Difference between revisions of "SOCR Events July2008"
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== Logistics== | == Logistics== | ||
| − | * '''Date''': Tue., July 29, 2008, | + | * '''Date''': Tue., July 29, 2008, 3:15-4:00 PM. |
* '''Presenter''': [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], [http://www.SOCR.ucla.edu Statistics Online Computational Resource (SOCR)] | * '''Presenter''': [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], [http://www.SOCR.ucla.edu Statistics Online Computational Resource (SOCR)] | ||
* '''Title''': ''Using SOCR to motivate simulation experiments in middle and high school'' | * '''Title''': ''Using SOCR to motivate simulation experiments in middle and high school'' | ||
| − | * '''Venue''': | + | * '''Venue''': [http://www.library.ucla.edu/college/powell/powinfo.htm Powell Library] ([http://www.clicc.ucla.edu/classroom_tools.asp CLICC Classroom C], [http://www.computerlabs.ucla.edu/Info.asp?LabID=4 Powell 320C], [http://www.clicc.ucla.edu/labservices_webcam.asp Powell Building Realtime WebCam], use only the North-West elevator/stairway) |
* '''Sponsors''': [http://censusatschool-california.stat.ucla.edu/ California CensusAtSchool], [http://www.stat.ucla.edu/ UCLA Statistics], [http://www.censusatschool.ntu.ac.uk/ C@S], [[SOCR]] | * '''Sponsors''': [http://censusatschool-california.stat.ucla.edu/ California CensusAtSchool], [http://www.stat.ucla.edu/ UCLA Statistics], [http://www.censusatschool.ntu.ac.uk/ C@S], [[SOCR]] | ||
| − | * '''Audience''': This presentation is intended for middle school teachers in various science and quantitative disciplines. | + | * '''Audience''': This presentation is intended for middle and high school teachers in various science and quantitative disciplines. |
* '''Overarching Goals''': To present an integrated approach for technology enhanced instruction using free Internet-based resources (web-applets, instructional materials and learning activities) | * '''Overarching Goals''': To present an integrated approach for technology enhanced instruction using free Internet-based resources (web-applets, instructional materials and learning activities) | ||
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* [http://socr.ucla.edu/htmls/SOCR_Charts.html Exploratory Data Analysis] | * [http://socr.ucla.edu/htmls/SOCR_Charts.html Exploratory Data Analysis] | ||
* [http://socr.ucla.edu/htmls/SOCR_Analyses.html Common Statistical Analyses] | * [http://socr.ucla.edu/htmls/SOCR_Analyses.html Common Statistical Analyses] | ||
| + | <center>[[Image:SOCR_Events_July2008_CAS_Fig1.png|600px]]</center> | ||
| − | + | ==[[SOCR_Events_July2008_C1_S1 | Use basic concepts of probability to determine the likelihood of an event and compare the results of various experiments]]== | |
| − | + | [[Image:SOCR_Icon_VirtualDemos.png|150px|thumbnail|right| [[SOCR_EduMaterials_ExperimentsActivities | SOCR Virtual Experiments]] ]] | |
| − | |||
*Write the results of a probability experiment as a fraction, ratio, or decimal, between zero and one, or as a percent between zero and one hundred, inclusive | *Write the results of a probability experiment as a fraction, ratio, or decimal, between zero and one, or as a percent between zero and one hundred, inclusive | ||
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*Compare individual, small group, and large group results of a probability experiment | *Compare individual, small group, and large group results of a probability experiment | ||
| − | + | ==[[SOCR_Events_July2008_C2_S1 | Display and compare data to make predictions and formulate conclusions]]== | |
| + | [[Image:SOCR_Icon_Data.png|150px|thumbnail|right| [[SOCR_Data | SOCR Datasets]] ]] | ||
*Display data using tables, scatter plots, and circle graphs | *Display data using tables, scatter plots, and circle graphs | ||
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*Propose and justify inferences and predictions based on data | *Propose and justify inferences and predictions based on data | ||
| − | + | ==[[SOCR_Events_July2008_C3_S1 |Calculate probabilities of events and compare theoretical and experimental probability]]== | |
| + | [[Image:SOCR_Icon_ToolsActivities.png|150px|thumbnail|right| [[SOCR_EduMaterials | SOCR Tools & Activities]] ]] | ||
Use of the Fundamental Counting Principle, complement, theoretical probability, experiment, data, percentile, histogram, box-and-whisker plot, spread | Use of the Fundamental Counting Principle, complement, theoretical probability, experiment, data, percentile, histogram, box-and-whisker plot, spread | ||
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* Collect and interpret data to show that as the number of trials increases, experimental probability approaches the theoretical probability | * Collect and interpret data to show that as the number of trials increases, experimental probability approaches the theoretical probability | ||
| − | + | ==[[SOCR_Events_July2008_C4_S1 | Formulate questions and answer the questions by organizing and analyzing data]]== | |
* Formulate questions that can be answered through data collection and analysis | * Formulate questions that can be answered through data collection and analysis | ||
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* Use graphical representations and numerical summaries to answer questions and interpret data | * Use graphical representations and numerical summaries to answer questions and interpret data | ||
| − | + | ==[[SOCR_Events_July2008_C5_S1 | Summarize, display, and analyze bivariate data]]== | |
Use of scatter plot, positive correlation, negative correlation, no correlation, line of best fit, bivariate data. | Use of scatter plot, positive correlation, negative correlation, no correlation, line of best fit, bivariate data. | ||
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* Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data | * Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data | ||
| − | + | ==[[SOCR_Events_July2008_C6_S1 |Apply basic concepts of probability]]== | |
| + | [[Image:SOCR_Icon_ConceptsMethods.png|150px|thumbnail|right| [[SOCR_EduMaterials |SOCR Concepts & Methods]] ]] | ||
| + | |||
Use of permutation, combination, conditional probability, discrete random variable, standard | Use of permutation, combination, conditional probability, discrete random variable, standard | ||
deviation, interquartile range, percentile. | deviation, interquartile range, percentile. | ||
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* Define simple discrete random variables | * Define simple discrete random variables | ||
| − | + | ==[[SOCR_Events_July2008_C7_S1 |Use percentiles and measures of variability to analyze data]]== | |
* Compute different measures of spread, including the range, standard deviation, and interquartile range | * Compute different measures of spread, including the range, standard deviation, and interquartile range | ||
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* Use histograms to obtain percentiles | * Use histograms to obtain percentiles | ||
| − | + | ==[[SOCR_Events_July2008_C8_S1 |Compute probabilities for discrete distributions and use sampling distributions to calculate approximate probabilities]]== | |
* Obtain sample spaces and probability distributions for simple discrete random variables. | * Obtain sample spaces and probability distributions for simple discrete random variables. | ||
| − | *Compute binomial probabilities using Pascal’s Triangle and the Binomial Theorem. | + | * Compute binomial probabilities using Pascal’s Triangle and the Binomial Theorem. |
| − | *Compute means and variances of discrete random variables. | + | * Compute means and variances of discrete random variables. |
| − | *Compute probabilities using areas under the Normal Curve. | + | * Compute probabilities using areas under the Normal Curve. |
| − | *Calculate parameters of sampling distributions for the sample average, sum, and proportion. | + | * Calculate parameters of sampling distributions for the sample average, sum, and proportion. |
| − | *Calculate probabilities in real problems using sampling distributions. | + | * Calculate probabilities in real problems using sampling distributions. |
| − | + | ==[[SOCR_Events_July2008_C9_S1 |Analyze bivariate data using linear regression methods]]== | |
*Fit regression lines to pairs of numeric variables and calculate the means and standard deviations of the two variables and the correlation coefficient, using technology. | *Fit regression lines to pairs of numeric variables and calculate the means and standard deviations of the two variables and the correlation coefficient, using technology. | ||
Latest revision as of 17:01, 24 July 2008
Contents
- 1 SOCR Events - SOCR Demonstration at the CensusAtSchool International Workshop
- 2 Logistics
- 3 Objectives
- 4 Use basic concepts of probability to determine the likelihood of an event and compare the results of various experiments
- 5 Display and compare data to make predictions and formulate conclusions
- 6 Calculate probabilities of events and compare theoretical and experimental probability
- 7 Formulate questions and answer the questions by organizing and analyzing data
- 8 Summarize, display, and analyze bivariate data
- 9 Apply basic concepts of probability
- 10 Use percentiles and measures of variability to analyze data
- 11 Compute probabilities for discrete distributions and use sampling distributions to calculate approximate probabilities
- 12 Analyze bivariate data using linear regression methods
- 13 References
SOCR Events - SOCR Demonstration at the CensusAtSchool International Workshop
Logistics
- Date: Tue., July 29, 2008, 3:15-4:00 PM.
- Presenter: Ivo Dinov, Statistics Online Computational Resource (SOCR)
- Title: Using SOCR to motivate simulation experiments in middle and high school
- Venue: Powell Library (CLICC Classroom C, Powell 320C, Powell Building Realtime WebCam, use only the North-West elevator/stairway)
- Sponsors: California CensusAtSchool, UCLA Statistics, C@S, SOCR
- Audience: This presentation is intended for middle and high school teachers in various science and quantitative disciplines.
- Overarching Goals: To present an integrated approach for technology enhanced instruction using free Internet-based resources (web-applets, instructional materials and learning activities)
Objectives
The specific aims of this hands-on presentation are to discuss the use of the free and integrated Internet-based SOCR Resources for
- Data download and Data Generation
- Virtual Experimentation and Simulation
- Exploratory Data Analysis
- Common Statistical Analyses
Use basic concepts of probability to determine the likelihood of an event and compare the results of various experiments
- Write the results of a probability experiment as a fraction, ratio, or decimal, between zero and one, or as a percent between zero and one hundred, inclusive
- Compare experimental results with theoretical probability
- Compare individual, small group, and large group results of a probability experiment
Display and compare data to make predictions and formulate conclusions
- Display data using tables, scatter plots, and circle graphs
- Compare two similar sets of data on the same graph
- Compare two different kinds of graphs representing the same set of data
- Propose and justify inferences and predictions based on data
Calculate probabilities of events and compare theoretical and experimental probability
Use of the Fundamental Counting Principle, complement, theoretical probability, experiment, data, percentile, histogram, box-and-whisker plot, spread
- Solve counting problems using the Fundamental Counting Principle
- Calculate the probability of an event or sequence of events with and without replacement using models
- Recognize that the sum of the probability of an event and the probability of its complement is equal to one
- Make approximate predictions using theoretical probability and proportions
- Collect and interpret data to show that as the number of trials increases, experimental probability approaches the theoretical probability
Formulate questions and answer the questions by organizing and analyzing data
- Formulate questions that can be answered through data collection and analysis
- Determine the 25th and 75th percentiles (first and third quartiles) to obtain information about the spread of data
- Graphically summarize data of a single variable using histograms and box-and-whisker plots
- Compute the mean and median of a numerical characteristic and relate these values to the histogram of the data
- Use graphical representations and numerical summaries to answer questions and interpret data
Summarize, display, and analyze bivariate data
Use of scatter plot, positive correlation, negative correlation, no correlation, line of best fit, bivariate data.
- Collect, record, organize, and display a set of data with at least two variables
- Determine whether the relationship between two variables is approximately linear or nonlinear by examination of a scatter plot
- Characterize the relationship between two linear related variables as having positive, negative, or approximately zero correlation
- Estimate, interpret, and use lines fit to bivariate data
- Estimate the equation of a line of best fit to make and test conjectures
- Interpret the slope and y-intercept of a line through data
- Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data
Apply basic concepts of probability
Use of permutation, combination, conditional probability, discrete random variable, standard deviation, interquartile range, percentile.
- Distinguish between permutations and combinations and identify situations in which each is appropriate
- Calculate probabilities using permutations and combinations to count events
- Compute conditional and unconditional probabilities in various ways, including by definitions, the general multiplication rule, and probability trees
- Define simple discrete random variables
Use percentiles and measures of variability to analyze data
- Compute different measures of spread, including the range, standard deviation, and interquartile range
- Compare the effectiveness of different measures of spread, including the range, standard deviation, and interquartile range in specific situations
- Use percentiles to summarize the distribution of a numerical variable
- Use histograms to obtain percentiles
Compute probabilities for discrete distributions and use sampling distributions to calculate approximate probabilities
- Obtain sample spaces and probability distributions for simple discrete random variables.
- Compute binomial probabilities using Pascal’s Triangle and the Binomial Theorem.
- Compute means and variances of discrete random variables.
- Compute probabilities using areas under the Normal Curve.
- Calculate parameters of sampling distributions for the sample average, sum, and proportion.
- Calculate probabilities in real problems using sampling distributions.
Analyze bivariate data using linear regression methods
- Fit regression lines to pairs of numeric variables and calculate the means and standard deviations of the two variables and the correlation coefficient, using technology.
- Compute predictions of y-values for given x-values using a regression equation, and recognize the limitations of such predictions.
References
- Interactive Statistics Education EBook
- SOCR Home page: http://www.socr.ucla.edu
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