Introductory Statistics Q2
Section outline
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Mrs. Sara Scholes • sscholes@mountainheightsacademy.org • 801.980.0091
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All work for each week is due at 6:00 pm on Friday. If you miss an assignment and would like to make it up, please fill out the Late Work Request Form. You can receive 80% of the total earned points. No late work will be accepted during the last week of each quarter.
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Students mustMark as done
Start Here. Learn How to Navigate This Course.
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Graphs Review
Learning Objectives: I can interpret, understand and create graphs that represent categorical and quantifiable data.
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Types of Data & Their Graphs
Learning Objectives: I can classify data as nominal, ordinal, ratio or interval. I can construct graphs of quantitative and qualitative data sets.
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Measures of Center
Learning Objectives: I can calculate the following measures of center by hand and with Google Spreadsheets: mean, median and mode. I can find the 5 measures necessary to create Box and Whisker plots and can construct them.
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Measures of Spread
Learning Objectives: I understand how outliers can affect the mean and median. I can describe shapes of data distributions by looking at their graphs. I can calculate the following measures of spread: range, IQR, variance and standard deviation. I understand the formula to calculate standard deviation and what it tells me about the spread of a data set.
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Students mustReceive a grade
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Students mustReceive a grade
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Shapes of Distributions
Learning Objectives: I understand what standard deviation is, what it calculates, and how it describes spread of a data set. I can describe the shape of a data distribution and determine the best measure of center and spread to describe the data set.
All work this week is due on Wednesday, November 22nd.
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Real World Data Sets
Learning Objectives: I can use what I've learned about measures of center and spread to summarize real world data sets. I can use these statistics to make conclusions and recommendations about the data sets.
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Students mustReceive a grade
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The Normal Distribution
Learning Objectives: I can identify data sets that are approximately normal distributions. I can interpret a normal distribution curve and can use the 68-95-99.7 rule to identify how data is distributed. I can use a standard normal distribution curve to calculate and interpret z-scores.
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Students mustMark as done
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Calculating Confidence Intervals
Learning Objectives: I can use a standard normal distribution curve to compare z-scores of different data sets. I understand what confidence intervals are and how they are calculated. I can find the necessary information from a data set and calculate a 90, 95 and 99 percent confidence interval by hand.
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Students mustReceive a grade
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Confidence Interval Project Intro
Two Day Week! All work this week is due on Tuesday, December 19th by 6pm.
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Students mustMake forum posts: 1
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Confidence Interval Project
Learning Objectives: I can examine a sample data set to make inferences about a population. I can calculate and interpret measures of spread and center for real world data sets. I understand bootstrapping and sampling with replacement and can use it as a method to gain a larger picture of a population. I can begin to informally create a confidence interval of a population mean.
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Students mustReceive a grade
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Confidence Interval Project
Learning Objectives: I can use bootstrapping with a sample data set to estimate a population mean within a certain confidence interval.
Short Week! Last day of the quarter is Thursday, January 11th at 6pm!
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