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Honors Statistics (CP) Pacing Guide
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Two-week summary of major instructional content and skills • 36 instructional weeks
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Week(s)Unit / TextMajor ContentMajor Skills and Assessments
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Weeks 1–2Unit 1: What is Statistics?What is Statistics (2d) • Variable Types (3d) • Data Colection and Sampling (2d) • Observational vs Experimental Studies (2d) • Misuses of Statistics (1d)Distinguish statistics as a discipline from individual data values; classify quantitative/categorical and discrete/continuous variables; select sampling methods that reduce bias; distinguish observational studies from experiments, identify treatment/control and explanatory/response variables, and recognize confounding or misuse of statistics.
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Weeks 3–4Unit 1: What is Statistics? / Unit 2: Displaying DataData Collection Chap 1 Lab* (2d) • Chapter 1 Review and Test (2d) • Organizing Data Introduction (1d) • Categorical Frequency Distributions (1d) • Grouped Frequency Distributions (3d) • Data Collection Lab (1d of 2d)Collect observational data using a structured lab; demonstrate Unit 1 understanding on review/test; organize data into categorical and grouped frequency distributions and interpret their structure. Begin the Unit 2 data-collection lab.
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Weeks 5–6Unit 2: Displaying DataData Collection Lab (1d of 2d) • Histograms (1d) • Distribution Shapes (1d) • Frequency Polygons and Ogives (3d) • Other Types of Categorical Graphs (2d) • Stem and Leaf Plots (1d) • Scatter Plots and Correlation* (1d of 2d)Complete the data-collection lab; construct and interpret histograms, distribution shapes, frequency polygons, ogives, categorical graphs, and stem-and-leaf plots; begin using scatter plots to examine relationships/correlation.
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Weeks 7–8Unit 2: Displaying Data / Unit 3: Descriptive StatisticsScatter Plots and Correlation* (1d of 2d) • Data Collection Lab* (2d) • Chapter 2 Review and Test (3d) • Introduction to Measures of Central Tendancy (1d) • Mean, Median, Mode, and Midrange (1d) • Mean and Modal Class of Grouped Data (2d)Complete scatter-plot/correlation work and a related data lab; demonstrate Unit 2 learning on review/test; identify measures of center and calculate mean, median, mode, midrange, and grouped-data mean/modal class.
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Weeks 9–10Unit 3: Descriptive StatisticsWeighted Mean (2d) • Distirbuiton Shapes (pt 2)* (1d) • Introduction to Variation and Range (1d) • Variation and Standard Deviation (2d) • Standard Deviation for Grouped Data* (1d) • Coefficeient of Variation (1d) • Data Collection Lab (1d) • Range Rule of Thumb and Chebyshevs Thm* (1d of 2d)Calculate weighted means, ranges, variance, standard deviation (including grouped data), and coefficient of variation; connect distribution shape to summary measures; use collected data to compare variability; begin applying the range rule of thumb and Chebyshev’s theorem.
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Weeks 11–12Unit 3: Descriptive StatisticsRange Rule of Thumb and Chebyshevs Thm* (1d of 2d) • The Empirical Rule (1d) • Z-score and Unusual Values (2d) • Percentiles and Quartiles (1d) • Ouliers (1d) • Boxplots (2d) • Chapter 3 Review and Test (2d)Apply Chebyshev’s theorem and the empirical rule; calculate and interpret z-scores and unusual values; determine percentiles, quartiles, IQR/outliers, and five-number summaries; construct boxplots and demonstrate Unit 3 mastery on the chapter assessment.
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Weeks 13–14Unit 4: Counting and ProbabilityIntroduction to Probability (1d) • Sample Spaces and Classic Probability (2d) • Empirical Probability (2d) • Subjective Probability (1d) • Addition Rules of Probability (2d) • Data Collection Lab (1d) • Mulitplication Rules of Probability (1d)Differentiate classical, empirical, and subjective probability; use sample spaces; compute empirical probabilities from tables/data; apply addition rules with mutual exclusivity and multiplication rules with independent/dependent events; compare theoretical and experimental outcomes in a dice lab.
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Weeks 15–16Unit 4: Counting and Probability / Unit 5: Discrete Probability DistributionConditional Probability (2d) • Fundamental Counting Rule (1d) • Permutations and Combinations (2d) • Probabililty and Counting Rules* (1d) • Chapter 4 Review and Test (2d) • Probability Distirbutions (2d of 3d)Calculate conditional probability; use the fundamental counting principle and factorials; distinguish and calculate permutations and combinations, including advanced counting situations; demonstrate Unit 4 mastery. Begin identifying and representing discrete probability distributions.
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Weeks 17–18Unit 5: Discrete Probability DistributionProbability Distirbutions (1d of 3d) • Mean of a Probabilty Distribution (1d) • SDev of a Probability Distribution (2d) • Data Collection Lab (1d) • Expected Value (1d) • The Binomial Distribution (3d) • Zener Test Lab (1d)Complete discrete-probability-distribution concepts; calculate distribution mean, variance, and standard deviation; use empirical data to build/graph distributions and compare with theory; calculate expected value; identify and solve binomial experiments; interpret results from the Zener test lab.
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Weeks 19–20Unit 5: Discrete Probability Distribution / Unit 6: The Normal DistributionOther Types of Discrete Probability Distributions* (1d) • Chapter 5 Review and Test (2d) • Introdcution to Standard Normal Distributions (1d) • Area Under The Standard Normal Curve (2d) • Using the SNC to find Probability (2d) • Using the Inverse Normal (2d)Identify and calculate Poisson, geometric, multinomial, and hypergeometric distributions; demonstrate Unit 5 mastery; describe continuous normal distributions; use standard-normal tables/technology to find areas/probabilities and use inverse normal methods to recover z-scores.
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Weeks 21–22Unit 6: The Normal DistributionApplications of the Normal cdf (2d) • Application of the Inverse Normal (1d) • Determining Normalcy* (1d) • Central Limit Theorem and Applications (3d) • The Normal Approximation of The Binomial Distribution* (2d) • Chapter 6 Review and Test (1d of 2d)Apply normal CDF and inverse normal methods to real continuous variables; assess normality using multiple tests; apply the Central Limit Theorem to sampling distributions; approximate binomial probabilities with a normal model; begin Unit 6 review/test.
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Weeks 23–24Unit 6: The Normal Distribution / Unit 7: Confidence IntervalsChapter 6 Review and Test (1d of 2d) • Introduction to Confidence Intervals (2d) • CI's when population Sdev is known (4d) • CI's when population Sdev is unknown (3d)Complete the Unit 6 assessment; define point/interval estimates and confidence levels; construct confidence intervals when population standard deviation is known, including nonstandard critical z-values, and when it is unknown using the t-distribution.
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Weeks 25–26Unit 7: Confidence IntervalsCI's for Sample Proportions (4d) • senior Trip (5d) • Mixed CI Review (1d of 2d)Construct confidence intervals and margins of error for proportions; account for the scheduled senior trip period; begin mixed confidence-interval review, selecting the appropriate formula and recognizing minimum-sample-size situations.
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Weeks 27–28Unit 7: Confidence Intervals / Unit 8: Hypothesis TestingMixed CI Review (1d of 2d) • Data Collection Lab (Tangram) (2d) • CI's for Variance and Sdev (Chi-Squared)* (2d) • Chapter 7 Review and Test (2d) • Introduction to Hypothesis Testing (1d) • Steps of Hypothesis Testing (1d) • z-test for a Mean (1d of 2d)Complete mixed CI review; collect Tangram data and build confidence intervals to make inferences; construct chi-square confidence intervals for variance/standard deviation; demonstrate Unit 7 mastery; identify null/alternative hypotheses and testing steps; begin a z-test for a mean.
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Weeks 29–30Unit 8: Hypothesis Testingz-test for a Mean (1d of 2d) • p-Value Test for a mean (2d) • t-test for a mean (2d) • Mid Unit Review and Test (1d) • Hypotheis Candy Lab (3d) • z-test for a Proportion (1d of 2d)Complete z-tests for means; perform p-value and t-tests for means and interpret conclusions; demonstrate mid-unit understanding; design and carry out a claim-testing candy lab; begin z-testing for proportions.
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Weeks 31–32Unit 8: Hypothesis Testingz-test for a Proportion (1d of 2d) • P-Value Test for a Proportion (2d) • Chi-Squared Test for a Var or Sdev* (2d) • Alternative Hypothesis Testing Techniques (3d) • Confidence Interval Hypotheis Testing (2d)Complete z-tests and p-value tests for proportions; use chi-square tests for variance/standard deviation; recognize alternative hypothesis-testing approaches; use confidence intervals as a two-tailed hypothesis-testing method.
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Weeks 33–34Unit 8: Hypothesis Testing / Unit 9: Correlation and Two-Variable TestingType II Error Testing* (1d) • Power of Test (1d) • Chapter 8 Review and Test (1d) • Z-Test between Two Means (1d) • t-Test between Two Means (1d) • Testing Between Two Proportions (2d) • Testing Between two SDEvs (1d) • Return to Scatter Plots and Correlation (1d) • Best Fit Lines (1d)Interpret Type II error and statistical power; demonstrate Unit 8 mastery; compare two means with z- and t-tests, two proportions, and two standard deviations; revisit scatter plots/correlation and create best-fit lines.
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Weeks 35–36Unit 9: Correlation and Two-Variable TestingDeterminition Coeefcients (1d) • Analyzing Bet Fit, r, and r^2 (1d) • Residual Plots (1d) • Standard Error and Prediction Interval (2d) • Chapter 9 Test or Lab (1d) • Final Review (3d) • Final (1d)Calculate and interpret correlation and determination coefficients (r and r²); analyze fit and residual plots; use standard error and prediction intervals; complete the Unit 9 test/lab, final review, and final assessment.
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