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Montgomery College Binomial Distribution & Histogram Questions
Experience 5 - The Binomial Distribution Questions: How do you know when to use the binomial distribution to ...
Montgomery College Binomial Distribution & Histogram Questions
Experience 5 - The Binomial Distribution Questions: How do you know when to use the binomial distribution to model a situation? What are the requirements for a binomial experiment? [4 bullets] When dealing with the binomial distribution, why are the possible values for the random variable always 0,1,2,3,…,n where n is the number of trials or sample size? Why can't we use negative values, or fractions, or numbers greater than n? [3 sentences] Under what conditions is a binomial distribution symmetric? Skewed left? Skewed right? Why? [3 sentences] How is the area in the bars of a binomial histogram related to the probability of choosing those X values? (Hint: figure it out for a single bar) [3 sentences] Experience 6 - Continuous Probability Distributions Critical Thinking Questions What does a z-score tell you about a number in a data set? [1 sentence] What two quantities do we need to fully describe a normal distribution? [1 sentence] How is probability determined from a continuous distribution? Why is this easy for the uniform distribution and not so easy for the normal distribution? [2 sentences] What does the symmetric bell shape of the normal curve imply about the distribution of individuals in a normal population? [2 sentences] How can the empirical rule be restated in terms of z-scores and percentiles? Restate it for four of the seven z-scores. Hint: Use the definitions of z-score and percentile and avoid use of the phrase "standard deviation" or the numbers 68, 95, and 99.7. [4 statements] Experience 7 - Normal Distributions Critical Thinking Questions Why is the probability that a continuous random variable is equal to a single number zero? (i.e. Why is P(X=a)=0 for any number a) [1 sentence] In what ways can a quick drawing of the normal curve (not a detailed empirical rule drawing but a simple one like that shown in the instructor's video) be used to estimate or verify your answer to a problem like practice exercises 2-4? [2 sentences] The empirical rule says that 95% of the population is within 2 standard deviations of the mean, but when I find the z-scores that mark off the middle 95% of the standard normal distribution I calculate -1.96 and 1.96. Is this a contradiction? Why or why not? In other words why are the normal distribution calculators not agreeing with the empirical rule? [2 sentences] Suppose you randomly select an individual from a population that is normally distributed and they are above average. When you find out the probability of randomly selecting that individual is very very small, what are some possible explanations? In other words what does this very very small probability suggest? [3 sentences]
MATH 160 Cuyamaca College Regression Line and Absolute Prediction Error Questions
InstructionsProgress CheckUse this activity to assess whether you can: Use StatCrunch to graph a scatterplot with its leas ...
MATH 160 Cuyamaca College Regression Line and Absolute Prediction Error Questions
InstructionsProgress CheckUse this activity to assess whether you can: Use StatCrunch to graph a scatterplot with its least squares regression line and to simultaneously produce the equation of the regression line along with its correlation coefficient, r.Identify the x with the largest absolute prediction error.Explain why a given data point is an outlier.In this activity you will use StatCrunch and embed your results in an essay question. The essay questions are not automatically graded; your instructor will enter the points for these questions later. WARNING: you will need to enter your response to each essay question with every attempt. Your instructor will only grade the essay for your attempt with the highest total score for the automatically graded questions.Discussion BoardUse the Module 27 discussion board (opens in a new tab) to ask questions or provide feedback about the problems in any Module 27 activity - including this lab. Attempt HistoryAttemptTimeScoreLATESTAttempt 125 minutes4 out of 10 ** Some questions not yet graded Correct answers are hidden.Score for this attempt: 4 out of 10 *Submitted May 2 at 10:36amThis attempt took 25 minutes.Learn by DoingSome 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.A list of StatCrunch directions is provided at the bottom of this text-box.ContextThe modern Olympic Games have changed dramatically since their inception in 1896. Are athletes getting better? We will use regression to investigate the change in winning times for one event—the men's 1,500 meter race.VariablesYear: the year of the Olympic Games, from 1896 to 2000.Time: the winning time for the 1,500 meter race, in seconds.Since the winning time depends on the year, the Year since 1896 is the explanatory variable, and the Winning time is the response variable.DataDownload the olympics (Links to an external site.) datafile for the men's 1,500 meter race. Then upload the datafile in StatCrunch. If you need a reminder about how to do this, review the list of StatCrunch directions below.PromptIn the first two questions below, you will use StatCrunch to produce and examine the scatterplot for the olympics datafile. You will also use StatCrunch to find the regression equation and correlation coefficient. List of StatCrunch DirectionsAs you work through numbers 1) and 2) below, refer back to these StatCrunch directions when you need a quick reminder.Purchase StatCrunch (You only need to do this once.)Open StatCrunchDownload Excel Data FileUpload Excel Data File to StatCrunchDownload StatCrunch Output Window (do not take screenshots; please use these directions)Upload Files to Your Stat-Class Folder in CanvasEmbed Pictures in a Discussion Post (do not post attachments; please use these directions)Copy and Paste StatCrunch Text OutputProduce Scatterplot, LSR Line, Regression Equation, & R (All at the same time!)UnansweredQuestion 1Not yet graded / 3 ptsThese directions assume that you have uploaded the olympics datafile in StatCrunch, and the StatCrunch worksheet with the data is open. If not, please see the Data section above.Using the year since 1896 as the explanatory variable and the winning time as the response variable: graph the scatterplot with the regression line and produce the regression equation with the correlation coefficient - all at the same time (directions)Toggle to the output page with the scatterplot and regression line. Notice that the data has a strong linear association, so it makes sense to use linear regression. (Always check the form of the scatterplot before using linear regression.)Download the StatCrunch output page with your scatterplot and regression line graphed together. (directions)Save the .png file (the graph of your scatterplot and regression line) to your Stats-Class folder. (directions)Embed the .png file for your scatterplot and regression line in the text-box below. (directions)UnansweredQuestion 2Not yet graded / 3 ptsThese directions assume you have produced the Simple linear regression results in a multipage StatCrunch output window. If not please see the previous question.Toggle to the StatCrunch output page with the regression equation, correlation coefficient, and other statistics.Under the heading Simple linear regression results, copy and paste the first five lines (dependent variable, independent variable, linear equation, sample size, and R) into the text-box below. (directions)Simple linear regression results:Dependent Variable: TimeIndependent Variable: Year Time = 994.19341 - 0.39304496 YearSample size: 24R (correlation coefficient) = -0.89075356Question 32 / 2 ptsFor which of the years 1900, 1940, or 2000 is the absolute prediction error the largest? 1900 1940undefinedundefined2000undefinedCorrect. Vertical distance from the regression line is the prediction error. The data point for 2000 is father from the regression line than the other options, so the prediction error is largest.Question 42 / 2 ptsFor the year 1896, the winning time for the men's 1500-meter race is an outlier. In what ways is this data point an outlier? Check all that applyundefinedan outlier with respect to Y=Time Correct. The winning time in 1896 is much larger than the other winning times.undefinedan outlier with respect to X=Yearundefinedundefinedan outlier with respect to the form of the relationship between X and YCorrect. The data point (1896, 273) deviates from the pattern in the rest of the data. It does not follow the strong, negative association or the linear pattern. Score: 4 out of 10
5 pages
Mathematics Quiz
If x²+4y²=40 and xy=6, what will be the value of x+2y? This question represents a type of linear equation called a simul ...
Mathematics Quiz
If x²+4y²=40 and xy=6, what will be the value of x+2y? This question represents a type of linear equation called a simultaneous equation.
3 pages
Psy380 Rs Project1
A study was done to explore the number of chocolate bars consumed by 16-year-old girls in a Use the data from the chocola ...
Psy380 Rs Project1
A study was done to explore the number of chocolate bars consumed by 16-year-old girls in a Use the data from the chocolate bar study to answer the ...
SNHU Housing Price Prediction Model Project
Overview
Recall that samples are used to generate a statistic, which businesses use to estimate the population parameter. ...
SNHU Housing Price Prediction Model Project
Overview
Recall that samples are used to generate a statistic, which businesses use to estimate the population parameter. You have learned how to take samples from populations and use them to produce statistics. For two quantitative variables, businesses can use scatterplots and the correlation coefficient to explore a potential linear relationship. Furthermore, they can quantify the relationship in a regression equation.
Prompt
This assignment picks up where the Module Two assignment left off and will use components of that assignment as a foundation.
You have submitted your initial analysis to the sales team at D.M. Pan Real Estate Company. You will continue your analysis of the provided Real Estate Data spreadsheet using your selected region to complete your analysis. You may refer back to the initial report you developed in the Module Two Assignment Template to continue the work. This document and the National Statistics and Graphs spreadsheet will support your work on the assignment.
Note: In the report you prepare for the sales team, the dependent, or response, variable (y) should be the listing price and the independent, or predictor, variable (x) should be the square feet.
Using the Module Three Assignment Template, specifically address the following:
Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment.
Determine r: Determine r and what it means. (What is the relationship between the variables?)
Determine the strength of the correlation (weak, moderate, or strong).
Discuss how you determine the direction of the association between the two variables.
Is there a positive or negative association?
What do you see as the direction of the correlation?
Examine the Slope and Intercepts: Examine the slopeb1{"version":"1.1","math":"b1"} and intercept b0{"version":"1.1","math":"b0"}.
Draw conclusions from the slope and intercept in the context of this problem.
Does the intercept make sense based on your observation of the line of best fit?
Determine the value of the land only.
Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens when x=0, which is the y-intercept. Does this value make sense in context?
Determine the R-squared Coefficient: Determine the R-squared value.
Discuss what R-squared means in the context of this analysis.
Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions:
Is the square footage for homes in your selected region different than for homes overall in the United States?
For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)?
What square footage range would the graph be best used for?
IS 3310 TU Suicide Rate Analysis Information System & Data Analytics Memorandum
Draft IRPDue Sep 20 by 11:59pm Points 100 Submitting a file upload Available Sep 9 at 12am - Oct 4 at 11:59pm 26 daysSumma ...
IS 3310 TU Suicide Rate Analysis Information System & Data Analytics Memorandum
Draft IRPDue Sep 20 by 11:59pm Points 100 Submitting a file upload Available Sep 9 at 12am - Oct 4 at 11:59pm 26 daysSummary:In the Independent Research Project (IRP), students conduct an analysis project using SAS primarily and Excel as needed to analyze real-world data. Note: You are required to do a ANOVA Analysis or a Regression Analysis submit them showing Pr or P values, F values, R-square, B values, Plots, etc. and discuss these values and what they mean - as needed for the type of analysis you are conducting (ANOVA or Regression). Homogeneity of Variance of residuals can be assumed so you can proceed with your required analysis. Use DAX 3 or DAX 4 qnd/or DAX 5 video and Instructions to help conduct your analysis. As in previous DAX exercises, you must also attach the Summary Descriptive Statistics Table along with the ANOVA Results Table or Regression Results Table. Example ANOVA or Regression Tables required:DAX ANOVA Tables Required-1.pdfPreview the documentExample Regression Results Tables.pdfPreview the documentIn the first assignment (IRP Research Question and Data Set), you identified the data set you wanted to use, developed a research question, and identified a statistical question that could be answered analytically.In this assignment, you will create and submit a business memo (template attached) that clearly communicates your analysis of the data set you identified in the previous assignment. When you submit this draft of your IRP, you will get feedback from your classmates and your instructor. You should use this feedback to improve your IRP.In later assignments (IRP Memorandum, IRP Presentation, Presentation Questions Reply), you will submit the final version of your IRP, create and submit a PowerPoint presentation that summarizes your IRP, present your analysis via a recorded video presentation, and answer questions about your presentation.AttachmentsIRP Template 2018.docxPreview the documentRubricIRP Draft (2)IRP Draft (2)Criteria Ratings PtsThis criterion is linked to a Learning OutcomeUse of software tools10.0 ptsGood8.0 ptsFair0.0 ptsInadequate10.0 ptsThis criterion is linked to a Learning OutcomeData Display15.0 ptsGood12.0 ptsFair0.0 ptsInadequate15.0 ptsThis criterion is linked to a Learning OutcomeAnalysis Procedure25.0 ptsGood20.0 ptsFair0.0 ptsInadequate25.0 ptsThis criterion is linked to a Learning OutcomeData Analysis Conclusion20.0 ptsGood16.0 ptsFair0.0 ptsInadequate20.0 ptsThis criterion is linked to a Learning OutcomeRecommended Actions20.0 ptsGood16.0 ptsFair0.0 ptsInadequate20.0 ptsThis criterion is linked to a Learning OutcomeDecision Approach10.0 ptsGood8.0 ptsFair0.0 ptsInadequate10.0 ptsTotal Points: 100.0
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Montgomery College Binomial Distribution & Histogram Questions
Experience 5 - The Binomial Distribution Questions: How do you know when to use the binomial distribution to ...
Montgomery College Binomial Distribution & Histogram Questions
Experience 5 - The Binomial Distribution Questions: How do you know when to use the binomial distribution to model a situation? What are the requirements for a binomial experiment? [4 bullets] When dealing with the binomial distribution, why are the possible values for the random variable always 0,1,2,3,…,n where n is the number of trials or sample size? Why can't we use negative values, or fractions, or numbers greater than n? [3 sentences] Under what conditions is a binomial distribution symmetric? Skewed left? Skewed right? Why? [3 sentences] How is the area in the bars of a binomial histogram related to the probability of choosing those X values? (Hint: figure it out for a single bar) [3 sentences] Experience 6 - Continuous Probability Distributions Critical Thinking Questions What does a z-score tell you about a number in a data set? [1 sentence] What two quantities do we need to fully describe a normal distribution? [1 sentence] How is probability determined from a continuous distribution? Why is this easy for the uniform distribution and not so easy for the normal distribution? [2 sentences] What does the symmetric bell shape of the normal curve imply about the distribution of individuals in a normal population? [2 sentences] How can the empirical rule be restated in terms of z-scores and percentiles? Restate it for four of the seven z-scores. Hint: Use the definitions of z-score and percentile and avoid use of the phrase "standard deviation" or the numbers 68, 95, and 99.7. [4 statements] Experience 7 - Normal Distributions Critical Thinking Questions Why is the probability that a continuous random variable is equal to a single number zero? (i.e. Why is P(X=a)=0 for any number a) [1 sentence] In what ways can a quick drawing of the normal curve (not a detailed empirical rule drawing but a simple one like that shown in the instructor's video) be used to estimate or verify your answer to a problem like practice exercises 2-4? [2 sentences] The empirical rule says that 95% of the population is within 2 standard deviations of the mean, but when I find the z-scores that mark off the middle 95% of the standard normal distribution I calculate -1.96 and 1.96. Is this a contradiction? Why or why not? In other words why are the normal distribution calculators not agreeing with the empirical rule? [2 sentences] Suppose you randomly select an individual from a population that is normally distributed and they are above average. When you find out the probability of randomly selecting that individual is very very small, what are some possible explanations? In other words what does this very very small probability suggest? [3 sentences]
MATH 160 Cuyamaca College Regression Line and Absolute Prediction Error Questions
InstructionsProgress CheckUse this activity to assess whether you can: Use StatCrunch to graph a scatterplot with its leas ...
MATH 160 Cuyamaca College Regression Line and Absolute Prediction Error Questions
InstructionsProgress CheckUse this activity to assess whether you can: Use StatCrunch to graph a scatterplot with its least squares regression line and to simultaneously produce the equation of the regression line along with its correlation coefficient, r.Identify the x with the largest absolute prediction error.Explain why a given data point is an outlier.In this activity you will use StatCrunch and embed your results in an essay question. The essay questions are not automatically graded; your instructor will enter the points for these questions later. WARNING: you will need to enter your response to each essay question with every attempt. Your instructor will only grade the essay for your attempt with the highest total score for the automatically graded questions.Discussion BoardUse the Module 27 discussion board (opens in a new tab) to ask questions or provide feedback about the problems in any Module 27 activity - including this lab. Attempt HistoryAttemptTimeScoreLATESTAttempt 125 minutes4 out of 10 ** Some questions not yet graded Correct answers are hidden.Score for this attempt: 4 out of 10 *Submitted May 2 at 10:36amThis attempt took 25 minutes.Learn by DoingSome 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.A list of StatCrunch directions is provided at the bottom of this text-box.ContextThe modern Olympic Games have changed dramatically since their inception in 1896. Are athletes getting better? We will use regression to investigate the change in winning times for one event—the men's 1,500 meter race.VariablesYear: the year of the Olympic Games, from 1896 to 2000.Time: the winning time for the 1,500 meter race, in seconds.Since the winning time depends on the year, the Year since 1896 is the explanatory variable, and the Winning time is the response variable.DataDownload the olympics (Links to an external site.) datafile for the men's 1,500 meter race. Then upload the datafile in StatCrunch. If you need a reminder about how to do this, review the list of StatCrunch directions below.PromptIn the first two questions below, you will use StatCrunch to produce and examine the scatterplot for the olympics datafile. You will also use StatCrunch to find the regression equation and correlation coefficient. List of StatCrunch DirectionsAs you work through numbers 1) and 2) below, refer back to these StatCrunch directions when you need a quick reminder.Purchase StatCrunch (You only need to do this once.)Open StatCrunchDownload Excel Data FileUpload Excel Data File to StatCrunchDownload StatCrunch Output Window (do not take screenshots; please use these directions)Upload Files to Your Stat-Class Folder in CanvasEmbed Pictures in a Discussion Post (do not post attachments; please use these directions)Copy and Paste StatCrunch Text OutputProduce Scatterplot, LSR Line, Regression Equation, & R (All at the same time!)UnansweredQuestion 1Not yet graded / 3 ptsThese directions assume that you have uploaded the olympics datafile in StatCrunch, and the StatCrunch worksheet with the data is open. If not, please see the Data section above.Using the year since 1896 as the explanatory variable and the winning time as the response variable: graph the scatterplot with the regression line and produce the regression equation with the correlation coefficient - all at the same time (directions)Toggle to the output page with the scatterplot and regression line. Notice that the data has a strong linear association, so it makes sense to use linear regression. (Always check the form of the scatterplot before using linear regression.)Download the StatCrunch output page with your scatterplot and regression line graphed together. (directions)Save the .png file (the graph of your scatterplot and regression line) to your Stats-Class folder. (directions)Embed the .png file for your scatterplot and regression line in the text-box below. (directions)UnansweredQuestion 2Not yet graded / 3 ptsThese directions assume you have produced the Simple linear regression results in a multipage StatCrunch output window. If not please see the previous question.Toggle to the StatCrunch output page with the regression equation, correlation coefficient, and other statistics.Under the heading Simple linear regression results, copy and paste the first five lines (dependent variable, independent variable, linear equation, sample size, and R) into the text-box below. (directions)Simple linear regression results:Dependent Variable: TimeIndependent Variable: Year Time = 994.19341 - 0.39304496 YearSample size: 24R (correlation coefficient) = -0.89075356Question 32 / 2 ptsFor which of the years 1900, 1940, or 2000 is the absolute prediction error the largest? 1900 1940undefinedundefined2000undefinedCorrect. Vertical distance from the regression line is the prediction error. The data point for 2000 is father from the regression line than the other options, so the prediction error is largest.Question 42 / 2 ptsFor the year 1896, the winning time for the men's 1500-meter race is an outlier. In what ways is this data point an outlier? Check all that applyundefinedan outlier with respect to Y=Time Correct. The winning time in 1896 is much larger than the other winning times.undefinedan outlier with respect to X=Yearundefinedundefinedan outlier with respect to the form of the relationship between X and YCorrect. The data point (1896, 273) deviates from the pattern in the rest of the data. It does not follow the strong, negative association or the linear pattern. Score: 4 out of 10
5 pages
Mathematics Quiz
If x²+4y²=40 and xy=6, what will be the value of x+2y? This question represents a type of linear equation called a simul ...
Mathematics Quiz
If x²+4y²=40 and xy=6, what will be the value of x+2y? This question represents a type of linear equation called a simultaneous equation.
3 pages
Psy380 Rs Project1
A study was done to explore the number of chocolate bars consumed by 16-year-old girls in a Use the data from the chocola ...
Psy380 Rs Project1
A study was done to explore the number of chocolate bars consumed by 16-year-old girls in a Use the data from the chocolate bar study to answer the ...
SNHU Housing Price Prediction Model Project
Overview
Recall that samples are used to generate a statistic, which businesses use to estimate the population parameter. ...
SNHU Housing Price Prediction Model Project
Overview
Recall that samples are used to generate a statistic, which businesses use to estimate the population parameter. You have learned how to take samples from populations and use them to produce statistics. For two quantitative variables, businesses can use scatterplots and the correlation coefficient to explore a potential linear relationship. Furthermore, they can quantify the relationship in a regression equation.
Prompt
This assignment picks up where the Module Two assignment left off and will use components of that assignment as a foundation.
You have submitted your initial analysis to the sales team at D.M. Pan Real Estate Company. You will continue your analysis of the provided Real Estate Data spreadsheet using your selected region to complete your analysis. You may refer back to the initial report you developed in the Module Two Assignment Template to continue the work. This document and the National Statistics and Graphs spreadsheet will support your work on the assignment.
Note: In the report you prepare for the sales team, the dependent, or response, variable (y) should be the listing price and the independent, or predictor, variable (x) should be the square feet.
Using the Module Three Assignment Template, specifically address the following:
Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment.
Determine r: Determine r and what it means. (What is the relationship between the variables?)
Determine the strength of the correlation (weak, moderate, or strong).
Discuss how you determine the direction of the association between the two variables.
Is there a positive or negative association?
What do you see as the direction of the correlation?
Examine the Slope and Intercepts: Examine the slopeb1{"version":"1.1","math":"b1"} and intercept b0{"version":"1.1","math":"b0"}.
Draw conclusions from the slope and intercept in the context of this problem.
Does the intercept make sense based on your observation of the line of best fit?
Determine the value of the land only.
Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens when x=0, which is the y-intercept. Does this value make sense in context?
Determine the R-squared Coefficient: Determine the R-squared value.
Discuss what R-squared means in the context of this analysis.
Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions:
Is the square footage for homes in your selected region different than for homes overall in the United States?
For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)?
What square footage range would the graph be best used for?
IS 3310 TU Suicide Rate Analysis Information System & Data Analytics Memorandum
Draft IRPDue Sep 20 by 11:59pm Points 100 Submitting a file upload Available Sep 9 at 12am - Oct 4 at 11:59pm 26 daysSumma ...
IS 3310 TU Suicide Rate Analysis Information System & Data Analytics Memorandum
Draft IRPDue Sep 20 by 11:59pm Points 100 Submitting a file upload Available Sep 9 at 12am - Oct 4 at 11:59pm 26 daysSummary:In the Independent Research Project (IRP), students conduct an analysis project using SAS primarily and Excel as needed to analyze real-world data. Note: You are required to do a ANOVA Analysis or a Regression Analysis submit them showing Pr or P values, F values, R-square, B values, Plots, etc. and discuss these values and what they mean - as needed for the type of analysis you are conducting (ANOVA or Regression). Homogeneity of Variance of residuals can be assumed so you can proceed with your required analysis. Use DAX 3 or DAX 4 qnd/or DAX 5 video and Instructions to help conduct your analysis. As in previous DAX exercises, you must also attach the Summary Descriptive Statistics Table along with the ANOVA Results Table or Regression Results Table. Example ANOVA or Regression Tables required:DAX ANOVA Tables Required-1.pdfPreview the documentExample Regression Results Tables.pdfPreview the documentIn the first assignment (IRP Research Question and Data Set), you identified the data set you wanted to use, developed a research question, and identified a statistical question that could be answered analytically.In this assignment, you will create and submit a business memo (template attached) that clearly communicates your analysis of the data set you identified in the previous assignment. When you submit this draft of your IRP, you will get feedback from your classmates and your instructor. You should use this feedback to improve your IRP.In later assignments (IRP Memorandum, IRP Presentation, Presentation Questions Reply), you will submit the final version of your IRP, create and submit a PowerPoint presentation that summarizes your IRP, present your analysis via a recorded video presentation, and answer questions about your presentation.AttachmentsIRP Template 2018.docxPreview the documentRubricIRP Draft (2)IRP Draft (2)Criteria Ratings PtsThis criterion is linked to a Learning OutcomeUse of software tools10.0 ptsGood8.0 ptsFair0.0 ptsInadequate10.0 ptsThis criterion is linked to a Learning OutcomeData Display15.0 ptsGood12.0 ptsFair0.0 ptsInadequate15.0 ptsThis criterion is linked to a Learning OutcomeAnalysis Procedure25.0 ptsGood20.0 ptsFair0.0 ptsInadequate25.0 ptsThis criterion is linked to a Learning OutcomeData Analysis Conclusion20.0 ptsGood16.0 ptsFair0.0 ptsInadequate20.0 ptsThis criterion is linked to a Learning OutcomeRecommended Actions20.0 ptsGood16.0 ptsFair0.0 ptsInadequate20.0 ptsThis criterion is linked to a Learning OutcomeDecision Approach10.0 ptsGood8.0 ptsFair0.0 ptsInadequate10.0 ptsTotal Points: 100.0
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