MMA.MATH.9.B

Analyze numerical data using measures of central tendency (mean, median, and mode) and variability (range, interquartile range or IQR, and standard deviation) in order to make inferences with normal distributions.

Mathematical Models with Applications · Texas Essential Knowledge and Skills (TEKS) · TEKS 2012

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Vocabulary
numerical datameasures of central tendencymeanmedianmodevariabilityrangeinterquartile rangeIQRstandard deviationinferencesnormal distributions
Skills
  • analyze (numerical data using measures of central tendency and variability) #dok2
  • calculate (mean, median, mode, range, interquartile range, standard deviation) #dok1
  • make (inferences with normal distributions) #dok3
  • interpret (measures of central tendency and variability in context) #dok2
Learning Targets
  • I can calculate the mean, median, mode, range, interquartile range, and standard deviation for a given data set. #dok1
  • I can analyze a set of numerical data using measures of central tendency and variability. #dok2
  • I can interpret the results of mean, median, mode, range, interquartile range, and standard deviation in the context of real-world scenarios. #dok2
  • I can make inferences about a population using measures of central tendency and variability with normal distributions. #dok3
Big Ideas
  • Measures of central tendency and variability help summarize and describe numerical data.
  • Inferences about populations can be made using data analysis and understanding normal distributions.
Essential Questions
  • How do different measures of central tendency and variability help us understand a set of numerical data?
  • In what situations might the mean not be the best measure of central tendency?
  • What information do measures of variability provide that measures of central tendency do not?
  • How can we use data and the concept of normal distribution to make predictions or inferences?
  • Why is it important to consider both central tendency and variability when analyzing data?