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Explanation & Answer
sin(u - v) = sin(u)*cos(v) - sin(v)*cos(u).
We know sin(u) and cos(v) so have to find cos(u) and sin(v).
cos(u) = +-sqrt[1-(sin(u))^2] = +-sqrt(144/169) = +-(12/13). Choose "+" sign because 0<u<pi/2.
sin(v) = +-sqrt[1-(cos(v))^2] = +-sqrt(16/25) = +-(4/5). Choose "+", sin(v)>=0 when pi/2<v<pi.
Gathering all we obtain
sin(u-v) = (5/13)*(-3/5) - (4/5)*(12/13) = -(1/(5*13))*(15 + 48) = -63/65.
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Aberystwyth University Forecasting Models for The Rate of Inflation Analysis
Experiential Learning - Forecasting models for the rate of inflation Data The variable PCEP is the price index for per ...
Aberystwyth University Forecasting Models for The Rate of Inflation Analysis
Experiential Learning - Forecasting models for the rate of inflation Data The variable PCEP is the price index for personal consumption expenditures from the U.S. National Income and Product Accounts (NIPA). In this hands-on exercise you will construct forecasting models for the rate of inflation, based on PCEP. For this analysis, use the sample period 1963:Q1–2012:Q4 (where data before 1963 may be used, as necessary, as initial values for lags in regressions). Use the QLR test with 15% trimming to test the stability of the coefficients in the AR(2) model for “the change in inflation” . Is the AR(2) model stable? Explain. Compute the (annualized) inflation rate,Plot the value of Infl from 1963:Q1 through 2012:Q4. Based on the plot, do you think that Infl has a stochastic trend? Explain. Double click in the table below to access to the excel table. Yes, It’s going upward before 1980 and going down afterwards. Its randomly determined. Compute the first four autocorrelations of Plot the value of Infl from 1963:Q1 through 2012:Q4. The plot should look “choppy” or “jagged.”Explain why this behavior is consistent with the first autocorrelation that you computed in part (i) for . Infl 0.823-0.2860.748-0.4420.7190.0920.661-0.00 Compute Run an OLS regression of on . Does knowing the change in inflation this quarter help predict the change in inflation next quarter? Explain.Estimate an AR(2) model for Infl. Is the AR(2) model better than an AR(1) model? Explain.Estimate an AR(p) model for . What lag length is chosen by BIC? What lag length is chosen by AIC?Use the AR(2) model to predict “the change in inflation from 2012:Q4 to 2013:Q1”-that is, predict the value of Use the AR(2) model to predict “the level of the inflation rate” in 2013:Q1—that is, . Use the ADF test for the regression in Equation (14.31) with two lags of to test for a stochastic trend in .Is the ADF test based on Equation (14.31) preferred to the test based on Equation (14.32) for testing for stochastic trend in ? Explain.In (i) you used two lags of . Should you use more lags? Fewer lags? Explain.Based on the test you carried out in (i), does the AR model for contain a unit root? Explain carefully. (Hint: Does the failure to reject a null hypothesis mean that the null hypothesis is true?) Using the AR(2) model for with a sample period that begins in 1963:Q1, compute pseudo out-of-sample forecasts for the change in inflation beginning in 2003:Q1 and going through 2012:Q4. That is, compute:Are the pseudo out-of-sample forecasts biased?That is, do the forecast errors have a nonzero mean?How large is the RMSFE of the pseudo out-of-sample forecasts? Is this consistent with the AR(2) model for estimated over the 1963:Q1–2002:Q4 sample period?There is a large outlier in 2008:Q4. Why did inflation fall so much in 2008:Q4? (Hint: Collect some data on oil prices. What happened to oil prices during 2008?)
Assignment: Introduction to Quantitative Analysis: Confidence Intervals
With this assignment, you displayed data based on a categorical variable and continuous variable from a specific dataset. ...
Assignment: Introduction to Quantitative Analysis: Confidence Intervals
With this assignment, you displayed data based on a categorical variable and continuous variable from a specific dataset. In Week 3, you used the same variables as in Week 2 to perform a descriptive analysis of the data. For this Assignment, you will calculate a confidence interval in SPSS for one of the variables from your Week 2 and Week 3 Assignments. To prepare for this Assignment: Using the SPSS software, open the High School Longitudinal Study dataset (whichever you chose) from Week 2. Use this link https://nces.ed.gov/surveys/hsls09 Choose an appropriate variable from Weeks 2 and 3 and calculate a confidence interval in SPSS. Once you perform your confidence interval, review Chapter 5 and 11 of the Wagner text to understand how to copy and paste your output into your Word document. For this Assignment: Write a 3- to 4-paragraph analysis of your results and include a copy and paste of the appropriate visual display of the data into your document. Based on the results of your data in this confidence interval Assignment, provide a brief explanation of what the implications for social change might be. Use appropriate APA format. Refer to the APA manual for appropriate citations. Here are some APA references you can use to assist with the assignments. I need 4 to 6 references. References Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications. Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications. DATA FROM WEEK 2 Introduction to Quantitative Analysis: Visually Displaying Data Results Visual Display of Data 1st Variable: Is Science beneficial? 2nd Variable: Is There A Mean Difference for Science Based on Gender? Table 1: Mean Vs. Gender SCORES Gender Mean N Minimum Maximum Variance Female 24.35 14628 32 65 408.253 Male 44.70 14628 39 80 327.389 Total 37.34 29256 0 80 452.297 Analysis Results The results of figure1demonstrate that numerous students do strongly agree with science being beneficial for everyday life. From the pie chart, 61% of students are certain that science is beneficial with a small percentage of 5.67 disagreeing with the statement that was presented. The results from the second variable show mean variance in scores between the genders. The results show that males scored an average of 44.70 as their female counterpart averaged 24.35. The maximum score for male was 80% whereas for the female was 65%. Therefore, we can conclude that male are best performers in science. However, students are more likely to choose science related courses such as computer science and engineering as their career of choice hoping for better future life (Krapp& Prenzel, 2011). DATA FROM WEEK 3 Table Set 1.1: Workplace Discrimination Results Discrimination at Work in Past 5 yrs N Valid 1456 Missing 1411 Variance .234 Discrimination at Work in Past 5 Years Frequency Frequency Percent Valid Percent Cumulative Percent Yes 269 9.4 18.5 18.5 No 1094 38.2 75.1 93.6 Valid Did not work or did not seek work 93 3.2 6.4 100.0 Total 1456 50.8 100 Central Tendency and Variability Frankfort-Nachmias and Leon-Guerrero (2018) suggested that the central tendency is normally measured through three methods that comprise of mean (the average value of data), median (the middle value of an ordered dataset), and mode (the most frequent value of the data). Variability analyzes variance among distributions of a categorical data (Frankfort-Nachmias & Leon-Guerrero, 2018). The General Social Survey 2016 dataset was utilized to perform analyses of both the categorical and continuous variables (Wagner, 2016). Krishnamoorthy (2016) found that the central tendency (mean, median, and mode) were used to perform the analyses on the continuous variable. It is an appropriate methodology because it calculates a single value from a continuous data and describes the collection of data by locating the central-point from the continuous dataset (Krishnamoorthy, 2016). However, this study will specifically use mean for its overall central tendency results conclusion because the dataset is from a large sample size and does not include outliers. For the categorical variable analyses, frequency distribution and variance were used. The frequency distribution and variance are appropriate analyses methods for categorical variable (Manikandan, 2011). The frequency distribution displays different measurement groups and to compare number of observations for each group (Manikandan, 2011). Presentation of Results Categorical Variable: The categorical variable (Variable No: 178 DISWK5) was stratified into three groups 1=Yes and 2=No, and 3=Did not work or did not seek work Research Question: Does discrimination exists at your workplace? Table 2: Number of Hours Usually Worked in a Week Statistics N Valid 1646 Missing 1221 Mean 40.91 Median 40.00 Mode 40 Std. Deviation 14.406 The results of central tendency as illustrated in Table 2, indicates that the mean, median, and mode were equivalent to 40. Though, the mean score recorded a higher value of 40.91. The standard deviation, σ =14.406. Based on the dataset attributes (large sample size and absence of outliers), mean score is chosen. Therefore, the results confirm that American’s average number of working hours in a week is 40.91. That is equivalent to 41 hours a week.
MG 315 PU Unit 8 Statistics Regression Analysis Project Report
select any city in the US and select 40-45 houses that are for sale in that city. Any website can be used for example zill ...
MG 315 PU Unit 8 Statistics Regression Analysis Project Report
select any city in the US and select 40-45 houses that are for sale in that city. Any website can be used for example zillow.com, realtor.com, ect. I need to create an excel spreadsheet with a price range for example $100K to $500K with my dependent variable= Price of the home and Independent variable= SQFT, number of bedrooms, number of bathrooms, and age.
Purpose Statement and Model
1) In the introductory paragraph, state why the dependent variable has been chosen for analysis. Then make a general statement about the model:
“The dependent variable _Price of the home__ is determined by variables _SQFT__, _number of bathrooms__, _number of bedrooms__, and _age__.”
2) In the second paragraph, identify the primary independent variable and defend why it is important.
“The most important variable in this analysis is ________ because _________.” In this paragraph, cite and discuss the two research sources that support the thesis, i.e., the model.
3) Write the general form of the regression model (less intercept and coefficients), with the variables named appropriately so reader can identify each variable at a glance:
Dep_Var = Ind_Var_1 + Ind_Var_2 + Ind_Var_3
For instance, a typical model would be written:
Price_of_Home = Square_Footage + Number_Bedrooms + Lot_Size
Where
Price_of_Home: brief definition of dependent variable
Square_Footage: brief definition of first independent variable
Number_Bedrooms: brief definition of second independent variable
Age: brief definition of third independent variable
Definition of Variables
4) Define and defend all variables, including the dependent variable, in a single paragraph for each variable. Also, state the expectations for each independent variable. These paragraphs should be in numerical order, i.e., dependent variable, X1, then X2, etc.
In each paragraph, the following should be addressed:
How is the variable defined in the data source?
Which unit of measurement is used?
For the independent variables: why does the variable determine Y?
What sign is expected for the independent variable's coefficient, positive or negative? Why?
Data Description
5) In one paragraph, describe the data and identify the data sources.
From which general sources and from which specific tables are the data taken? (Citing a website is not acceptable.)
Which year or years were the data collected?
Are there any data limitations?
Presentation and Interpretation of Results
6) Write the regression (prediction) equation:
Dep_Var = Intercept + c1 * Ind_Var_1 + c2 * Ind_Var_2 + c3* Ind_Var_3
7) Identify and interpret the adjusted R2 (one paragraph):
Define “adjusted R2.”
What does the value of the adjusted R2 reveal about the model?
If the adjusted R2 is low, how has the choice of independent variables created this result?
8) Identify and interpret the F test (one paragraph):
Using the p-value approach, is the null hypothesis for the F test rejected or not rejected? Why or why not?
Interpret the implications of these findings for the model.
9) Identify and interpret the t tests for each of the coefficients (one separate paragraph for each variable, in numerical order):
Are the signs of the coefficients as expected? If not, why not?
For each of the coefficients, interpret the numerical value.
Using the p-value approach, is the null hypothesis for the t test rejected or not rejected for each coefficient? Why or why not?
Interpret the implications of these findings for the variable.
Identify the variable with the greatest significance.
10) Analyze multicollinearity of the independent variables (one paragraph):
Generate the correlation matrix.
Define multicollinearity.
Are any of the independent variables highly correlated with each other? If so, identify the variables and explain why they are correlated.
State the implications of multicollinearity (if found) for the model.
Cuyamaca College Let Learners Struggle Discussion
Learn by DoingHere are the directions, grading rubric, and definition of high-quality feedback for the Learn by Doing disc ...
Cuyamaca College Let Learners Struggle Discussion
Learn by DoingHere are the directions, grading rubric, and definition of high-quality feedback for the Learn by Doing discussion board exercises. As always, please do not continue without clicking on the Learn-by-Doing link in the previous sentence and reading every word!ContextTo prepare for this Learn by Doing discussion read the When and How to Let Learners Struggle (Links to an external site.) article (it's a blog post by Annie Murphy). This activity is included in the Reading Assignments and Quizzes portion of your grade. PromptIn your first post to the discussion respond to each of the following numbered items.Watch Nike’s Michael Jordan Failure Commercial (30 sec) and then respond to the question.
In this video, what is the main point of Michael Jordan’s message? You should be able to state his main point in one short sentence!Now watch Nike’s Michael Jordan Maybe It’s My Fault Commercial (1 min 2 sec) and then respond to the question.
In this video, what is the main point of Michael Jordan’s message, and how is it related to the main point in his Failure video? You should be able to state his main point in one short sentence as well!What is the main point of the Annie Murphy’s blog post: When and How to Let Learner’s Struggle? You should be able to state her main point in one short sentence!From the reading, what are the three conditions that promote beneficial struggle? How are the messages in the article and the Michael Jordan videos related?Do any of your instructors use the three conditions that promote beneficial struggle? If so, explain how and give examples. If not, use this space to write a quick note to a generic instructor explaining the value of productive failure and how to set up the three conditions that promote beneficial struggle in the classroom learning environment.If you're comfortable sharing with your group, give an example in your own life where failure has been productive and actually contributed to your success (your instructor will read your response to this question as well). If you're not comfortable sharing a personal example, share how you plan to address setbacks in this class so that they contribute to your success.
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Aberystwyth University Forecasting Models for The Rate of Inflation Analysis
Experiential Learning - Forecasting models for the rate of inflation Data The variable PCEP is the price index for per ...
Aberystwyth University Forecasting Models for The Rate of Inflation Analysis
Experiential Learning - Forecasting models for the rate of inflation Data The variable PCEP is the price index for personal consumption expenditures from the U.S. National Income and Product Accounts (NIPA). In this hands-on exercise you will construct forecasting models for the rate of inflation, based on PCEP. For this analysis, use the sample period 1963:Q1–2012:Q4 (where data before 1963 may be used, as necessary, as initial values for lags in regressions). Use the QLR test with 15% trimming to test the stability of the coefficients in the AR(2) model for “the change in inflation” . Is the AR(2) model stable? Explain. Compute the (annualized) inflation rate,Plot the value of Infl from 1963:Q1 through 2012:Q4. Based on the plot, do you think that Infl has a stochastic trend? Explain. Double click in the table below to access to the excel table. Yes, It’s going upward before 1980 and going down afterwards. Its randomly determined. Compute the first four autocorrelations of Plot the value of Infl from 1963:Q1 through 2012:Q4. The plot should look “choppy” or “jagged.”Explain why this behavior is consistent with the first autocorrelation that you computed in part (i) for . Infl 0.823-0.2860.748-0.4420.7190.0920.661-0.00 Compute Run an OLS regression of on . Does knowing the change in inflation this quarter help predict the change in inflation next quarter? Explain.Estimate an AR(2) model for Infl. Is the AR(2) model better than an AR(1) model? Explain.Estimate an AR(p) model for . What lag length is chosen by BIC? What lag length is chosen by AIC?Use the AR(2) model to predict “the change in inflation from 2012:Q4 to 2013:Q1”-that is, predict the value of Use the AR(2) model to predict “the level of the inflation rate” in 2013:Q1—that is, . Use the ADF test for the regression in Equation (14.31) with two lags of to test for a stochastic trend in .Is the ADF test based on Equation (14.31) preferred to the test based on Equation (14.32) for testing for stochastic trend in ? Explain.In (i) you used two lags of . Should you use more lags? Fewer lags? Explain.Based on the test you carried out in (i), does the AR model for contain a unit root? Explain carefully. (Hint: Does the failure to reject a null hypothesis mean that the null hypothesis is true?) Using the AR(2) model for with a sample period that begins in 1963:Q1, compute pseudo out-of-sample forecasts for the change in inflation beginning in 2003:Q1 and going through 2012:Q4. That is, compute:Are the pseudo out-of-sample forecasts biased?That is, do the forecast errors have a nonzero mean?How large is the RMSFE of the pseudo out-of-sample forecasts? Is this consistent with the AR(2) model for estimated over the 1963:Q1–2002:Q4 sample period?There is a large outlier in 2008:Q4. Why did inflation fall so much in 2008:Q4? (Hint: Collect some data on oil prices. What happened to oil prices during 2008?)
Assignment: Introduction to Quantitative Analysis: Confidence Intervals
With this assignment, you displayed data based on a categorical variable and continuous variable from a specific dataset. ...
Assignment: Introduction to Quantitative Analysis: Confidence Intervals
With this assignment, you displayed data based on a categorical variable and continuous variable from a specific dataset. In Week 3, you used the same variables as in Week 2 to perform a descriptive analysis of the data. For this Assignment, you will calculate a confidence interval in SPSS for one of the variables from your Week 2 and Week 3 Assignments. To prepare for this Assignment: Using the SPSS software, open the High School Longitudinal Study dataset (whichever you chose) from Week 2. Use this link https://nces.ed.gov/surveys/hsls09 Choose an appropriate variable from Weeks 2 and 3 and calculate a confidence interval in SPSS. Once you perform your confidence interval, review Chapter 5 and 11 of the Wagner text to understand how to copy and paste your output into your Word document. For this Assignment: Write a 3- to 4-paragraph analysis of your results and include a copy and paste of the appropriate visual display of the data into your document. Based on the results of your data in this confidence interval Assignment, provide a brief explanation of what the implications for social change might be. Use appropriate APA format. Refer to the APA manual for appropriate citations. Here are some APA references you can use to assist with the assignments. I need 4 to 6 references. References Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications. Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications. DATA FROM WEEK 2 Introduction to Quantitative Analysis: Visually Displaying Data Results Visual Display of Data 1st Variable: Is Science beneficial? 2nd Variable: Is There A Mean Difference for Science Based on Gender? Table 1: Mean Vs. Gender SCORES Gender Mean N Minimum Maximum Variance Female 24.35 14628 32 65 408.253 Male 44.70 14628 39 80 327.389 Total 37.34 29256 0 80 452.297 Analysis Results The results of figure1demonstrate that numerous students do strongly agree with science being beneficial for everyday life. From the pie chart, 61% of students are certain that science is beneficial with a small percentage of 5.67 disagreeing with the statement that was presented. The results from the second variable show mean variance in scores between the genders. The results show that males scored an average of 44.70 as their female counterpart averaged 24.35. The maximum score for male was 80% whereas for the female was 65%. Therefore, we can conclude that male are best performers in science. However, students are more likely to choose science related courses such as computer science and engineering as their career of choice hoping for better future life (Krapp& Prenzel, 2011). DATA FROM WEEK 3 Table Set 1.1: Workplace Discrimination Results Discrimination at Work in Past 5 yrs N Valid 1456 Missing 1411 Variance .234 Discrimination at Work in Past 5 Years Frequency Frequency Percent Valid Percent Cumulative Percent Yes 269 9.4 18.5 18.5 No 1094 38.2 75.1 93.6 Valid Did not work or did not seek work 93 3.2 6.4 100.0 Total 1456 50.8 100 Central Tendency and Variability Frankfort-Nachmias and Leon-Guerrero (2018) suggested that the central tendency is normally measured through three methods that comprise of mean (the average value of data), median (the middle value of an ordered dataset), and mode (the most frequent value of the data). Variability analyzes variance among distributions of a categorical data (Frankfort-Nachmias & Leon-Guerrero, 2018). The General Social Survey 2016 dataset was utilized to perform analyses of both the categorical and continuous variables (Wagner, 2016). Krishnamoorthy (2016) found that the central tendency (mean, median, and mode) were used to perform the analyses on the continuous variable. It is an appropriate methodology because it calculates a single value from a continuous data and describes the collection of data by locating the central-point from the continuous dataset (Krishnamoorthy, 2016). However, this study will specifically use mean for its overall central tendency results conclusion because the dataset is from a large sample size and does not include outliers. For the categorical variable analyses, frequency distribution and variance were used. The frequency distribution and variance are appropriate analyses methods for categorical variable (Manikandan, 2011). The frequency distribution displays different measurement groups and to compare number of observations for each group (Manikandan, 2011). Presentation of Results Categorical Variable: The categorical variable (Variable No: 178 DISWK5) was stratified into three groups 1=Yes and 2=No, and 3=Did not work or did not seek work Research Question: Does discrimination exists at your workplace? Table 2: Number of Hours Usually Worked in a Week Statistics N Valid 1646 Missing 1221 Mean 40.91 Median 40.00 Mode 40 Std. Deviation 14.406 The results of central tendency as illustrated in Table 2, indicates that the mean, median, and mode were equivalent to 40. Though, the mean score recorded a higher value of 40.91. The standard deviation, σ =14.406. Based on the dataset attributes (large sample size and absence of outliers), mean score is chosen. Therefore, the results confirm that American’s average number of working hours in a week is 40.91. That is equivalent to 41 hours a week.
MG 315 PU Unit 8 Statistics Regression Analysis Project Report
select any city in the US and select 40-45 houses that are for sale in that city. Any website can be used for example zill ...
MG 315 PU Unit 8 Statistics Regression Analysis Project Report
select any city in the US and select 40-45 houses that are for sale in that city. Any website can be used for example zillow.com, realtor.com, ect. I need to create an excel spreadsheet with a price range for example $100K to $500K with my dependent variable= Price of the home and Independent variable= SQFT, number of bedrooms, number of bathrooms, and age.
Purpose Statement and Model
1) In the introductory paragraph, state why the dependent variable has been chosen for analysis. Then make a general statement about the model:
“The dependent variable _Price of the home__ is determined by variables _SQFT__, _number of bathrooms__, _number of bedrooms__, and _age__.”
2) In the second paragraph, identify the primary independent variable and defend why it is important.
“The most important variable in this analysis is ________ because _________.” In this paragraph, cite and discuss the two research sources that support the thesis, i.e., the model.
3) Write the general form of the regression model (less intercept and coefficients), with the variables named appropriately so reader can identify each variable at a glance:
Dep_Var = Ind_Var_1 + Ind_Var_2 + Ind_Var_3
For instance, a typical model would be written:
Price_of_Home = Square_Footage + Number_Bedrooms + Lot_Size
Where
Price_of_Home: brief definition of dependent variable
Square_Footage: brief definition of first independent variable
Number_Bedrooms: brief definition of second independent variable
Age: brief definition of third independent variable
Definition of Variables
4) Define and defend all variables, including the dependent variable, in a single paragraph for each variable. Also, state the expectations for each independent variable. These paragraphs should be in numerical order, i.e., dependent variable, X1, then X2, etc.
In each paragraph, the following should be addressed:
How is the variable defined in the data source?
Which unit of measurement is used?
For the independent variables: why does the variable determine Y?
What sign is expected for the independent variable's coefficient, positive or negative? Why?
Data Description
5) In one paragraph, describe the data and identify the data sources.
From which general sources and from which specific tables are the data taken? (Citing a website is not acceptable.)
Which year or years were the data collected?
Are there any data limitations?
Presentation and Interpretation of Results
6) Write the regression (prediction) equation:
Dep_Var = Intercept + c1 * Ind_Var_1 + c2 * Ind_Var_2 + c3* Ind_Var_3
7) Identify and interpret the adjusted R2 (one paragraph):
Define “adjusted R2.”
What does the value of the adjusted R2 reveal about the model?
If the adjusted R2 is low, how has the choice of independent variables created this result?
8) Identify and interpret the F test (one paragraph):
Using the p-value approach, is the null hypothesis for the F test rejected or not rejected? Why or why not?
Interpret the implications of these findings for the model.
9) Identify and interpret the t tests for each of the coefficients (one separate paragraph for each variable, in numerical order):
Are the signs of the coefficients as expected? If not, why not?
For each of the coefficients, interpret the numerical value.
Using the p-value approach, is the null hypothesis for the t test rejected or not rejected for each coefficient? Why or why not?
Interpret the implications of these findings for the variable.
Identify the variable with the greatest significance.
10) Analyze multicollinearity of the independent variables (one paragraph):
Generate the correlation matrix.
Define multicollinearity.
Are any of the independent variables highly correlated with each other? If so, identify the variables and explain why they are correlated.
State the implications of multicollinearity (if found) for the model.
Cuyamaca College Let Learners Struggle Discussion
Learn by DoingHere are the directions, grading rubric, and definition of high-quality feedback for the Learn by Doing disc ...
Cuyamaca College Let Learners Struggle Discussion
Learn by DoingHere are the directions, grading rubric, and definition of high-quality feedback for the Learn by Doing discussion board exercises. As always, please do not continue without clicking on the Learn-by-Doing link in the previous sentence and reading every word!ContextTo prepare for this Learn by Doing discussion read the When and How to Let Learners Struggle (Links to an external site.) article (it's a blog post by Annie Murphy). This activity is included in the Reading Assignments and Quizzes portion of your grade. PromptIn your first post to the discussion respond to each of the following numbered items.Watch Nike’s Michael Jordan Failure Commercial (30 sec) and then respond to the question.
In this video, what is the main point of Michael Jordan’s message? You should be able to state his main point in one short sentence!Now watch Nike’s Michael Jordan Maybe It’s My Fault Commercial (1 min 2 sec) and then respond to the question.
In this video, what is the main point of Michael Jordan’s message, and how is it related to the main point in his Failure video? You should be able to state his main point in one short sentence as well!What is the main point of the Annie Murphy’s blog post: When and How to Let Learner’s Struggle? You should be able to state her main point in one short sentence!From the reading, what are the three conditions that promote beneficial struggle? How are the messages in the article and the Michael Jordan videos related?Do any of your instructors use the three conditions that promote beneficial struggle? If so, explain how and give examples. If not, use this space to write a quick note to a generic instructor explaining the value of productive failure and how to set up the three conditions that promote beneficial struggle in the classroom learning environment.If you're comfortable sharing with your group, give an example in your own life where failure has been productive and actually contributed to your success (your instructor will read your response to this question as well). If you're not comfortable sharing a personal example, share how you plan to address setbacks in this class so that they contribute to your success.
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