website (10 points maximum) that augments what you have already learned about fermentation and the particular product of fermentation. The initial blog post will be scored on topic [product of fermentation] (10 points), grammar and spelling (10 points), content and discussion of the specific product of fermentation (40 points maximum), submitted on a different day than Part 2 (5 points) and meeting the minimum blog post length of 150 words (5 points)
Part 2: response blog post to a fellow classmate and it will be scored on grammar and spelling (2.5 points), content and discussion of what was learned about the product of fermentation from the blog post and resource (15 points maximum) and meeting the minimum blog post length of 100 words (2.5 points maximum).(AFTER PART 1 IS SUBMITTED- I WILL BE COPYING AND PASTING 1 RESPONSE FROM A CLASSMATE IN ORDER FOR YOU TO COMPLETE THE SECOND PART OF THE ASSIGMENT) ( 1ST PART NEEDS TO BE DONE BY 05/21/2020 AND 2ND PART NEEDS TO BE DONE BY 05/24/2020)
5 pages
Lab 6 Electrochemistry
1. Using your own words, explain what red-ox reaction mean? Is a global reaction in which two reactions take place: an oxi ...
Lab 6 Electrochemistry
1. Using your own words, explain what red-ox reaction mean? Is a global reaction in which two reactions take place: an oxidation reaction, in which a ...
3 pages
Osmosis Lab Report
The spontaneous movement of water across a semi-permeable membrane is called osmosis. Osmosis is the movement from area of ...
Osmosis Lab Report
The spontaneous movement of water across a semi-permeable membrane is called osmosis. Osmosis is the movement from area of low concentration to area ...
Glendale Community College Reemergence and Spread of Malaria Discussion
Explain how each of the following could lead to the reemergence of malaria in the United States: (a) global warming, (b) i ...
Glendale Community College Reemergence and Spread of Malaria Discussion
Explain how each of the following could lead to the reemergence of malaria in the United States: (a) global warming, (b) increased travel of individuals from endemic regions to the United States, (c) increased immigration of individuals from endemic regions to the United States, and (d) laws protecting wetlands.
8 pages
Acceleration Labexploration
In this laboratory, you will continue your exploration of descriptions of one-dimensional motion. This week’s experiment ...
Acceleration Labexploration
In this laboratory, you will continue your exploration of descriptions of one-dimensional motion. This week’s experiment will particularly focus on ...
RSCH 8210 WU The Afrobarometer Dataset and Key Leadership Figure Analysis
Use SPSS to answer the research question. Post your response to the following:
If you are using the Afrobarometer Datas ...
RSCH 8210 WU The Afrobarometer Dataset and Key Leadership Figure Analysis
Use SPSS to answer the research question. Post your response to the following:
If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.
What is your research question?
What is the null hypothesis for your question?
What research design would align with this question?
What dependent variable was used and how is it measured?
What independent variables are used and how are they measured? What is the justification for including these predictor variables?
If you found significance, what is the strength of the effect?
Explain your results for a lay audience, explain what the answer to your research question.
Discussion
Use the General Social Survey data set and construct a research question that can be answered using multiple regression. To do this you will need to select three variables that are measured on an interval or ratio level. In SPSS they will be listed as scale data in the variable view.
Select two IVs (AKA predictor variables) that could be used to predict the value of the DV (AKA, criterion variable or outcome variable). For example, the length and the weight of a car (predictor variables) could be used to predict its miles per gallon (outcome variable). Use an alpha level of .05 for these analyses.
In this week’s video example, 3 variables were selected from the GSS data set. Note that our data set has been edited and is not exactly the same as theirs. You can follow along and you should get similar results, but you will not get exactly the same values. That is OK, remember, we have a revised data set that is a little different than the one used for the video example. Here are the variables used in the example:
DV = sei10, R's socioeconomic index (2010)
IV 1 = prestg10, Rs occupational prestige score (2010)
IV 2 = educ, HIGHEST YEAR OF SCHOOL COMPLETED
Do Not use these variables for your discussion or application assignment for multiple regression.
Multiple Regression
Here is an overview of how to run the Multiple Regression
Analyze > Regression > Linear
Enter your 1 (and only 1) DV into the Dependent box.
Enter your 2 (and only 2) IVs into the Independent(s) box.
Click OK
Reading the Output & Reporting Results
Model Summary
The overall Model Summary shows the R, R Square, and Adjusted R Square. In my experience, we typically report the R Square value. Yet our video recommends reporting the Adjusted R square. For this example, R square and Adjusted R square are the same, R2 = .787. However, sometimes they will be different.
Because we have conflicting information, you may report either. However, clearly state whether you are reporting R square or the adjusted R square.
Figure 1. Model Summary for multiple regression in SPSS
The next box shows the ANOVA summary.
Figure 2. ANOVA summary of the overall model for multiple regression
This is for the overall model with your two independent variables and your one dependent variable. Notice that the Sig. column shows .000, we would report the results like this:
The purpose of this standard regression analysis was to examine the combined and relative effects of the respondents’ occupational prestige score and highest year of school completed in predicting their socioeconomic status. The combined effect of prestg10 and educ statistically significantly predicted sei10, F(2, 1404) = 2595.24, p < .001, adjusted R2 = .787. The two predictors combined, explained about 79% of the variability in socioeconomic status index scores. This is a large effect.
By convention, 2% is considered a small effect, 13% is medium, and 26% is large.
Here is a resource: http://core.ecu.edu/psyc/wuenschk/docs30/EffectSizeConventions.pdf
Coefficients
The next box (Figure 3) shows the Coefficients. With 2 IVs, there will be 2 t-tests to inspect and to report. The t-test results presented in the Coefficients box tests whether each individual IV significantly predicts the DV. Specifically, it tests the null hypothesis that the B coefficient is equal to 0.
Figure 3. Coefficients
IV 1
The B coefficient for the RS occupational prestige score, 1.055, is significantly different from zero because the sig column shows .000. We could report this as:
While holding the effects of the other predictor constant, the RS occupational prestige score significantly predicts socioeconomic index values, t(1404) = 52.30, p < .001. For each 1- point increase in prestige score, socioeconomic status index values are expected to increase by 1.055 points.
IV 2
The B coefficient for Highest year of school completed, 1.226, was significantly different from 0 because the Sig. column shows .000. We could report this as:
While holding the effects of the other predictor constant, the highest year of school completed significantly predicts socioeconomic index values, t(1404) = 13.60, p < .001. For each 1- point increase in prestige score, socioeconomic status index values are expected to increase by 1.226 points.
Constant
As we saw in Week 8, the Constant B of -13.373 is the y-intercept. At point (0, -13.373) the regression line will cross the y-axis (the vertical line).
The Multiple Regression Equation
For this analysis, we could report the Multiple Regression equation as
Predicted sei10 = -13.373 + 1.055(prestg10) + 1.226(educ)
For example, if someone had a prestige score of 1 and an education score of 15 we could predict their sei10 score:
Predicted sei10 = -13.373 + 1.055(prestg10) + 1.226(educ)
Predicted sei10 = -13.373 + 1.055(1) + 1.226(15)
Predicted sei10 = -13.373 + 1.055 + 18.39
Predicted sei10 = 6.062
A person with a prestige score of 1, who attended 15 years of schooling is predicted to have a socioeconomic index score of 6.062.
The Null Hypotheses for Multiple Regression is not in any of our course materials.
I don’t recall seeing this explicitly stated in our course materials. Technically, there is one null hypothesis for the combined model, and then one for each of the IVs.
Here is an example for a Multiple Regression model with 2 IVs and one DV.
Null 1: The combined effect of the two IVs will not significantly predict the DV.
Null 2: The First IV is not a significant predictor of the DV, while controlling for the second IV.
Null 3: The Second IV is not a significant predictor of the DV, while controlling for the first IV.
Hopefully it is obvious that this is a generic example and you would insert the names of your variables in place of First IV, Second IV, and DV.
This is an introduction
This week we learn how to run a multiple regression and how to interpret the results and report them. However, there is much more to learn on this topic. I have greatly oversimplified the information we typically report for a multiple regression analysis.
Next week you will learn about the assumptions of multiple regression. That is, you will learn about several additional statistics that we must check ahead of time to ensure it is appropriate to run and interpret a multiple regression analysis. For now, just focus on the general idea of multiple regression and what the results tell you.
NOTES:
- all three variables should be interval or ratio variables. They should be listed as scale variables in your data set
- the variable you wish to predict should be entered as the DV
- leave the Method as “Enter”, this is referred to as a standard regression and it enters all of the IVs at the same time, whether or not they are significantly related to the DV
- select Only 2 IVs for the discussion and assignments for multiple regression.
You should address all of the following:
- State your research question
- State your null hypothesis
- Describe your outcome variable (DV) and how it was measured.
- Describe the predictor variables (IVs) and how they were measured. (Select 2 and only 2 IVs)
- explain whether one IV was a control and if so, why
- explain the rationale for selecting IVs
- clearly identify the name of the variables as they appear in the data set
- Indicate whether the overall model was significant or not, explain how you know
- indicate whether each of your two predictors were significant or not, explain how you know
- report the results of your multiple regression analysis in APA format, interpret the effect size using R square (see pp. 440 – 441, and p. 450 in your text and my example below)
- ensure you clearly explain whether the variables are significant predictors or not and state the regression equation for for your results (use my example above as a guide).
- Discuss implications for positive social change
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Keiser University Week 2 Sauerkraut Fermentation Discussion
Part 1: FermentationFermentation is an important process in biology. It is also an important process in which many foods w ...
Keiser University Week 2 Sauerkraut Fermentation Discussion
Part 1: FermentationFermentation is an important process in biology. It is also an important process in which many foods we eat and beverages we drink are made. Now that you have had a chance to study the various ways in which fermentation can be used, you will choose a topic from the table below that uses an organism that undergoes fermentation to produce the desired product and discuss. Find a credible and reliable, scientific resource that augments what you have already learned about fermentation and the product is produces and address the following questions.
Which food product did you choose?
What does this process involve (describe the method)?
What types of other foods can be prepared using this method?
What type of organism (living creature) is used for this product of fermentation?
NOTE: Neither open resources nor open blogs can be used as sources of information; see 'Researching Information' within the class for more information on appropriate credible/reliable sources of information.Do not use any websites associated with the following:
Encyclopedia/Wikipedia Articles
Course Descriptions
Sites posted by elementary and high school classes
Sites designed to "sell" you on a religious view, a philosophy or a product
Sites already posted by another student or the instructor. If the root web address is the same, don't use it. No duplicate websites are permitted.
Share the website with the class by writing a full paragraph summary (150 words minimum) of what you have learned or have found particularly helpful when reviewing the website. Do not copy and paste directly from the website. This blog should be written in your own words. Any content and paraphrased information from the resource (URL) must be properly cited per APA including the specific URL (website) you are recommending. The blog post must be directly posted in the classroom; please do not upload a Word document only. The initial blog post is strongly suggested to be posted by 11:59 p.m. ET Thursday of Week 2.
Part 2: Blog Response PostPeruse some of the Blog entries made by your fellow classmates. Choose at least one Fermentation blog) that you found useful and comment on how the website helped you with this week's content. The comment should be 100 words minimum.For Part 2 – posting the Blog Response Post to a fellow classmate, click the “Week 2 Assignment - Website Recommendation Blog” to enter the assignment. The screen will refresh and on the right side will be a black bar with “Blog details” on it and below that will be your name and a down arrow. Click the arrow and all the people who have posted blog posts will be listed. Read through the post by selecting the fellow student’s name and the screen will refresh – scroll to bottom and on the bottom left will be the student’s blog post. When ready to respond, click “Comment” at the end of the blog post and then a space will open below the blog post you are responding to. When done with the response blog post, click “Add”. Then the response blog post is posted.Both parts (1 & 2) of the Week 2 Assignment must be posted on separate days. The Week 2 Assignment (both Parts 1 & 2) are due and must be completed by 11:59 p.m. ET Sunday of Week 2.The Week 2 Assignment - Website Recommendation Blog is worth a potential total maximum of 100 points and there are two components:
Part 1: your initial blog post, 150 words minimum, discussing a credible and reliable, scientific website (10 points maximum) that augments what you have already learned about fermentation and the particular product of fermentation. The initial blog post will be scored on topic [product of fermentation] (10 points), grammar and spelling (10 points), content and discussion of the specific product of fermentation (40 points maximum), submitted on a different day than Part 2 (5 points) and meeting the minimum blog post length of 150 words (5 points)
Part 2: response blog post to a fellow classmate and it will be scored on grammar and spelling (2.5 points), content and discussion of what was learned about the product of fermentation from the blog post and resource (15 points maximum) and meeting the minimum blog post length of 100 words (2.5 points maximum).(AFTER PART 1 IS SUBMITTED- I WILL BE COPYING AND PASTING 1 RESPONSE FROM A CLASSMATE IN ORDER FOR YOU TO COMPLETE THE SECOND PART OF THE ASSIGMENT) ( 1ST PART NEEDS TO BE DONE BY 05/21/2020 AND 2ND PART NEEDS TO BE DONE BY 05/24/2020)
5 pages
Lab 6 Electrochemistry
1. Using your own words, explain what red-ox reaction mean? Is a global reaction in which two reactions take place: an oxi ...
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1. Using your own words, explain what red-ox reaction mean? Is a global reaction in which two reactions take place: an oxidation reaction, in which a ...
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The spontaneous movement of water across a semi-permeable membrane is called osmosis. Osmosis is the movement from area of ...
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RSCH 8210 WU The Afrobarometer Dataset and Key Leadership Figure Analysis
Use SPSS to answer the research question. Post your response to the following:
If you are using the Afrobarometer Datas ...
RSCH 8210 WU The Afrobarometer Dataset and Key Leadership Figure Analysis
Use SPSS to answer the research question. Post your response to the following:
If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.
What is your research question?
What is the null hypothesis for your question?
What research design would align with this question?
What dependent variable was used and how is it measured?
What independent variables are used and how are they measured? What is the justification for including these predictor variables?
If you found significance, what is the strength of the effect?
Explain your results for a lay audience, explain what the answer to your research question.
Discussion
Use the General Social Survey data set and construct a research question that can be answered using multiple regression. To do this you will need to select three variables that are measured on an interval or ratio level. In SPSS they will be listed as scale data in the variable view.
Select two IVs (AKA predictor variables) that could be used to predict the value of the DV (AKA, criterion variable or outcome variable). For example, the length and the weight of a car (predictor variables) could be used to predict its miles per gallon (outcome variable). Use an alpha level of .05 for these analyses.
In this week’s video example, 3 variables were selected from the GSS data set. Note that our data set has been edited and is not exactly the same as theirs. You can follow along and you should get similar results, but you will not get exactly the same values. That is OK, remember, we have a revised data set that is a little different than the one used for the video example. Here are the variables used in the example:
DV = sei10, R's socioeconomic index (2010)
IV 1 = prestg10, Rs occupational prestige score (2010)
IV 2 = educ, HIGHEST YEAR OF SCHOOL COMPLETED
Do Not use these variables for your discussion or application assignment for multiple regression.
Multiple Regression
Here is an overview of how to run the Multiple Regression
Analyze > Regression > Linear
Enter your 1 (and only 1) DV into the Dependent box.
Enter your 2 (and only 2) IVs into the Independent(s) box.
Click OK
Reading the Output & Reporting Results
Model Summary
The overall Model Summary shows the R, R Square, and Adjusted R Square. In my experience, we typically report the R Square value. Yet our video recommends reporting the Adjusted R square. For this example, R square and Adjusted R square are the same, R2 = .787. However, sometimes they will be different.
Because we have conflicting information, you may report either. However, clearly state whether you are reporting R square or the adjusted R square.
Figure 1. Model Summary for multiple regression in SPSS
The next box shows the ANOVA summary.
Figure 2. ANOVA summary of the overall model for multiple regression
This is for the overall model with your two independent variables and your one dependent variable. Notice that the Sig. column shows .000, we would report the results like this:
The purpose of this standard regression analysis was to examine the combined and relative effects of the respondents’ occupational prestige score and highest year of school completed in predicting their socioeconomic status. The combined effect of prestg10 and educ statistically significantly predicted sei10, F(2, 1404) = 2595.24, p < .001, adjusted R2 = .787. The two predictors combined, explained about 79% of the variability in socioeconomic status index scores. This is a large effect.
By convention, 2% is considered a small effect, 13% is medium, and 26% is large.
Here is a resource: http://core.ecu.edu/psyc/wuenschk/docs30/EffectSizeConventions.pdf
Coefficients
The next box (Figure 3) shows the Coefficients. With 2 IVs, there will be 2 t-tests to inspect and to report. The t-test results presented in the Coefficients box tests whether each individual IV significantly predicts the DV. Specifically, it tests the null hypothesis that the B coefficient is equal to 0.
Figure 3. Coefficients
IV 1
The B coefficient for the RS occupational prestige score, 1.055, is significantly different from zero because the sig column shows .000. We could report this as:
While holding the effects of the other predictor constant, the RS occupational prestige score significantly predicts socioeconomic index values, t(1404) = 52.30, p < .001. For each 1- point increase in prestige score, socioeconomic status index values are expected to increase by 1.055 points.
IV 2
The B coefficient for Highest year of school completed, 1.226, was significantly different from 0 because the Sig. column shows .000. We could report this as:
While holding the effects of the other predictor constant, the highest year of school completed significantly predicts socioeconomic index values, t(1404) = 13.60, p < .001. For each 1- point increase in prestige score, socioeconomic status index values are expected to increase by 1.226 points.
Constant
As we saw in Week 8, the Constant B of -13.373 is the y-intercept. At point (0, -13.373) the regression line will cross the y-axis (the vertical line).
The Multiple Regression Equation
For this analysis, we could report the Multiple Regression equation as
Predicted sei10 = -13.373 + 1.055(prestg10) + 1.226(educ)
For example, if someone had a prestige score of 1 and an education score of 15 we could predict their sei10 score:
Predicted sei10 = -13.373 + 1.055(prestg10) + 1.226(educ)
Predicted sei10 = -13.373 + 1.055(1) + 1.226(15)
Predicted sei10 = -13.373 + 1.055 + 18.39
Predicted sei10 = 6.062
A person with a prestige score of 1, who attended 15 years of schooling is predicted to have a socioeconomic index score of 6.062.
The Null Hypotheses for Multiple Regression is not in any of our course materials.
I don’t recall seeing this explicitly stated in our course materials. Technically, there is one null hypothesis for the combined model, and then one for each of the IVs.
Here is an example for a Multiple Regression model with 2 IVs and one DV.
Null 1: The combined effect of the two IVs will not significantly predict the DV.
Null 2: The First IV is not a significant predictor of the DV, while controlling for the second IV.
Null 3: The Second IV is not a significant predictor of the DV, while controlling for the first IV.
Hopefully it is obvious that this is a generic example and you would insert the names of your variables in place of First IV, Second IV, and DV.
This is an introduction
This week we learn how to run a multiple regression and how to interpret the results and report them. However, there is much more to learn on this topic. I have greatly oversimplified the information we typically report for a multiple regression analysis.
Next week you will learn about the assumptions of multiple regression. That is, you will learn about several additional statistics that we must check ahead of time to ensure it is appropriate to run and interpret a multiple regression analysis. For now, just focus on the general idea of multiple regression and what the results tell you.
NOTES:
- all three variables should be interval or ratio variables. They should be listed as scale variables in your data set
- the variable you wish to predict should be entered as the DV
- leave the Method as “Enter”, this is referred to as a standard regression and it enters all of the IVs at the same time, whether or not they are significantly related to the DV
- select Only 2 IVs for the discussion and assignments for multiple regression.
You should address all of the following:
- State your research question
- State your null hypothesis
- Describe your outcome variable (DV) and how it was measured.
- Describe the predictor variables (IVs) and how they were measured. (Select 2 and only 2 IVs)
- explain whether one IV was a control and if so, why
- explain the rationale for selecting IVs
- clearly identify the name of the variables as they appear in the data set
- Indicate whether the overall model was significant or not, explain how you know
- indicate whether each of your two predictors were significant or not, explain how you know
- report the results of your multiple regression analysis in APA format, interpret the effect size using R square (see pp. 440 – 441, and p. 450 in your text and my example below)
- ensure you clearly explain whether the variables are significant predictors or not and state the regression equation for for your results (use my example above as a guide).
- Discuss implications for positive social change
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