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      <title>IM1- Unit 4- Descriptive Statistics by Erica Campos</title>
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      <pubDate>2017-07-16 00:57:21 UTC</pubDate>
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         <title>Essential Questions</title>
         <author>camposroom104</author>
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         <description><![CDATA[<ul><li>How can data be represented using various methods?</li><li>How can a two way frequency table be used to<br>collect data?</li><li>How can a scatter plot estimate further<br>predictions for the data given?</li><li>Does the representation of all data sets look<br>exactly the same? Explain why or why not.&nbsp;</li><li>How can you visually represent the difference<br>between data sets? How can this representation<br>be used to find trends?</li><li>How are correlation and causation different?&nbsp;</li></ul>]]></description>
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         <pubDate>2017-07-16 05:43:30 UTC</pubDate>
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         <title>Learning Objectives</title>
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         <description><![CDATA[<ul><li>Summarize, represent, and interpret data on a single count or measurement variable.</li><li>Summarize, represent, and interpret data on two categorical and quantitative variables.</li><li>Interpret linear models.</li><li>Students will interpret data given various data representations.</li><li>Look at a variety of social and political data.</li><li>Interpret differences in shape, center, and spread in the context of data sets, accounting for extreme data points.</li><li>Students will be able to categorize and compare data using a two-way frequency table.</li><li>Students will interpret relative frequencies in the context of the data, including joint, marginal, and<br>conditional relative frequencies.</li><li>Students will recognize possible associations and trends in the data.</li><li>Students will create scatterplots.</li><li>Students compute the correlation coefficient using technology.</li><li>Students will distinguish between correlation and causation and work with situations where they are<br>mistakenly interchanged.</li><li>Students will fit a linear or exponential function to a scatter plot.</li></ul>]]></description>
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         <pubDate>2017-07-16 05:43:37 UTC</pubDate>
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         <title>Standards</title>
         <author>camposroom104</author>
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         <description><![CDATA[<div>PRIORITY:<br>S.ID.1 Represent data with plots on the real number line (dot plots, histograms, and box plots).<br><br>S.ID.5​ Summarize categorical data for two categories in two-way frequency tables. Interpret relative<br>frequencies in the context of the data (including joint, marginal, and conditional relative frequencies).<br>Recognize possible associations and trends in the data.<br><br>SID.6​ Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.<br>a. Fit a function to the data; use functions fitted to data to solve problems in the context of the<br>data. Use given functions or choose a function suggested by the context. Emphasize linear and<br>exponential models.<br>b. Informally assess the fit of a function by plotting and analyzing residuals.<br>c. Fit a linear function for scatter plots that suggest a linear association<br><br>S.ID.9​ Distinguish between correlation and causation.<br><br>SUPPORTING:<br>S.ID.2​ Use statistics appropriate to the shape of the data distribution to compare center (median, mean)<br>and spread (interquartile range, standard deviation) of two or more different data sets.<br><br>S.ID.3​ Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).<br><br>S.ID.7​ Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the<br>context of the data.<br><br>S.ID.8​ Compute (using technology) and interpret the correlation coefficient of a linear fit.</div>]]></description>
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         <pubDate>2017-07-16 05:43:41 UTC</pubDate>
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         <title>Tier 2</title>
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         <pubDate>2017-07-19 04:13:39 UTC</pubDate>
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         <title>Tier 3</title>
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         <link>https://padlet.com/camposroom104/IM1unit4/wish/179014106</link>
         <description><![CDATA[<div>data distribution<br>shape&nbsp;<br>center<br>spread<br>median<br>statistical<br>representation<br>outlier<br>data set<br>data trends<br>quantitative and categorical data<br>dot plot<br>Histogram<br>box plot<br>data distribution<br>statistical<br>representation<br>data trends<br>two-way frequency table<br>relative frequency table<br>shape&nbsp;<br>center&nbsp;<br>spread<br>outlier<br>joint<br>marginal&nbsp;<br>and<br>conditional&nbsp;<br>relative<br>frequencies<br>Bivariate data<br>Scatter plot<br>Slope<br>outlier<br>Causation<br>Correlation<br>statistical<br>representation<br>data set<br>data trends<br>quantitative and<br>categorical data,<br>standard deviation<br>correlation coefficient</div>]]></description>
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         <pubDate>2017-07-19 04:13:43 UTC</pubDate>
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         <pubDate>2017-08-06 19:21:09 UTC</pubDate>
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         <pubDate>2017-08-06 19:26:01 UTC</pubDate>
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         <description><![CDATA[<div>Construct a box-and-whisker plot</div>]]></description>
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         <pubDate>2018-01-05 04:50:32 UTC</pubDate>
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         <description><![CDATA[<div>Constructing a box-and-whisker plot</div>]]></description>
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         <pubDate>2018-01-05 04:54:35 UTC</pubDate>
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         <description><![CDATA[<div>Box-and-Whisker Plot EXAMPLE</div>]]></description>
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         <pubDate>2018-01-05 22:25:22 UTC</pubDate>
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         <description><![CDATA[<div>Box-and-whisker plot worksheet</div>]]></description>
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         <pubDate>2018-01-05 22:27:03 UTC</pubDate>
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         <pubDate>2018-01-05 22:30:44 UTC</pubDate>
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         <pubDate>2018-01-05 22:40:41 UTC</pubDate>
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         <pubDate>2018-01-06 06:02:51 UTC</pubDate>
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         <pubDate>2018-01-06 06:11:56 UTC</pubDate>
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         <pubDate>2018-01-07 05:39:50 UTC</pubDate>
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         <pubDate>2018-02-07 04:40:00 UTC</pubDate>
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